| 1. |
- 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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| 2. |
- 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.
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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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| 3. |
- 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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| 4. |
- Pang, Shibao, et al.
(författare)
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Dual-Dimensional Manufacturing Service Collaboration Optimization Toward Industrial Internet Platforms
- 2023
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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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| 5. |
- Wang, Baicun, et al.
(författare)
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Human-centric smart manufacturing
- 2023
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 69, s. 18-19
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 6. |
- 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
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 89
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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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| 7. |
- 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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| 8. |
- 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
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 67, s. 97-110
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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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| 9. |
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Advanced human-robot collaboration in manufacturing
- 2021
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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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| 10. |
- 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.
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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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