| 1. |
- Liu, Yongkui, et al.
(författare)
-
Logistics-involved service composition in a dynamic cloud manufacturing environment : A DDPG-based approach
- 2022
-
Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 76, s. 102323-
-
Tidskriftsartikel (refereegranskat)abstract
- Service composition as an important technique for combining multiple services to construct a value-added service is a major research issue in cloud manufacturing. Highly dynamic environments present great challenges to cloud manufacturing service composition (CMfg-SC). Most of previous studies employ heuristic algorithms to solve service composition issues in cloud manufacturing, which, however, are designed for specific problems and lack adaptability necessary to dynamic environment. Hence, CMfg-SC calls for new adaptive approaches. Recent advances in deep reinforcement learning (DRL) provide a new means for solving this issue. Based on DRL, we propose a Deep Deterministic Policy Gradient (DDPG)-based service composition approach to cloud manufacturing, with which optimal service composition solutions can be learned through repeated training. Performance of DDPG in solving CMfg-SC in both static and dynamic environments is examined. Results obtained with another DRL algorithm -Deep Q-Networks (DQN) and the traditional Ant Colony Optimization (ACO) are also presented. Comparison indicates that DDPG has better adaptability, robustness, and extensibility to dynamic environments than ACO, although ACO converges faster and its steady QoS value of the service composition solution is higher than that of DDPG by 0.997%. DDPG outperforms DQN in convergence speed and stability, and the QoS value of the service composition solution of DDPG is higher than that of DQN by 3.249%.
|
|
| 2. |
- Bi, Z. M., et al.
(författare)
-
Safety assurance mechanisms of collaborative robotic systems in manufacturing
- 2021
-
Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 67
-
Tidskriftsartikel (refereegranskat)abstract
- Collaborative robots (cobots) are robots that are designed to collaborate with humans in an open workspace. In contrast to industrial robots in an enclosed environment, cobots need additional mechanisms to assure humans' safety in collaborations. It is especially true when a cobot is used in manufacturing environment; since the workload or moving mass is usually large enough to hurt human when a contact occurs. In this article, we are interested in understanding the existing studies on cobots, and especially, the safety requirements, and the methods and challenges of safety assurance. The state of the art of safety assurance of cobots is discussed at the aspects of key functional requirements (FRs), collaboration variants, standardizations, and safety mechanisms. The identified technological bottlenecks are (1) acquiring, processing, and fusing diversified data for risk classification, (2) effectively updating the control to avoid any interference in a real-time mode, (3) developing new technologies for the improvement of HMI performances, especially, workloads and speeds, and (4) reducing the overall cost of safety assurance features. To promote cobots in manufacturing applications, the future researches are expected for (1) the systematic theory and methods to design and build cobots with the integration of ergonomic structures, sensing, real-time controls, and human-robot interfaces, (2) intuitive programming, task-driven programming, and skill-based programming which incorporate the risk management and the evaluations of biomechanical load and stopping distance, and (3) advanced instrumentations and algorithms for effective sensing, processing, and fusing of diversified data, and machine learning for high-level complexity and uncertainty. The needs of the safety assurance of integrated robotic systems are specially discussed with two development examples.
|
|
| 3. |
- Li, Wei, et al.
(författare)
-
Non-lab and semi-lab algorithms for screening undiagnosed diabetes : A cross-sectional study
- 2018
-
Ingår i: EBioMedicine. - : ELSEVIER SCIENCE BV. - 2352-3964. ; 35, s. 307-316
-
Tidskriftsartikel (refereegranskat)abstract
- Background: The terrifying undiagnosed rate and high prevalence of diabetes have become a public emergency. A high efficiency and cost-effective early recognition method is urgently needed. We aimed to generate innovative, user-friendly nomograms that can be applied for diabetes screening in different ethnic groups in China using the non-lab or noninvasive semi-lab data. Methods: This multicenter, multi-ethnic, population-based, cross-sectional study was conducted in eight sites in China by enrolling subjects aged 20-70. Sociodemographic and anthropometric characteristics were collected. Blood and urine samples were obtained 2 h following a standard 75 g glucose solution. In the final analysis, 10,794 participants were included and randomized into model development (n - 8096) and model validation (n = 2698) group with a ratio of 3:1. Nomograms were developed by the stepwise binary logistic regression. The nomograms were validated internally by a bootstrap sampling method in the model development set and externally in the model validation set. The area under the receiver operating characteristic curve (AUC) was used to assess the screening performance of the nomograms. Decision curve analysis was applied to calculate the net benefit of the screening model. Results: The overall prevalence of undiagnosed diabetes was 9.8% (1059/10794) according to ADA criteria. The non-lab model revealed that gender, age, body mass index, waist circumference, hypertension, ethnicities, vegetable daily consumption and family history of diabetes were independent risk factors for diabetes. By adding 2 h post meal glycosuria qualitative to the non-lab model, the semi-lab model showed an improved Akaike information criterion (AIC: 4506 to 3580). The AUC of the semi-lab model was statistically larger than the non-lab model (0.868 vs 0.763, P < 0.001). The optimal cutoff probability in semi-lab and non-lab nomograms were 0.088 and 0.098, respectively. The sensitivity and specificity were 76.3% and 81.6%, respectively in semi-lab nomogram, and 72.1% and 673% in non-lab nomogram at the optimal cut off point. The decision curve analysis also revealed a bigger decrease of avoidable OGTT test (52 per 100 subjects) in the semi-lab model compared to the non-lab model (36 per 100 subjects) and the existed New Chinese Diabetes Risk Score (NCDRS, 35 per 100 subjects). Conclusion: The non-lab and semi-lab nomograms appear to be reliable tools for diabetes screening, especially in developing countries. However, the semi-lab model outperformed the non-lab model and NCDRS prediction systems and might be worth being adopted as decision support in diabetes screening in China.
|
|
| 4. |
- Liang, Huagang, et al.
(författare)
-
Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning
- 2021
-
Ingår i: Robotics and Computer-Integrated Manufacturing. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0736-5845 .- 1879-2537. ; 67
-
Tidskriftsartikel (refereegranskat)abstract
- Cloud manufacturing is a new manufacturing model that aims to provide on-demand manufacturing services to consumers over the Internet. Service composition is an essential issue as well as an important technique in cloud manufacturing (CMfg) that supports construction of larger-granularity, value-added services by combining a number of smaller-granularity services to satisfy consumers' complex requirements. Meta-heuristics algorithms such as genetic algorithm, particle swarm optimization, and ant colony algorithm are frequently employed for addressing service composition issues in cloud manufacturing. These algorithms, however, require complex design flows and painstaking parameter tuning, and lack adaptability to dynamic environment. Deep re-inforcement learning (DRL) provides an alternative approach for solving cloud manufacturing service compo-sition (CMfg-SC) issues. DRL as model-free artificial intelligent methods enables a system to learn optimal service composition solutions through training, which can therefore circumvent the aforementioned problems with meta-heuristics algorithms. This paper is dedicated to exploring possible applications of DRL in CMfg-SC. A logistics-involved QoS-aware DRL-based CMfg-SC is proposed. A dueling Deep Q-Network (DQN) with prior-itized replay named PD-DQN is designed as the DRL algorithm. Effectiveness, robustness, adaptability, and scalability of PD-DQN are investigated, and compared with that of the basic DQN and Q-learning. Experimental results indicate that PD-DQN is able to effectively address the CMfg-SC problem.
|
|
| 5. |
- Liu, Y. -K, et al.
(författare)
-
A multi-agent architecture for scheduling in platform-based smart manufacturing systems
- 2019
-
Ingår i: Frontiers of Information Technology and Electronic Engineering. - : Zhejiang University Press. - 2095-9184. ; 20:11, s. 1465-1492
-
Tidskriftsartikel (refereegranskat)abstract
- During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterprise-level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for scheduling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.
|
|
| 6. |
- Ma, Yue, et al.
(författare)
-
Acid suppressants use and risk of atherosclerotic cardiovascular disease in middle-aged and older adults
- 2022
-
Ingår i: Atherosclerosis. - : Elsevier BV. - 0021-9150. ; 358, s. 47-54
-
Tidskriftsartikel (refereegranskat)abstract
- Background and aims: Concerns regarding adverse events associated with the use of acid suppressants have increased. However, the impact of proton pump inhibitors (PPIs) and histamine‐2 receptor antagonists (H2RAs) on the risk of atherosclerotic cardiovascular disease (ASCVD) remains unknown. This study aimed to estimate the risk of ASCVD in association with the use of PPIs and H2RAs. Methods: This prospective cohort study included participants without cardiovascular diseases or anti-hypertensive treatment at baseline (2006–2010) in the UK Biobank. The outcomes were ASCVD and each subtype (coronary artery disease, myocardial infarction, peripheral artery disease, and ischemic stroke). The association was estimated by Cox proportional-hazards models. Results: Among 316,730 individuals (aged 50–88 years), during a median of 12.5 years of follow-up, we documented 13,503 (4.3%) incident ASCVD. Regular PPIs use was associated with a higher risk of ASCVD (HR: 1.16, 95% CI: 1.09–1.23) and every subtype of ASCVD. Among each type of PPIs, omeprazole (HR: 1.19, 95% CI: 1.11–1.28), lansoprazole (HR: 1.11, 95% CI: 1.02–1.22), and pantoprazole (HR: 1.40, 95% CI: 1.00–1.97) were associated with a higher risk of ASCVD. Stratification analysis showed that PPIs use was associated with a higher risk of ASCVD among individuals without indications of medications for PPIs. In addition, use of H2RAs was not related to the risk of ASCVD (HR: 0.97, 95% CI: 0.85–1.11). Conclusions: PPIs were associated with increased risk of ASCVD, particularly amongst participants without indications for medication. Our findings are of important practical significance and suggest that clinicians should be cautious in prophylactic use of PPIs.
|
|
| 7. |
- Zhang, H, et al.
(författare)
-
Service composition in cloud manufacturing : A DQN-based approach
- 2020
-
Ingår i: International Series in Operations Research and Management Science. - Cham : Springer. ; , s. 239-254
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Cloud manufacturing is a new service-oriented manufacturing model that integrates distributed manufacturing resources to provide on-demand manufacturing services over the Internet. Service composition that builds larger-granularity, value-added services by combining a number of smaller-granularity services to satisfy consumers’ complex requirements is an important issue in cloud manufacturing. Meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, and ant colony algorithm are frequently employed for addressing service composition issues in cloud manufacturing. However, these algorithms require complex design flows and lack adaptability to dynamic environment. Deep reinforcement learning provides an alternative approach for solving cloud manufacturing service composition issues. This chapter proposes a deep Q-network (DQN) based approach for service composition in cloud manufacturing, which is able to find optimal service composition solutions through repeated training and learning. Results of experiments that take into account changes of service scales and service unavailability reveal the scalability and robustness of the DQN algorithm-based service composition approach.
|
|
| 8. |
- Zhang, Zhuangzhuang, et al.
(författare)
-
A residual reinforcement learning method for robotic assembly using visual and force information
- 2024
-
Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 72, s. 245-262
-
Tidskriftsartikel (refereegranskat)abstract
- Robotic autonomous assembly is critical in intelligent manufacturing and has always been a research hotspot. Most previous approaches rely on prior knowledge, such as geometric parameters and pose information of the assembled parts, which are hard to estimate in unstructured environments. This paper proposes a residual reinforcement learning (RL) policy for robotic assembly via combining visual and force information. The residual RL policy, which consists of a visual-based policy and a force-based policy, is trained and tested in an end-to-end manner. In the assembly procedure, the visual-based policy focuses on spatial search, while the force-based policy handles the interactive behaviors. The experimental results reveal the high sample efficiency of our approach, which exhibits the ability to generalize across diverse assembly tasks involving variations in geometries, clearances, and configurations. The validation experiments are conducted both in simulation and on a real robot.
|
|
| 9. |
- Zhang, Zhuangzhuang, et al.
(författare)
-
Digital twin-enabled grasp outcomes assessment for unknown objects using visual-tactile fusion perception
- 2023
-
Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 84
-
Tidskriftsartikel (refereegranskat)abstract
- Humans can instinctively predict whether a given grasp will be successful through visual and rich haptic feedback. Towards the next generation of smart robotic manufacturing, robots must be equipped with similar capabilities to cope with grasping unknown objects in unstructured environments. However, most existing data-driven methods take global visual images and tactile readings from the real-world system as input, making them incapable of predicting the grasp outcomes for cluttered objects or generating large-scale datasets. First, this paper proposes a visual-tactile fusion method to predict the results of grasping cluttered objects, which is the most common scenario for grasping applications. Concretely, the multimodal fusion network (MMFN) uses the local point cloud within the gripper as the visual signal input, while the tactile signal input is the images provided by two high-resolution tactile sensors. Second, collecting data in the real world is high-cost and time-consuming. Therefore, this paper proposes a digital twin-enabled robotic grasping system to collect large-scale multimodal datasets and investigates how to apply domain randomization and domain adaptation to bridge the sim-to-real transfer gap. Finally, extensive validation experiments are conducted in physical and virtual environments. The experimental results demonstrate the effectiveness of the proposed method in assessing grasp stability for cluttered objects and performing zero-shot sim-to-real policy transfer on the real robot with the aid of the proposed migration strategy.
|
|
| 10. |
- Zhou, Lihui, et al.
(författare)
-
Association of impaired lung function with dementia, and brain magnetic resonance imaging indices : a large population-based longitudinal study
- 2022
-
Ingår i: Age and Ageing. - : Oxford University Press (OUP). - 1468-2834 .- 0002-0729. ; 51:11
-
Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: to examine the association between different patterns of impaired lung function with the incident risk of dementia and magnetic resonance imaging (MRI)-based brain structural features. METHODS: in UK Biobank, a total of 308,534 dementia-free participants with valid lung function measures (forced expiratory volume in 1 s [FEV1] and forced vital capacity [FVC]) were included. Association was assessed using Cox proportional hazards regression model. Furthermore, the association between impaired lung function and brain MRI biomarkers related to cognitive function was analysed among 30,159 participants. RESULTS: during a median follow-up of 12.6 years, 3,607 incident all-cause dementia cases were recorded. Restrictive impairment (hazard ratio [HR], 1.42; 95% confidence interval [CI], 1.27-1.60) and obstructive impairment (HR, 1.28; 95% CI, 1.15-1.42) were associated with higher risk of all-cause dementia. The restricted cubic splines indicated FEV1% predicted and FVC % predicted had reversed J-shaped associations with dementia. Participants with impaired lung function have higher risks of all-cause dementia across all apolipoprotein E (APOE) risk categories, whereas associations were stronger among those of low APOE risk (P for interaction = 0.034). In addition, restrictive and obstructive impairment were linked to lower total (β: -0.075, SE: 0.021, Pfdr = 0.002; β: -0.033, SE: 0.017, Pfdr = 0.069) and frontoparietal grey matter volumes, higher white matter hyperintensity, poorer white matter integrity, lower hippocampus (β: -0.066, SE: 0.024, Pfdr = 0.017; β: -0.051, SE: 0.019, Pfdr = 0.019) and other subcortical volumes. CONCLUSIONS: participants with restrictive and obstructive impairments had a higher risk of dementia. Brain MRI indices further supported adverse effects and provided insight into potential pathophysiology biomarkers.
|
|
| 11. |
- Bi, Z., et al.
(författare)
-
Internet of things (IoT) and big data analytics (BDA) for digital manufacturing (DM)
- 2021
-
Ingår i: International Journal of Production Research. - : Informa UK Limited. - 0020-7543 .- 1366-588X. ; , s. 1-18
-
Tidskriftsartikel (refereegranskat)abstract
- This paper aims to investigate the impact of enterprise architecture (EA) on system capabilities in dealing with changes and uncertainties in globalised business environments. Enterprise information systems are viewed as information systems to acquire, process, and utilise data in decision-making supports at all levels and domains of businesses, and Internet of things (IoT), big data analytics (BDA), and digital manufacturing (DM) are introduced as representative enabling technologies for data collection, processing, and utilisation in manufacturing applications. The historical development of manufacturing technologies is examined to understand the evolution of system paradigms. The Shannon entropy is adopted to measure the complexity of systems and illustrate the roles of EAs in managing system complexity and achieving system stability in the long term. It is our argument that existing EAs sacrifice system flexibility, resilience, and adaptability for the reduction of system complexity; note that higher adaptability is critical to make a manufacturing system successfully. New EA is proposed to maximise system capabilities for higher flexibility, resilience, and adaptability. The potentials of the proposed EA to modern manufacturing are explored to identify critical research topics with illustrative examples from an application perspective.
|
|
| 12. |
- Bi, Zhuming, et al.
(författare)
-
Multidisciplinary Design Optimization in Engineering
- 2013
-
Ingår i: Mathematical problems in engineering (Print). - : Hindawi Limited. - 1024-123X .- 1563-5147. ; , s. 351097-
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
|
|
| 13. |
- Chen, Mo, et al.
(författare)
-
Study on Efficient Fused Deposition Modelling of Thermoplastic Polyurethane Inflatable Wall Features for Airtightness
- 2020
-
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.
|
|
| 14. |
|
|
| 15. |
- Fang, Wei, et al.
(författare)
-
Head-mounted display augmented reality in manufacturing : A systematic review
- 2023
-
Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 83
-
Forskningsöversikt (refereegranskat)abstract
- Head-mounted display (HMD) augmented reality (AR) has attracted more and more attention in manufacturing activities, as it enables operators to access visual guidance in front of their view directly while freeing human's two hands. Nevertheless, HMD AR has not been widely adopted in manufacturing fields as humans expected since the release of Google Glass in 2012, and thus it is important to understand the related issues arising from the actual deployments of HMD AR on the shop floor. To the best of the authors' knowledge, there have not been comprehensive discussions on HMD AR in manufacturing from a holistic perspective. This article aims to provide an extensive map for the distribution of HMD AR in various manufacturing activities and a systematic overview of underlying technical perspectives associated with their actual industrial applications between 2010 and 2022, involving AR visualization, tracking and registration, context awareness, human-machine interaction, as well as ergonomics and usability, which are significant for the actual AR deployments for human-centric manufacturing in Industry 5.0. It is also worth mentioning that this work presents a historical overview of the current research on the development of HMD AR, as well as a summary of the existing methods and open problems for HMD AR in manufacturing. It is helpful to understand the current technical situations of HMD AR while providing insights to deploy industrial AR applications and perform academic research in the future.
|
|
| 16. |
- Gao, Robert X., et al.
(författare)
-
Human motion recognition and prediction for robot control
- 2021
-
Ingår i: Advanced Human-Robot Collaboration in Manufacturing. - Cham : Springer Nature. ; , s. 261-282
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The ever-increasing demand for higher productivity, lower cost and improved safety continues to drive the advancement of manufacturing technologies. As one of the key elements, human-robot collaboration (HRC) envisions a workspace where humans and robots can dynamically collaborate for improved operational efficiency while maintaining safety. As the effectiveness of HRC is affected by a robot's ability to sense, understand and forecast the state of the collaborating human worker, human action recognition and motion trajectory prediction have become a crucial part in realising HRC. In this chapter, deep-learning-based methods for accomplishing this goal, based on the in-situ sensing data from the workspace are presented. Specifically, to account for the variability and heterogeneity of human workers during assembly, a context-aware deep convolutional neural network (DCNN) has been developed to identify the task-associated context for inferencing human actions. To improve the accuracy and reliability of human motion trajectory prediction, a functional unit-incorporated recurrent neural network (RNN) has been developed to parse worker's motion patterns and forecast worker's future motion trajectories. Collectively, these techniques allow the robot to answer the question: "which tool or part should be delivered to which location next?", and enable online robot action planning and execution for the collaborative assembly operation. The methods developed are experimentally evaluated, with the collaborative assembly of an automotive engine as a case study.
|
|
| 17. |
- Gu, Song, et al.
(författare)
-
Online Video Object Segmentation via Boundary-Constrained Low-Rank Sparse Representation
- 2019
-
Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 7, s. 53520-53533
-
Tidskriftsartikel (refereegranskat)abstract
- Graphcut-based algorithm is adopted in many video object segmentation systems because different terms can be probabilistically fused together in a framework. Constructing spatio-temporal coherences is an important stage in segmentation systems. However, many steps are involved when computing a key term with good discriminative power. If the cascade steps are adopted, the inaccurate output of the previous step will definitely affect the next step, leading to inaccurate segmentation. In this paper, a key term that is computed by a single framework referred to as boundary-constrained low-rank sparse representation (BCLRSR) is proposed to achieve the accurate segmentation. By treating the elements as linear combinations of dictionary templates, low-rank sparse optimization is adopted to achieve the spatio-temporal saliency. For adding the spatial information to the low-rank sparse model, a boundary constraint is adopted in the framework as a Laplacian regularization. A BCLRSR saliency is then obtained by the represented coefficients, which measure the similarity between the elements in the current frame and the ones in the dictionary. At last, the object is segmented by minimizing the energy function, which is formalized by the spatio-temporal coherences. The experiments on some public datasets show that our proposed algorithm outperforms the state-of-the-art methods.
|
|
| 18. |
- Guo, Zhengang, et al.
(författare)
-
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.
|
|
| 19. |
- Ji, Qinglei, et al.
(författare)
-
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.
|
|
| 20. |
- Ji, Qinglei, et al.
(författare)
-
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.
|
|
| 21. |
|
|
| 22. |
|
|
| 23. |
- Li, Xuebing, et al.
(författare)
-
A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion
- 2021
-
Ingår i: Measurement. - : Elsevier BV. - 0263-2241 .- 1873-412X. ; 185
-
Tidskriftsartikel (refereegranskat)abstract
- Tool wear monitoring during the cutting process is crucial for ensuring part quality and productivity. A datadriven monitoring approach based on radar map feature fusion is proposed for tool wear recognition and quantitative prediction, aiming at tracking the evolution of tool wear comprehensively. Specifically, the sensitive features from multi-source signals are fused by a radar map, and health indicators capable of characterizing the tool wear evolution are obtained. For the recognition of tool wear state and the quantitative prediction of tool wear values, the Adaboost Decision Tree (Adaboost-DT) ensemble learning model and stacked bi-directional long short-term memory (SBiLSTM) deep learning network are established, respectively. Experimental results demonstrated that the proposed approach could recognize the current wear state quickly and accurately whilst predicting wear values based on limited historical data available. Combining tool wear recognition and prediction results contributes to making a more flexible tool replacement decision in intelligent manufacturing processes.
|
|
| 24. |
|
|
| 25. |
- Lin, Jing, et al.
(författare)
-
Association of time spent in outdoor light and genetic susceptibility with the risk of type 2 diabetes
- 2023
-
Ingår i: Science of the Total Environment. - 0048-9697 .- 1879-1026. ; 888
-
Tidskriftsartikel (refereegranskat)abstract
- To explore the joint association of time spent in outdoor light and genetic susceptibility with the risk of type 2 diabetes (T2D). A total of 395,809 individuals of European ancestry with diabetes-free at baseline in the UK Biobank were in-cluded. Time spent in outdoor light on a typical day in summer or winter was obtained from the questionnaire. T2D genetic risk was quantified via the polygenic risk score (PRS) and divided into three levels based on tertiles (lower, in-termediate, and higher). T2D cases were ascertained according to the hospital records of diagnoses. After the median follow-up of 12.55 years, the association of outdoor light time and T2D risk demonstrated a nonlinear (J-shaped) trend. Compared to individuals with an average of 1.5-2.5 h/day of outdoor light, individuals who spent <1.5 h/ day or >2.5 h/day in outdoor light both had an elevated risk of T2D, and the risk of T2D related to <1.5 h/day outdoor light time was much higher (hazard ratio [HR] = 1.10, 95 % confidence interval [CI]: 1.06 to 1.15). After combining with PRS, in comparison with the lower PRS - average 1.5-2.5 h/day outdoor light group (reference), the higher PRS - <1.5 h/day outdoor light group had the highest T2D risk (HR = 2.74, 95 % CI: 2.55 to 2.94), the higher PRS - >2.5 h/ day outdoor light group also had a higher risk of T2D (HR = 2.58, 95 % CI: 2.43 to 2.74). The interaction between average outdoor light time and genetic susceptibility for T2D was statistically significant (Paverage for interaction <0.001). We found that optimal outdoor light time may modify the genetic risk for T2D. This suggests the T2D risk related to genetic factors could be prevented by spending optimal outdoor light time.
|
|
