| 1. |
- Chen, Xin, et al.
(författare)
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Reinforcement learning for distributed hybrid flowshop scheduling problem with variable task splitting towards mass personalized manufacturing
- 2024
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 76, s. 188-206
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Tidskriftsartikel (refereegranskat)abstract
- Mass personalization manufacturing (MPM), an emerging production pattern, aims to improve enterprise profit in modern industries. However, the processing of heterogeneous orders from the consumers complicates such production scheduling problem. In addition, different scale tasks should adopt different splitting strategies in practical manufacturing, which makes the task splitting method more worthy of investigation. Towards MPM, this paper presents a distributed hybrid flowshop scheduling problem with variable task splitting (DHFSP-VTS) to minimize the makespan and total energy consumption simultaneously. Meanwhile, the VTS allows the tasks to be split into different sublots so they can save setup and transfer time. To solve these problems, we present an order modularization processing method that can categorize multiple types of orders into specific generation tasks, and a highly effective reinforcement learning-multiple objective evolutionary algorithm based on decomposition (RLMOEA/D) is designed. In RL-MOEA/D, there are three features: (1) three initial rules are used for initialization based on the current splitting scheme that can increase the diversity of solutions; (2) the reinforcement learning agent uses the Q-learning mechanism to dynamically select the scheme of task splitting as action; (3) a neighborhood search strategy improves the exploitation ability and expand the solution space. To verify the effectiveness of RL-MOEA/D, the MOEA/Ds based on four splitting schemes and four RL combined meta-heuristics are compared on 18 instances. The results show that RL-MOEA/D can obtain the best optimization and stability of all the other comparison algorithms. Therefore, it's a new technique to solve DHFSP with large-scale tasks, especially for the problem of MPM.
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| 2. |
- Liu, Sichao, et al.
(författare)
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Energy-efficient trajectory planning for an industrial robot using a multi-objective optimisation approach
- 2018
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Ingår i: Procedia Manufacturing. - : Elsevier BV. - 2351-9789. ; , s. 517-525
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Konferensbidrag (refereegranskat)abstract
- This paper presents an approach for energy-efficient trajectory planning of an industrial robot. A model that can be used to formulate the energy consumption of the robot with the kinematics constraints is developed. Given the trajectory in the Cartesian space, the septuple B-spline is applied in joint space trajectory planning to make the velocities, accelerations, and jerks bounded and continuous, with constraints on the initial and ending values. Then, energy-efficient optimisation problem with nonlinear constraints is discussed. Simulation results show that, the proposed approach is effective solution to trajectory planning, with ensuring a good energy improvement and fluent movement for the robot manipulators.
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| 3. |
- Liu, Yongkui, et al.
(författare)
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Multi-agent-based scheduling in cloud manufacturing with dynamic task arrivals
- 2018
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Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271. ; , s. 953-960
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Konferensbidrag (refereegranskat)abstract
- Scheduling is a critical means for providing on-demand manufacturing services in cloud manufacturing. Multi-agent technologies provide an effective approach for addressing scheduling issues in cloud manufacturing, which, however, have rarely been used for solving the issue. This paper addresses scheduling issues in cloud manufacturing using multi-agent technologies. A multi-agent architecture for scheduling in cloud manufacturing is proposed firstly. Then, a corresponding multi-agent model is presented, which incorporates many-to-many negotiations based on an extended contract net protocol and takes into account dynamic task arrivals. Simulation results indicate the feasibility of the model and approach proposed.
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| 4. |
- Wang, Peng, et al.
(författare)
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Linking Emergence to the Complex Product System
- 2020
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 8, s. 34286-34298
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Tidskriftsartikel (refereegranskat)abstract
- The increasing complexity of product calls for manufacturing integration, while in turn high integration brings the problems of system level complexity. This paper proposes that complex product system (CoPS) should be managed as a dynamical system. The dynamical characteristics of CoPS are discussed from the perspective of emergence. A conceptual model is established to analyze the cause, process and result of the CoPS emergence. The mechanism of inner state emergence in CoPS is interpreted by formal languages to provide a point view of state space. It is concluded that the behavior of CoPS, especially the complexity, exhibits the 'entity is greater than the sum of the parts' phenomena when satisfying given necessary conditions. A novel methodology is then established to evaluate this emergence-based complexity. The feasibility and application of the novel complexity measurement is verified by an example of turbine housing production process. Further discussions are made on how to manage the potential emerging complexity based on the proposed measurement.
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| 5. |
- Weinstein, John N., et al.
(författare)
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The cancer genome atlas pan-cancer analysis project
- 2013
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 45:10, s. 1113-1120
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Tidskriftsartikel (refereegranskat)abstract
- The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile. © 2013 Nature America, Inc. All rights reserved.
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| 6. |
- Gao, Y., et al.
(författare)
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A Review on Recent Advances in Vision-based Defect Recognition towards Industrial Intelligence
- 2021
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642.
-
Tidskriftsartikel (refereegranskat)abstract
- In modern manufacturing, vision-based defect recognition is an essential technology to guarantee product quality, and it plays an important role in industrial intelligence. With the developments of industrial big data, defect images can be captured by ubiquitous sensors. And, how to realize accuracy recognition has become a research hotspot. In the past several years, many vision-based defect recognition methods have been proposed, and some newly-emerged techniques, such as deep learning, have become increasingly popular and have addressed many challenging problems effectively. Hence, a comprehensive review is urgently needed, and it can promote the development and bring some insights in this area. This paper surveys the recent advances in vision-based defect recognition and presents a systematical review from a feature perspective. This review divides the recent methods into designed-feature based methods and learned-feature based methods, and summarizes the advantages, disadvantages and application scenarios. Furthermore, this paper also summarizes the performance metrics for vision-based defect recognition methods. And some challenges and development trends are also discussed.
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| 7. |
- Li, Shufei, et al.
(författare)
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Proactive human-robot collaboration : Mutual-cognitive, predictable, and self-organising perspectives
- 2023
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 81, s. 102510-
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Forskningsöversikt (refereegranskat)abstract
- Human-Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human -centricity, sustainability, and resilience. However, existing HRC development mainly undertakes either a human-dominant or robot-dominant manner, where human and robotic agents reactively perform operations by following pre-defined instructions, thus far from an efficient integration of robotic automation and human cognition. The stiff human-robot relations fail to be qualified for complex manufacturing tasks and cannot ease the physical and psychological load of human operators. In response to these realistic needs, this paper presents our arguments on the obvious trend, concept, systematic architecture, and enabling technologies of Proactive HRC, serving as a prospective vision and research topic for future work in the human-centric smart manufacturing era. Human-robot symbiotic relation is evolving with a 5C intelligence - from Connection, Coordination, Cyber, Cognition to Coevolution, and finally embracing mutual-cognitive, predictable, and self -organising intelligent capabilities, i.e., the Proactive HRC. With proactive robot control, multiple human and robotic agents collaboratively operate manufacturing tasks, considering each others' operation needs, desired resources, and qualified complementary capabilities. This paper also highlights current challenges and future research directions, which deserve more research efforts for real-world applications of Proactive HRC. It is hoped that this work can attract more open discussions and provide useful insights to both academic and industrial practitioners in their exploration of human-robot flexible production.
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| 8. |
- Pang, Shibao, et al.
(författare)
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Dual-Dimensional Manufacturing Service Collaboration Optimization Toward Industrial Internet Platforms
- 2023
-
Ingår i: ENGINEERING. - : Elsevier BV. - 2095-8099. ; 22, s. 34-48
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Tidskriftsartikel (refereegranskat)abstract
- An Industrial Internet platform is acknowledged to be a requisite promoter for smart manufacturing, enabling physical manufacturing resources to be virtualized and permitting resources to collaborate in the form of services. As a central function of the platform, manufacturing service collaboration optimization is dedicated to establishing high-quality service collaboration solutions for manufacturing tasks. Such optimization is inseparable from the functional and amount requirements of a task, which must be satisfied when orchestrating services. However, existing manufacturing service collaboration optimization methods mainly focus on horizontal collaboration among services for functional demands and rarely consider vertical collaboration to cover the needed amounts. To address this gap, this paper proposes a dual-dimensional service collaboration methodology that combines functional and amount collaboration. First, a multi-granularity manufacturing service modeling method is presented to describe services. On this basis, a dual-dimensional manufacturing service collaboration optimization (DMSCO) model is formulated. In the vertical dimension, multiple functionally equivalent services form a service cluster to fulfill a subtask; in the horizontal dimension, complementary service clusters collaborate for the entire task. Service selection and amount distribution to the selected services are critical issues in the model. To solve the problem, a multi-objective memetic algorithm with multiple local search operators is tailored. The algorithm embeds a competition mechanism to dynamically adjust the selection probabilities of the local search operators. The experimental results demonstrate the superiority of the algorithm in terms of convergence, solution quality, and comprehensive metrics, in comparison with commonly used algorithms.
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| 9. |
- Wang, Baicun, et al.
(författare)
-
Human-centric smart manufacturing
- 2023
-
Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 69, s. 18-19
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 10. |
- Wang, Tianyu, et al.
(författare)
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Data-efficient multimodal human action recognition for proactive human–robot collaborative assembly: A cross-domain few-shot learning approach
- 2024
-
Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 89
-
Tidskriftsartikel (refereegranskat)abstract
- With the recent vision of Industry 5.0, the cognitive capability of robots plays a crucial role in advancing proactive human–robot collaborative assembly. As a basis of the mutual empathy, the understanding of a human operator's intention has been primarily studied through the technique of human action recognition. Existing deep learning-based methods demonstrate remarkable efficacy in handling information-rich data such as physiological measurements and videos, where the latter category represents a more natural perception input. However, deploying these methods in new unseen assembly scenarios requires first collecting abundant case-specific data. This leads to significant manual effort and poor flexibility. To deal with the issue, this paper proposes a novel cross-domain few-shot learning method for data-efficient multimodal human action recognition. A hierarchical data fusion mechanism is designed to jointly leverage the skeletons, RGB images and depth maps with complementary information. Then a temporal CrossTransformer is developed to enable the action recognition with very limited amount of data. Lightweight domain adapters are integrated to further improve the generalization with fast finetuning. Extensive experiments on a real car engine assembly case show the superior performance of proposed method over state-of-the-art regarding both accuracy and finetuning efficiency. Real-time demonstrations and ablation study further indicate the potential of early recognition, which is beneficial for the robot procedures generation in practical applications. In summary, this paper contributes to the rarely explored realm of data-efficient human action recognition for proactive human–robot collaboration.
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| 11. |
- Wang, Xi Vincent, Dr. 1985-, et al.
(författare)
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Robots in the industrial internet : A cloud-based approach based on gateways
- 2019
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Ingår i: Proceedings of ASME 2019 14th International Manufacturing Science and Engineering Conference, MSEC 2019.
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Konferensbidrag (refereegranskat)abstract
- In the context of Industrial Internet, connectivity is a must during system construction as it provides the possibility of sharing the hardware data with the network, and accessing the hardware from other systems or devices. An efficient communication approach is the highest priority to deploy a successful technology like Industry 4.0, Industrial Internet, Internet of Things, etc.. Thus in this research, a system integration method is presented using industrial robots as the test scenario. Cloud and gateway technologies are utilised to achieve high-performance connectivity, integration and security. Multiple deployment models are developed for public, private and hybrid cloud scenarios. During implementation, the Universal Robot 5 is utilised as the test robot integrating to the KTH cloud system in Sweden. The results are quantifiably evaluated and discussed. The proposed approach also contributes to the Cloud Robotics research by proposing novel system structures and integration methods.
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| 12. |
- Zhou, Huiying, et al.
(författare)
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An attention-based deep learning approach for inertial motion recognition and estimation in human-robot collaboration
- 2023
-
Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 67, s. 97-110
-
Tidskriftsartikel (refereegranskat)abstract
- In line with a human-centric smart manufacturing vision, human-robot collaboration is striving to combine robots' high efficiency and quality with humans' rapid adaptability and high flexibility. In particular, perception, recognition and estimation of human motion determine when and what robot to collaborate with humans. This work presents an attention-based deep learning approach for inertial motion recognition and estimation in order to infer when robotic assistance will be requested by the human and to allow the robot to perform partial human tasks. First, in the stage of motion perception and recognition, quaternion-based calibration and forward kinematic analysis methods enable the reconstruction of human motion based on data streaming from an inertial motion capture device. Then, in the stage of motion estimation, residual module and Bidirectional Long ShortTerm Memory module are integrated with proposed attention mechanism for estimating arm motion trajectories further. Experimental results show the effectiveness of the proposed approach in achieving better recognition and estimation in comparison with traditional approaches and existing deep learning approaches. It is experimentally verified in a laboratory environment involving a collaborative robot employed in a small part assembly task.
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| 13. |
-
Advanced human-robot collaboration in manufacturing
- 2021
-
Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- This book presents state-of-the-art research, challenges and solutions in the area of human-robot collaboration (HRC) in manufacturing. It enables readers to better understand the dynamic behaviour of manufacturing processes, and gives more insight into on-demand adaptive control techniques for industrial robots. With increasing complexity and dynamism in today's manufacturing practice, more precise, robust and practical approaches are needed to support real-time shop-floor operations. This book presents a collection of recent developments and innovations in this area, relying on a wide range of research efforts. The book is divided into five parts. The first part presents a broad-based review of the key areas of HRC, establishing a common ground of understanding in key aspects. Subsequent chapters focus on selected areas of HRC subject to intense recent interest. The second part discusses human safety within HRC. The third, fourth and fifth parts provide in-depth views of relevant methodologies and algorithms. Discussing dynamic planning and monitoring, adaptive control and multi-modal decision making, the latter parts facilitate a better understanding of HRC in real situations. The balance between scope and depth, and theory and applications, means this book appeals to a wide readership, including academic researchers, graduate students, practicing engineers, and those within a variety of roles in manufacturing sectors.
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| 14. |
- Chen, Mo, et al.
(författare)
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Study on Efficient Fused Deposition Modelling of Thermoplastic Polyurethane Inflatable Wall Features for Airtightness
- 2020
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Ingår i: Swedish Production Symposium 2020.
-
Konferensbidrag (refereegranskat)abstract
- The thermoplastic polyurethane (TPU) material is an elastomer that canbe used for inflatable products. Fused deposition modelling (FDM) is a widelyused additive manufacturing process for TPU material due to the capability ofgenerating complex structures with low cost. However, TPU is soft and thusdifficult to be extruded as continuously and uniformly as hard materials such aspolylactide by FDM. Inappropriate extruder structure and speed settings can leadto filament buckling problem, resulting in poor material filling quality, longprinting time and low printing success rate. This paper aims at improving the FDMprinting efficiency of TPU inflatable products by adding lateral support to thefilament and finding out the appropriate speed ranges for different wall featuresand thicknesses. Firstly, a filament guide sheet is designed as being inserted intothe gap between the drive gears and the bottom frame of the gear chamber in orderto prevent the soft TPU filament from buckling. Secondly, inflatable product wallfeatures are classified into floors, roofs and sidewalls and experiment for findingthe relationship between printing speed and airtightness is carried out. In order toverify the proposed solution, wall features are printed and the material fillingsobtained under different printing speeds are compared by measuring theairtightness of the wall features. Results show that the proposed filament guidesheet mitigates filament buckling, and the speed range that meets the airtightnessrequirement can be found for various wall features and thicknesses. In summary,the sealing of the filament feeding channel between the drive gears and the nozzle,as well as the speed optimisation according to product features, are essential forthe efficient printing of TPU inflatable products.
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| 15. |
- Chodnicki, Marek, et al.
(författare)
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Project-Based Collaborative Research and Training Roadmap for Manufacturing Based on Industry 4.0
- 2024
-
Ingår i: Flexible Automation and Intelligent Manufacturing. - : Springer Nature. ; , s. 708-715
-
Konferensbidrag (refereegranskat)abstract
- The importance of the economy being up to date with the latest developments, such as Industry 4.0, is more evident than ever before. Successful implementation of Industry 4.0 principles requires close cooperation of industry and state authorities with universities. A paradigm of such cooperation is described in this paper stemming from university partners with partly overlapping and partly complementary areas of expertise in manufacturing. Specific areas that are targeted include Additive Manufacturing, cloud computing and control, Virtual Reality, Digital Twins, and Artificial Intelligence. The manufacturing system domains that are served pertaining to process planning and optimization, process and system monitoring, and innovative / precision manufacturing. The described collaborative research and training framework involves a combination of pertinent targeted individual exploratory innovation projects as well as a synthetic multifaceted common research project. Based on these, the research and innovation project knowledge will be transferred to the industry by building a Cluster of Excellence, i.e., a network consisting of academic and industrial stakeholders.
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| 16. |
- Cui, Y., et al.
(författare)
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Research on milling temperature measuring tool embedded with NiCr/NiSi thin film thermocouple
- 2018
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Ingår i: 51st CIRP Conference on Manufacturing Systems. - : Elsevier. ; , s. 1457-1462
-
Konferensbidrag (refereegranskat)abstract
- In order to measure the milling area temperature in-situ, the milling tool embedded with NiCr/NiSi thin film thermocouple (TFTC) is prepared. TFTC capable well temperature performance is embedded on the tool tip by successively depositing SiO2 insulating film, NiCr/NiSi thermoelectric film, and SiO2 protective film. Surface morphology and thin film properties are confirmed to achieve expectation by means of TEM and SEM. Imitation reflects that TFTC abrasion has minor effect on dynamic and static characteristic. The in-situ milling area temperature is successfully detected by TFTC temperature measuring tool in field test.
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| 17. |
- Fuoco, Tiziana, PhD, 1986-, et al.
(författare)
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Hydrogel Polyester Scaffolds via Direct-Ink-Writing of Ad Hoc Designed Photocurable Macromonomer
- 2022
-
Ingår i: Polymers. - : MDPI. - 2073-4360. ; 14:4
-
Tidskriftsartikel (refereegranskat)abstract
- Synthetic, degradable macromonomers have been developed to serve as ink for 3D printing technologies based on direct-ink-writing. The macromonomers are purposely designed to be cross-linkable under the radical mechanism, to impart hydrophilicity to the final material, and to have rheological properties matching the printer's requirements. The suitable viscosity enables the ink to be printed at room temperature, in absence of organic solvents, and to be cross-linked to manufacture soft 3D scaffolds that show no indirect cytotoxicity and have a hydration capacity of up to 100% their mass and a compressive modulus in the range of 0.4-2 MPa.
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| 18. |
- Givehchi, Mohammad, et al.
(författare)
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Function block-enabled operation planning and machine control in Cloud-DPP
- 2022
-
Ingår i: International Journal of Production Research. - : Informa UK Limited. - 0020-7543 .- 1366-588X. ; , s. 1-17
-
Tidskriftsartikel (refereegranskat)abstract
- Today, due to shop-floor uncertainties and widespread cross-enterprise collaborations, manufacturing systems of enterprises are increasingly demanded to be agile, adaptive, flexible and interoperable. Process planning systems are mission-critical constituent components of manufacturing systems in machining job shops of small and medium-sized enterprises in the machining and metal cutting sector. Cloud-based adaptive distributed process planning, which includes global supervisory planning in the cloud and local operation planning based on function block and cloud technologies, provides an effective approach for enhancing agility, adaptability, flexibility and interoperability of manufacturing systems.
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| 19. |
- Guo, Zhengang, et al.
(författare)
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Exploring self-organization and self-adaption for smart manufacturing complex networks
- 2023
-
Ingår i: Frontiers of Engineering Management. - : Springer Nature. - 2095-7513 .- 2096-0255. ; 10:2, s. 206-222
-
Tidskriftsartikel (refereegranskat)abstract
- Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments, which poses great challenges to manufacturing enterprises. Fortunately, recent advances in the Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber—physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Moreover, the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology, which can be added to or removed from the networks in a plug-and-play manner. Materials, information, and financial assets are passed through interactive links across the networks. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. Consequently, an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions. The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time. This work potentially enables managers and practitioners to implement active perception, active response, self-organization, and self-adaption solutions in discrete manufacturing enterprises.
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| 20. |
- Jeong, Yongkuk, et al.
(författare)
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Digital Twin-Based Services and Data Visualization of Material Handling Equipment in Smart Production Logistics Environment
- 2022
-
Ingår i: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems. - Cham : Springer Nature. ; , s. 556-564
-
Konferensbidrag (refereegranskat)abstract
- Smart production logistics has introduced in manufacturing industries with emerging technologies such as digital twin, industrial internet of things, and cyber-physical system. This technological innovation initiates the new way of working, working environment, and decision-making process. Especially the decision-making process has changed from experience and intuition to knowledge and data driven. In this paper, digital twin-based services, and data visualization of material handling equipment in smart production logistics environment are presented. There are several applications of digital twin in manufacturing industries already, however feedback from the virtual environment to physical environment and interactions between them which are the essential features of digital twin are very weak in many applications. Therefore, we have developed digital twin-based services in the laboratory scale including feedback and interaction. In addition, data visualization application of material handling equipment in automotive industry is presented to provide insights to the users. Both applications have developed based on the same framework including database and middleware, so it has possibilities to develop further in the future.
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| 21. |
- Ji, Qinglei, et al.
(författare)
-
A Flexible 4D Printing Service Platform for Smart Manufacturing
- 2020
-
Ingår i: Swedish Production Symposium.
-
Konferensbidrag (refereegranskat)abstract
- With the extensive application of 3D printing (3DP) in smartmanufacturing, 4D printing (4DP), which enhances 3D printed objects with shapemorphing ability by using smart materials, has shown significant industrial potentialand attracted tremendous attention. One key concern of 4DP is how to effectivelyand quickly meet different production and application requirements considering thecomplexity of materials and diversity of stimulus methods. In order to provide ageneral research platform for 4DP researchers, a flexible 4DP service platform isproposed. Components and modules for building 4DP and test systems are modeledand virtualized to form the different resources. These resources are then integratedvirtually or physically to provide some basic functions such as a 3D displacementstage or a visual monitoring system. According to different 4DP requirements, thesefunctions are then encapsulated into services to serve different research. Theplatform enables a variety of 4DP applications in smart manufacturing environmentssuch as 4D printed magnetic medical robots, test platform for studying the 4DPresponse, etc. A case study on designing a ferromagnetic 4DP platform based on theservice platform is performed to prove the feasibility of the method.
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| 22. |
- Ji, Qinglei, et al.
(författare)
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Customized protective visors enabled by closed loop controlled 4D printing
- 2022
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- The COVID-19 pandemic makes protective visors important for protecting people in close contacts. However, the production of visors cannot be increased greatly in a short time, especially at the beginning of the pandemic. The 3D printing community contributed largely in fabricating the visor frames using the rapid and adaptive manufacturing ability. While there are many open source designs of face visors for affordable 3D printers, all these designs fabricate mono-sized frames without considering diverse users’ dimensions. Here, a new method of visor post-processing technology enabled by closed loop controlled 4D printing is proposed. The new process can further deform the printed visor to any customized size for a more comfortable user experience. FEM analysis of the customized visor also shows consistent wearing experience in different circumstances compared with the old visor design. The fabrication precision and time cost of the method is studied experimentally. A case study regarding the reducing, reusing and recycling (3R) of customized visors in classrooms is proposed to enable the customized visors manufactured in a more sustainable way.
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| 23. |
- Ji, Qinglei, et al.
(författare)
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Design and calibration of 3D printed soft deformation sensors for soft actuator control
- 2023
-
Ingår i: Mechatronics (Oxford). - : Elsevier BV. - 0957-4158 .- 1873-4006. ; 92, s. 102980-102980
-
Tidskriftsartikel (refereegranskat)abstract
- Soft actuators made from compliant materials are superior to conventional rigid robots in terms of flexibility, adaptability and safety. However, an inherent drawback of soft actuator is the low actuation precision. Implementing closed loop control is a possible solution, but the soft actuator shape can hardly be measured directly by commercially available sensors, which either are too stiff for integration or cause performance degradation of the actuator. Although 3D printing has been applied to print bendable sensors from conductive materials, they either have larger stiffness than the soft actuator or are made from specially designed materials that are difficult to reproduce. In this study, easily accessible commercial soft conductive material is applied to directly 3D print soft sensors on soft actuators. Different configurations of the printed sensors are studied to investigate how the sensor design affects the performance. The best sensor configuration is selected to provide shape feedback using its changing resistance during deformation. Compared with a commercial flexible bending sensor, the printed sensor has less influences on the soft actuator performance and enjoys higher shape estimation accuracy. Closed loop shape control of the actuator using feedback from the 3D printed sensor is then designed, implemented and compared with the control results using image feedback. A gripper consisting of three individually controlled soft actuators demonstrates the applications of the soft sensor.
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| 24. |
- Ji, Qinglei, et al.
(författare)
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Design and closed loop control of a 3D printed soft actuator
- 2020
-
Ingår i: 2020 16th IEEE International Conference on Automation Science and Engineering (CASE). - : IEEE. ; , s. 842-848
-
Konferensbidrag (refereegranskat)abstract
- Soft robots, made of soft materials such as di-electric elastomer or shape memory polymers, have receivedtremendous attentions due to its dexterousness, flexibility andsafety compared with rigid robots. However, wider applicationof soft robots is limited due to their complex fabrication processand poor controllability. Here, we introduce a closed loopcontrolled soft actuator that is fully 3D printed with flexiblematerial. The structure of the soft actuator is optimized withFinite Element Method (FEM) to acquire shortest fabricationtime and highest deformation for same stimulus input. A desk-top Fused Deposition Modeling (FDM) 3D printer is used forlow-cost fabrication of such actuators. A webcamera is used forthe image feedback which offers the real time shape monitoringof the soft actuator. An output feedback Proportional IntegralDerivative (PID) controller with lowpass filter is developed withpole placement design method based on a data-driven modelof the 3D printed soft actuator. The controller is implementedto regulate the input air pressure to ensure a fast-response, precise and robust shape changing for any work environments.
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| 25. |
- Ji, Qinglei, et al.
(författare)
-
Development of a 3D Printed Multi-Axial Force Sensor
- 2022
-
Ingår i: Advances in Transdisciplinary Engineering. - : IOS Press.
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Sensors play a vital role in the industry transformation. Commercialsensors such as force sensors have limited options in shapes, stiffness, measuringranges, etc. Customized force sensors optimized for the production environmentcan greatly increase the integration workflow and avoid the trade-off in design freedomof using commercial sensors. 3D printing, as a rapid prototyping technology,offers great potential in fabricating force sensors customized to a specific application.However, most of the existing 3D printed force sensors are limited to onedirectionalsensing, while most of them use materials developed in-house. In thisstudy, a fully 3D printed force sensor using commercial conductive 3D printing materialsis presented. By utilizing the resistance change when under load, the sensorcan estimate the applied force in multiple directions. The resistive performance ofthe prototype 3D printed force sensor is first characterized and then validated in acase study.
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| 26. |
- Ji, Qinglei, et al.
(författare)
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Feedback control for the precise shape morphing of 4D printed shape memory polymer
- 2021
-
Ingår i: IEEE Transactions on Industrial Electronics. - : IEEE. - 0278-0046 .- 1557-9948. ; , s. 12698-12707
-
Tidskriftsartikel (refereegranskat)abstract
- Four-dimensional printing (4DP) is a newly emerged technology that uses smart materials for additive manufacturing and thus enables shape and/or property change upon stimulus after the printing process. Present study on 4DP has been focused on open loop stimulus, which can hardly ensure high shape precision and predictable final states. In this paper, a new closed loop 4DP (CL4DP) process supplementing 4D printed actuation with closed loop control methods is proposed. Image feedback is used for enhancing the conventional open loop 4DP morphing process and a controller is implemented to regulate the intensity of the stimulus accordingly in real-time. To achieve precise control, a nonlinear affine system model is built by model identification with measurement data to describe the dynamic shape recovery process of the 4D printed Shape Memory Polymer (SMP). Precise shape control is achieved and the effects of controller parameters on the precision of CL4DP are studied. Traditionally, SMP has a discrete number of selected steady states. With CL4DP, such steady states can be continuous and arbitrary.
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| 27. |
- Ji, Qinglei, 1993-
(författare)
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Learning-based Control for 4D Printing and Soft Robotics
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Exploiting novel sensors and actuators made of flexible and smart materials becomes a new trend in robotics research. The studies on the design, production, and control of the new type of robots motivate the research fields of soft robots and 4D printed robots. 3D Printing (3DP) is an additive manufacturing technology that is widely used in printing flexible materials to fabricate soft robots. 4D Printing (4DP) combines 3DP technologies with smart materials to produce transformable devices. 4DP first prints structures with specifically designed responsive materials. When external stimuli such as temperature, voltage, or magnetic field are applied to the printed structure, it changes shape in a programmable way. The shape morphing property of 4DP makes it a novel approach to the actuators of robots.The employment of these special materials empowers these new robots with better compliance and adaptability to the working environment. However, compared with the rigid counterparts, they also have complex dynamic properties such as substantial non-linearity and time-variance. These factors make the precise modeling and robust control of these new robots challenging and thus hinder their potential applications. Focusing on soft robotic systems enabled by 3DP and 4DP approaches, this dissertation studies both traditional and Machine Learning (ML)-based approaches to the modeling, perception, and control of soft, non-linear, and time-variant robotic systems. The main contributions of this dissertation are:The scheme of Closed-Loop (CL) controlled 4DP (CL4DP) using temperature stimulated Shape Memory Polymer (SMP) is designed and validated numerically and experimentally. The feedback control system increases the precision and robustness of the shape morphing process of 4D printed SMP. Applications of CL4DP are explored.Data-driven model identification methods are applied to learn the dynamic model of the shape morphing process of CL4DP and the learned model has good quality to support model-based control design. Model-free and adaptive Reinforcement Learning (RL) controllers are developed to deal with the non-linearity and time variance of 4D printed actuators. To improve the stability and quick adaptability, a concise basis function set is selected instead of blindly using Deep Neural Networks (DNNs).A quadruped robot enabled by soft actuators and its simulation model are developed. The computation efficiency and model accuracy of the simulator are studied and optimized by comparing different simulation methods such as Finite Element Method (FEM) and lumped parameter method.The optimal walking gait pattern of a soft-legged quadruped robot is found by grid parameter search and RL with a physics based simulation model. To speed up the RL training process, modeling tricks are used to reduce the simulation time of the model and curriculum learning is used to reduce the learning time.A soft sensor made by printable conductive materials and 3DP is designed and optimally calibrated to estimate the shape of a pneumatically driven soft actuator. The geometry of the soft sensor is optimally designed for the best linearity, hysteresis and drift properties. The online estimation is based on a linear regression model learned from experimental data.A pneumatically driven soft gripper is developed by 3DP, the printable soft sensor, and pole-placement control methods. The operation of the gripper does not require an external image feedback system to measure its shape, which is estimated by the integrated soft sensor. The position feedback by the soft sensor and the controller by the pole-placement method enable the soft gripper to perform complex tasks with high precision.
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| 28. |
- Ji, Qinglei, et al.
(författare)
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Omnidirectional walking of a quadruped robot enabled by compressible tendon-driven soft actuators
- 2022
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Ingår i: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (<em>IROS 2022</em>), Kyoto, October 23–27, 2022. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 11015-11022
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Konferensbidrag (refereegranskat)abstract
- Using soft actuators as legs, soft quadruped robots have shown great potential in traversing unstructured and complex terrains and environments. However, unlike rigid robots whose gaits can be generated using foot pattern design and kinematic model of the rigid legs, the gait generation of soft quadruped robots remains challenging due to the high DoFs of the soft actuators and the uncertain deformations during their contact with the ground. This study is based on a quadruped robot using four Compressible Tendon-driven Soft Actuators (CTSAs) as the legs, with the actuator's compression motion being utilized to improve the walking performance of the robot. For the gait design, an inverse kinematics model considering the compression of the CTSA is developed and validated in simulation. Based on this model, walking gaits realizing different motion speeds and directions are generated. Closed loop direction and speed controllers are developed for increasing the robustness and precision of the robot walking. Simulation and experimental results show that omnidirectional locomotion and complex walking tasks can be realized by tuning the gait parameters and the motions are resistant to external disturbances.
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| 29. |
- Ji, Qinglei, et al.
(författare)
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Online reinforcement learning for the shape morphing adaptive control of 4D printed shape memory polymer
- 2022
-
Ingår i: Control Engineering Practice. - : Elsevier. - 0967-0661 .- 1873-6939. ; 126, s. 105257-105257
-
Tidskriftsartikel (refereegranskat)abstract
- Combining 3D printing and smart materials, 4D printing technologies enable the printed actuators to furtherchange their shapes or other properties after prototyping. However, the shape morphing of 4D printed actuatorssuffers from poor controllability and low precision. One of the main challenges is that the 4D printed actuatorsare hard to be modeled and it is difficult to develop an appropriate controller for them. In this study, variouspopular reinforcement learning (RL) methods are applied to address the problem of online and adaptive model-free control of 4D printed shape memory polymer (SMP). Their training efficiencies are compared and anadaptive LQR controller based on Q learning is developed to realize efficient online learning. The RL controllerachieves precise and quick shape control within 2 − −3 learning episodes and is adaptive to the changingproperties of SMP. The RL controller performance is then compared with a model-based LQR controller andshows high control precision and excellent adaptability to the varying control plant.
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| 30. |
- Ji, Qinglei, et al.
(författare)
-
Optimal shape morphing control of 4D printed shape memory polymer based on reinforcement learning
- 2022
-
Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier. - 0736-5845 .- 1879-2537. ; 73
-
Tidskriftsartikel (refereegranskat)abstract
- 4D printing technology, as a new generation of Additive Manufacturing methods, enables printed objectsto further change their shapes or other properties upon external stimuli. One main category of 4D printingresearch is 4D printed thermal Shape Memory Polymer (SMP). Its morphing process has large time delay, isnonlinear time variant, and susceptible to unpredictable disturbances. Reaching an arbitrary position with highprecision is an active research question. This paper applies the Reinforcement Learning (RL) method to developan optimal control method to perform closed loop control of the SMP actuation. Precise and prompt shapemorphing is achieved compared with previous control methods using a PI controller. The training efforts of RLare further reduced by simplifying the optimal control policy using the structural property of the prior trainedresults. Customized protective visors against COVID-19 are fabricated using the proposed control method.
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| 31. |
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| 32. |
- Ji, Qinglei, et al.
(författare)
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Synthesizing the optimal gait of a quadruped robot with soft actuators using deep reinforcement learning
- 2022
-
Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier. - 0736-5845 .- 1879-2537. ; 78, s. 102382-102382
-
Tidskriftsartikel (refereegranskat)abstract
- Quadruped robots have the advantages of traversing complex terrains that are difficult for wheeled robots. Most of the reported quadruped robots are built by rigid parts. This paper proposes a new design of quadruped robots using soft actuators driven by tendons as the four legs. Compared to the rigid robots, the proposed soft quadruped robot has inherent safety, less weight and simpler mechanism for fabrication and control, but the corresponding challenge is that the accurate mathematical model applicable to model-based control design of the soft robot is difficult to derive by dynamics. To synthesize the optimal gait controller of the soft-legged robot, the paper makes the following contributions. First, the flexible components of the quadruped robot are modeled with different finite element and lumped parameter methods. The model accuracy and computation efficiency are analyzed. Second, soft actor–critic methods and curriculum learning are applied to learn the optimal gaits for different walking tasks. Third, The learned gaits are implemented in an in-house robot to transport hand tools. Preliminary results show that the robot can walk forward and correct the walking directions.
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| 33. |
- Jiang, Pei, et al.
(författare)
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Energy consumption prediction and optimization of industrial robots based on LSTM
- 2023
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 70, s. 137-148
-
Tidskriftsartikel (refereegranskat)abstract
- Due to wide distribution and low energy efficiency, the energy-saving of industrial robots draws more and more attention, and a large number of methods have emerged to predict or optimize the energy consumption (EC) of robots. However, many dynamic and electrical parameters are unavailable due to the commercial limitations of industrial robots, which constrains the application of those model-based methods. Therefore, this paper proposes a data-driven method for the prediction and optimization of robot EC. Initially, the cause-and-effect relationship between robot EC and joint motion variables, such as the joint position, velocity, and acceleration, is qualitatively analyzed based on the influence of the capacitive and inductive components in the drive system. And a deep neural network based on long short-term memory (LSTM) is proposed to reveal the nonlinear mapping between the industrial robot EC and the joint motion variables, which can predict EC without the parameters of the industrial robot. Based on the proposed neural network, the adaptive genetic algorithm is adopted to optimize the time-variant scaling function, which can optimize the scaled trajectory to reduce EC without hardware modification. To validate the accuracy and efficacy of the proposed method, experiments are conducted on a KUKA KR60-3 six degree-of-freedom (DOF) industrial robot. The results demonstrate that the proposed neural network can predict EC with a mean absolute percentage error less than 4.21% and the proposed method reduces the EC by 22.35%.
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| 34. |
- Kemény, Zsolt, et al.
(författare)
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Human-robot collaboration in manufacturing : A multi-agent view
- 2021
-
Ingår i: Advanced Human-Robot Collaboration in Manufacturing. - Cham : Springer Nature. ; , s. 3-41
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Recent years have witnessed a growing interest in reintroducing the advantages of the human workforce into industrial production processes while keeping the benefits of machines already proven in production automation. Both industry and academia exhibit intense interest in the topic of combining human and robotic resources in collaborative production environments. Nevertheless, the domain of human-robot collaboration is still undergoing intense evolution-nothing proves it more than the diversity of fundamental approaches, world-views, and gaps in standardisation that all hint at the fact that even the overall understanding of the domain is yet to be consolidated. It is, thus, not realistic to expect that a single comprehensive morphological work would reconcile today's multitude of views on human-robot collaboration, and this is not the goal of this introductory chapter either. Instead, the chapter gives an overview of the domain relying on a single selected paradigm, namely, multi-agent systems. This choice is based on the assumption that this branch of distributed artificial intelligence, having matured over several decades of research and application, provides feasible perspectives and terminological waypoints for collaborative settings under the structured circumstances of industrial production. The chapter aims to outline structural properties and mechanisms of collaborative systems from an agent-oriented point of view, and aims to provide a reference of terms and concepts which make many different views of the domain comparable. Further chapters of this book, as well as numerous application examples, known industrial solutions and standards, are positioned within this framework to connect theoretical waypoints and practical findings.
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| 35. |
- Kong, Depeng, et al.
(författare)
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Bioinspired Co-Design of Tactile Sensor and Deep Learning Algorithm for Human-Robot Interaction
- 2022
-
Ingår i: ADVANCED INTELLIGENT SYSTEMS. - : Wiley. - 2640-4567. ; 4:6
-
Tidskriftsartikel (refereegranskat)abstract
- Robots equipped with bionic skins for enhancing the robot perception capability are increasingly deployed in wide applications ranging from healthcare to industry. Artificial intelligence algorithms that can provide bionic skins with efficient signal processing functions further accelerate the development of this trend. Inspired by the somatosensory processing hierarchy of humans, the bioinspired co-design of a tactile sensor and a deep learning-based algorithm is proposed herein, simplifying the sensor structure while providing computation-enhanced tactile sensing performance. The soft piezoresistive sensor, based on the carbon black-coated polyurethane sponge, offers a continuous sensing area. By utilizing a customized deep neural network (DNN), it can detect external tactile stimulus spatially continuously. Besides, a novel data augmentation method is developed based on the sensor's hexagonal structure that has a sixfold rotation symmetry. It can significantly enhance the generalization ability of the DNN model by enriching the collected training data with generated pseudo-data. The functionality of the sensor and the robustness of the proposed data augmentation strategy are verified by precisely recognizing five touch modalities, illustrating a well-generalized performance, and providing a promising application prospect in human-robot interaction.
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| 36. |
- Li, Shufei, et al.
(författare)
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Self-organising multiple human-robot collaboration : A temporal subgraph reasoning-based method
- 2023
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 68, s. 304-312
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Tidskriftsartikel (refereegranskat)abstract
- Multiple Human-Robot Collaboration (HRC) requires self-organising task allocation to adapt to varying operation goals and workspace changes. However, nowadays an HRC system relies on predefined task arrangements for human and robot agents, which fails to accomplish complicated manufacturing tasks consisting of various operation sequences and different mechanical parts. To overcome the bottleneck, this paper proposes a temporal subgraph reasoning-based method for self-organising HRC task planning between multiple agents. Firstly, a tri-layer Knowledge Graph (KG) is defined to depict task-agent-operation relations in HRC tasks. Then, a subgraph mechanism is introduced to learn node embeddings from subregions of the HRC KG, which distills implicit information from local object sets. Thirdly, a temporal reasoning module is leveraged to integrate features from previous records and update the HRC KG for forecasting humans' and robots' subsequent operations. Finally, a car engine assembly task is demonstrated to evaluate the effectiveness of the proposed method, which outperforms other benchmarks in experimental results.
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| 37. |
- Li, Shufei, et al.
(författare)
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Towards Mutual-Cognitive Human-Robot Collaboration : A Zero-Shot Visual Reasoning Method
- 2023
-
Ingår i: 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- Human-Robot Collaboration (HRC) is showing the potential of widespread application in today's human-centric smart manufacturing, as prescribed by Industry 5.0. To enable safe and efficient collaboration, numerous visual perception methods have been explored, which allows the robot to perceive surroundings and plan collision-free, reactive manipulations. However, current visual perception approaches can only convey basic information between robots and humans, falling short of semantic knowledge. With this limitation, HRC cannot guarantee smooth operation when confronted with similar yet unseen situations in real-world applications. Therefore, a mutual-cognitive HRC architecture is proposed to plan human and robot operations based on the learning of knowledge representation of onsite situations and task structures. A zero-shot visual reasoning approach is introduced to derive suitable teamwork strategies in the mutual-cognitive HRC from perceived results, including human actions and detected objects. It assigns adaptive robot path planning and knowledge support for humans by incorporating perception components into a knowledge graph, even when dealing with a new but similar HRC task. Lastly, the significance of the proposed mutual-cognitive HRC system is revealed through its evaluation in collaborative disassembly tasks of aging electric vehicle batteries.
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| 38. |
- Lian, Binbin, et al.
(författare)
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Elastodynamic modeling and parameter sensitivity analysis of a parallelmanipulator with articulated traveling plate
- 2019
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Ingår i: The International Journal of Advanced Manufacturing Technology. - : Elsevier. - 0268-3768 .- 1433-3015.
-
Tidskriftsartikel (refereegranskat)abstract
- This paper deals with the elastodynamic modeling and parameter sensitivity analysis of a parallel manipulator with articulated traveling plate (PM-ATP) for assembling large components in aviation and aerospace. In the elastodynamic modeling, the PM-ATP is divided into four levels, i.e., element, part, substructure, and the whole mechanism. Herein, three substructures, including translation, bar, and ATP, are categorized according to the composition of the PM-ATP. Based on the kineto-elastodynamic (KED) method, differential motion equations of lower levels are formulated and assembled to build the elastodynamic model of the upper level. Degrees of freedom (DoFs) at connecting nodes of parts and deformation compatibility conditions of substructures are considered in the assembling. The proposed layer-by-layer method makes the modeling process more explicit, especially for the ATP having complex structures and multiple joints. Simulations by finite element software and experiments by dynamic testing system are carried out to verify the natural frequencies of the PM-ATP, which show consistency with the results from the analytical model. In the parameter sensitivity analysis, response surface method (RSM) is applied to formulate the surrogate model between the elastic dynamic performances and parameters. On this basis, differentiation of performance reliability to the parameter mean value and standard variance are adopted as the sensitivity indices, from which the main parameters that greatly affect the elastic dynamic performances can be selected as the design variables. The present works are necessary preparations for future optimal design. They can also provide reference for the analysis and evaluation of other PM-ATPs.
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| 39. |
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| 40. |
- Lian, Binbin, et al.
(författare)
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Static and dynamic optimization of a pose adjusting mechanism considering parameter changes during construction
- 2019
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier Ltd. - 0736-5845 .- 1879-2537. ; 59, s. 267-277
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Tidskriftsartikel (refereegranskat)abstract
- Having potentially high stiffness and good dynamic response, a parallel pose adjusting mechanism was proposed for being an attachment to a big serial robot of a macro-micro robotic system. This paper addresses its design optimization problem mainly concerning arrangements of design variables and objectives. Parameter changes during construction are added to the design variables in order to prevent the negative effects to the physical prototype. These parameter changes are interpreted as parameter uncertainty and modeled by probabilistic theory. For the objectives, both static and dynamic performances are simultaneously optimized by Pareto-based method. The involved performance indices are instantaneous energy based stiffness index, first natural frequency and execution mass. The optimization procedure is implemented as: (1) carrying out performance modeling and defining performance indices, (2) reformulating statistical objectives and probabilistic constraints considering parameter uncertainty, (3) conducting Pareto-based optimization with the aid of response surface method (RSM) and particle swarm optimization (PSO), (4) selecting optimal solution by searching for cooperative equilibrium point (CEP). By addressing parameter uncertainty and the best compromise among multiple objectives, the presented optimization procedure provides more reliable optimal parameters that would not be affected by minor parameter changes during construction, and less biased optimum between static and dynamic performances comparing with the conventional optimization methods. The proposed optimization method can also be applied to the other similar mechanisms.
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| 41. |
- Liu, Peiji, et al.
(författare)
-
A generalized method for the inherent energy performance modeling of machine tools
- 2021
-
Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 61, s. 406-422
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Tidskriftsartikel (refereegranskat)abstract
- Machine tools (MTs), as the key equipment of manufacturing systems, have enormous quantities and consume a great amount of energy. However, the diversity of both machines and their energy consumption properties make it difficult to transfer the energy-saving knowledge and services among different MT. To facilitate the initialization configuration of energy-saving services, the inherent energy performance (IEP) is investigated to describe the differences in energy consumption among MTs, and a generalized method for modeling the IEP of MT and its electrical subsystems is proposed. Three key enablers, including generalized experimental design rules, automatic coding, and data processing algorithms, are presented and integrated into a supporting system to reduce the modeling efforts and knowledge requirements. Case studies of an offline manufacturing scenario and an Internet of Things (IoT)-enabled manufacturing scenario were carried out to verify the effectiveness and convenience of the proposed method. The results show that the proposed method can provide essential modeling support for large-scale energy-saving service configurations and energy-efficient MT development.
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| 42. |
- Liu, Sichao, et al.
(författare)
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A Framework of Data-Driven Dynamic Optimisation for Smart Production Logistics
- 2020
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Ingår i: APMS 2020: Advances in Production Management Systems. Towards Smart and Digital Manufacturing. - Cham : Springer. ; , s. 213-221
-
Konferensbidrag (refereegranskat)abstract
- Production logistics systems in the context of manufacturing, especially in automotive sectors today, are challenged by the lack of real-time data of logistics resources, optimal configuration and management strategies of materials, and optimisation approaches of logistics operations. This turns out to be the bottleneck in achieving flexible and adaptive logistics operations. To address these challenges, this paper presents a framework of real-time data-driven dynamic optimisation schemes for production logistics systems using the combined strength of advanced technologies and decision-making algorithms. Within the context, a real-time data sensing model is developed for the timely acquisition, storage, distribution, and utilisation of equipment and process data in which sensing devices are deployed on physical shop floors. The value-added data enable production logistics processes to be digitally visible and are shared among logistics resources. A multi-agent-based optimisation scheme for production logistics systems based on real-time data is developed to obtain the optimal configuration of logistics resources. Finally, a prototype-based simulation within an automotive manufacturing shop floor is used to demonstrate the proposed conceptual framework.
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| 43. |
- Liu, Sichao, et al.
(författare)
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An ‘Internet of Things’ enabled dynamic optimization method for smart vehicles and logistics tasks
- 2019
-
Ingår i: Journal of Cleaner Production. - : Elsevier. - 0959-6526 .- 1879-1786. ; 215, s. 806-820
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Tidskriftsartikel (refereegranskat)abstract
- Centralized and one-way logistics services and the lack of real-time information of logistics resources are common in the logistics industry. This has resulted in the increased logistics cost, energy consumption, logistics resources consumption, and the decreased loading rate. Therefore, it is difficult to achieve efficient, sustainable, and green logistics services with dramatically increasing logistics demands. To deal with such challenges, a real-time information-driven dynamic optimization strategy for smart vehicles and logistics tasks towards green logistics is proposed. Firstly, an ‘Internet of Things’-enabled real-time status sensing model of logistics vehicles is developed. It enables the vehicles to obtain and transmit real-time information to the dynamic distribution center, which manages value-added logistics information. Then, such information can be shared among logistics companies. A dynamic optimization method for smart vehicles and logistics tasks is developed to optimize logistics resources, and achieve a sustainable balance between economic, environmental, and social objectives. Finally, a case study is carried out to demonstrate the effectiveness of the proposed optimization method. The results show that it contributes to reducing logistics cost and fuel consumption, improving vehicles’ utilization rate, and achieving real-time logistics services with high efficiency.
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| 44. |
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| 45. |
- Liu, Sichao, et al.
(författare)
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Digital twin-enabled advance execution for human-robot collaborative assembly
- 2022
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Ingår i: CIRP annals. - : Elsevier BV. - 0007-8506 .- 1726-0604. ; 71:1, s. 25-28
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Tidskriftsartikel (refereegranskat)abstract
- A reliable human-robot workcell relies on accurate and nearly real-time updated models, especially in a constrained yet dynamic environment. This paper investigates digital twin-driven human-robot collaborative assembly enabled by function blocks. Leveraging sensor data, digital models are developed to precisely mimic physical human-robot collaborative settings supported by a digital-twin architecture. An advance-execution twin system based on the current status through real-time condition monitoring performs assembly planning and adaptive robot control using a network of function blocks. An augmented reality-based interaction method using HoloLens further facilitates human-centric assembly. An engine-assembly case study is performed to validate the effectiveness of the system.
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| 46. |
- Liu, Sichao, et al.
(författare)
-
Function block-based multimodal control for symbiotic human-robot collaborative assembly
- 2021
-
Ingår i: Journal of manufacturing science and engineering. - : ASME International. - 1087-1357 .- 1528-8935. ; 143:9, s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- In human–robot collaborative assembly, robots are often required to dynamically changetheir preplanned tasks to collaborate with human operators in close proximity. One essential requirement of such an environment is enhanced flexibility and adaptability, as well asreduced effort on the conventional (re)programming of robots, especially for complexassembly tasks. However, the robots used today are controlled by rigid native codes thatcannot support efficient human–robot collaboration. To solve such challenges, thisarticle presents a novel function block-enabled multimodal control approach for symbiotichuman–robot collaborative assembly. Within the context, event-driven function blocks asreusable functional modules embedded with smart algorithms are used for the encapsulation of assembly feature-based tasks/processes and control commands that are transferredto the controller of robots for execution. Then, multimodal control commands in the form ofsensorless haptics, gestures, and voices serve as the inputs of the function blocks to triggertask execution and human-centered robot control within a safe human–robot collaborativeenvironment. Finally, the performed processes of the method are experimentally validatedby a case study in an assembly work cell on assisting the operator during the collaborativeassembly. This unique combination facilitates programming-free robot control and theimplementation of the multimodal symbiotic human–robot collaborative assembly withthe enhanced adaptability and flexibility.
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| 47. |
- Liu, Sichao, et al.
(författare)
-
Leveraging multimodal data for intuitive robot control towards human-robot collaborative assembly
- 2021
-
Ingår i: <em>Procedia CIRP of the 54th Conference on Manufacturing Systems</em>. - : Elsevier BV. ; , s. 206-211
-
Konferensbidrag (refereegranskat)abstract
- In human-robot collaborative assembly, robots are often required to assist human operators to execute the assembly of complex tasks. However, the robots cannot be intuitively controlled to execute accurate task assembly and motion control in close proximity. In response to this need, a novel approach using multimodal data is developed for human-centred robot control in human-robot collaborative assembly. An interface design is developed to fuse multimodal communication channels for robust and adaptive robot control, and then multimodal data are defined as control input for assembly task execution. This control scheme offers human operators symbiotic multimodal tools for proactive HRC with enhanced flexibility and adaptability.
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| 48. |
- Liu, Sichao, et al.
(författare)
-
Multimodal Data-Driven Robot Control for Human-Robot Collaborative Assembly
- 2022
-
Ingår i: Journal of manufacturing science and engineering. - : ASME International. - 1087-1357 .- 1528-8935. ; 144:5
-
Tidskriftsartikel (refereegranskat)abstract
- In human-robot collaborative assembly, leveraging multimodal commands for intuitive robot control remains a challenge from command translation to efficient collaborative operations. This article investigates multimodal data-driven robot control for human-robot collaborative assembly. Leveraging function blocks, a programming-free human-robot interface is designed to fuse multimodal human commands that accurately trigger defined robot control modalities. Deep learning is explored to develop a command classification system for low-latency and high-accuracy robot control, in which a spatial-temporal graph convolutional network is developed for a reliable and accurate translation of brainwave command phrases into robot commands. Then, multimodal data-driven high-level robot control during assembly is facilitated by the use of event-driven function blocks. The high-level commands serve as triggering events to algorithms execution of fine robot manipulation and assembly feature-based collaborative assembly. Finally, a partial car engine assembly deployed to a robot team is chosen as a case study to demonstrate the effectiveness of the developed system.
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| 49. |
- Liu, Sichao, et al.
(författare)
-
Sensorless force estimation for industrial robots using disturbance observer and neural learning of friction approximation
- 2021
-
Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier. - 0736-5845 .- 1879-2537. ; 71, s. 1-11
-
Tidskriftsartikel (refereegranskat)abstract
- Contact force estimation enables robots to physically interact with unknown environments and to work with human operators in a shared workspace. Most heavy-duty industrial robots without built-in force/torque sensors rely on the inverse dynamics for the sensorless force estimation. However, this scheme suffers from the serious model uncertainty induced by the nonnegligible noise in the estimation process. This paper proposes a sensorless scheme to estimate the unknown contact force induced by the physical interaction with robots. The model-based identification scheme is initially used to obtain dynamic parameters. Then, neural learning of friction approximation is designed to enhance estimation performance for robotic systems subject with the model uncertainty. The external force exerted on the robot is estimated by a disturbance observer which models the external disturbance. A momentum observer is modified to develop a disturbance Kalman filter-based approach for estimating the contact force. The neural network-based model uncertainty and measurement noise level are analysed to guarantee the robustness of the Kalman filter-based force observer. The proposed scheme is verified by the measurement data from a heavy-duty industrial robot with 6 degrees of freedom (KUKA AUGLIS six). The experimental results are used to demonstrate the estimation performance of the proposed approach by the comparison with the existing schemes.
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| 50. |
- Liu, Sichao, et al.
(författare)
-
Sensorless haptic control for human-robot collaborative assembly
- 2021
-
Ingår i: CIRP - Journal of Manufacturing Science and Technology. - : Elsevier BV. - 1755-5817 .- 1878-0016. ; 32, s. 132-144
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an approach to haptically controlling an industrial robot without using any external sensors for human-robot collaborative assembly. The sensorless haptic control approach is enabled by the dynamic models of the robot where only joint angles and joint torques are measurable. Accurate dynamic models of the robot in the presliding and sliding regimes are developed to estimate the external forces/torques, where the friction model is also explored. The estimated external force applied to the robot by an operator is converted to the reference position and speed of the robot by an admittance controller. In this research, adaptive admittance control is adopted to support human-robot collaborative assembly, naturally and easily, with accurate positioning and control for smooth movement. Moreover, torque-based commands are used to control the robot’s assembly operations. Finally, the proposed approach is validated by a case study on assisting an operator during the collaborative assembly of a car engine.
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