| 1. |
- Wang, Baicun, et al.
(author)
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Human-centric smart manufacturing
- 2023
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In: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 69, s. 18-19
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Journal article (other academic/artistic)
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| 2. |
- Zhou, Huiying, et al.
(author)
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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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In: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 67, s. 97-110
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Journal article (peer-reviewed)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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| 3. |
- Huang, Sihan, et al.
(author)
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Industry 5.0 and Society 5.0-Comparison, complementation and co-evolution
- 2022
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In: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 64, s. 424-428
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Journal article (peer-reviewed)abstract
- Recently, the futuristic industry and society have caught increasing attention, that is, on Industry 5.0 and Society 5.0. Industry 5.0 is announced by European Commission toward a sustainable, human-centric, and resilient European industry. Society 5.0 is proposed by Japan Cabinet to balance economic advancement with the reso-lution of social problems in Japanese society. Generally, the revolutions of industry and society have profoundly interacted with each other since the first industrial revolution. The coexistence of Industry 5.0 and Society 5.0 could raise varying confusions to be clarified and a series of questions to be answered. Therefore, we attempt to present the comparison, complementation, and co-evolution between Industry 5.0 and Society 5.0 to address the corresponding foundational arguments about Industry 5.0 and Society 5.0, which could be the basic inspiration for future investigation and discussion and accelerate the development of Industry 5.0 and Society 5.0.
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| 4. |
- Leng, Jiewu, et al.
(author)
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Industry 5.0 : Prospect and retrospect
- 2022
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In: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 65, s. 279-295
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Journal article (peer-reviewed)abstract
- Industry 5.0 blows the whistle on global industrial transformation. It aims to place humans' well-being at the center of manufacturing systems, thereby achieving social goals beyond employment and growth to provide prosperity robustly for the sustainable development of all humanity. However, the current exploration of Industry 5.0 is still in its infancy where research findings are relatively scarce and little systematic. This paper first reviews the evolutionary vein of Industry 5.0 and three leading characteristics of Industry 5.0: human-centricity, sustainability, and resiliency. The connotation system of Industry 5.0 is discussed, and its diversified essence is analyzed. Then, this paper constructs a tri-dimension system architecture for implementing Industry 5.0, namely, the technical dimension, reality dimension, and application dimension. The paper further discusses key enablers, the future implementation path, potential applications, and challenges of realistic scenarios of Industry 5.0. Finally, the limitations of the current research are discussed with potential future research directions highlighted. It is expected that this review work will arouse lively discussions and debates, and bring together the strengths of all beings for building a comprehensive system of Industry 5.0.
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| 5. |
- Leng, Jiewu, et al.
(author)
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Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges
- 2024
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In: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 73, s. 349-363
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Research review (peer-reviewed)abstract
- With the continuous development of human-centric, resilient, and sustainable manufacturing towards Industry 5.0, Artificial Intelligence (AI) has gradually unveiled new opportunities for additional functionalities, new features, and tendencies in the industrial landscape. On the other hand, the technology-driven Industry 4.0 paradigm is still in full swing. However, there exist many unreasonable designs, configurations, and implementations of Industrial Artificial Intelligence (IndAI) in practice before achieving either Industry 4.0 or Industry 5.0 vision, and a significant gap between the individualized requirement and actual implementation result still exists. To provide insights for designing appropriate models and algorithms in the upgrading process of the industry, this perspective article classifies IndAI by rating the intelligence levels and presents four principles of implementing IndAI. Three significant opportunities of IndAI, namely, collaborative intelligence, self-learning intelligence, and crowd intelligence, towards Industry 5.0 vision are identified to promote the transition from a technology-driven initiative in Industry 4.0 to the coexistence and interplay of Industry 4.0 and a value-oriented proposition in Industry 5.0. Then, pathways for implementing IndAI towards Industry 5.0 together with key empowering techniques are discussed. Social barriers, technology challenges, and future research directions of IndAI are concluded, respectively. We believe that our effort can lay a foundation for unlocking the power of IndAI in futuristic Industry 5.0 research and engineering practice.
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| 6. |
- Li, Chengxi, et al.
(author)
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Deep reinforcement learning in smart manufacturing : A review and prospects
- 2023
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In: CIRP - Journal of Manufacturing Science and Technology. - : Elsevier BV. - 1755-5817 .- 1878-0016. ; 40, s. 75-101
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Research review (peer-reviewed)abstract
- To facilitate the personalized smart manufacturing paradigm with cognitive automation capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by offering an adaptive and flexible solution. DRL takes the advantages of both Deep Neural Networks (DNN) and Reinforcement Learning (RL), by embracing the power of representation learning, to make precise and fast decisions when facing dynamic and complex situations. Ever since the first paper of DRL was published in 2013, its applications have sprung up across the manufacturing field with exponential publication growth year by year. However, there still lacks any comprehensive review of the DRL in the field of smart manufacturing. To fill this gap, a systematic review process was conducted, with 261 relevant publications selected to date (20-Oct-2022), to gain a holistic understanding of the development, application, and challenges of DRL in smart manufacturing along the whole engineering lifecycle. First, the concept and development of DRL are summarized. Then, the typical DRL applications are analyzed in the four engineering lifecycle stages: design, manufacturing, dis-tribution, and maintenance. Finally, the challenges and future directions are illustrated, especially emerging DRL-related technologies and solutions that can improve the manufacturing system's deployment feasi-bility, cognitive capability, and learning efficiency, respectively. It is expected that this work can provide an insightful guide to the research of DRL in the smart manufacturing field and shed light on its future perspectives.
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| 7. |
- Li, Xingyu, et al.
(author)
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Smart Reconfigurable Manufacturing: Literature Analysis
- 2024
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In: 11th CIRP Global Web Conference, CIRPe 2023. - : Elsevier B.V.. ; , s. 43-48
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Conference paper (peer-reviewed)abstract
- Smart manufacturing (SM) enhances the competitiveness of manufacturing companies by promoting automation and overall equipment effectiveness (OEE), targeting to produce 100% qualified products fully automatically. One of the key challenges to the SM initiatives is the continuous demand fluctuations in the specification and quantity, especially when a new product variant comes to the production line. Reconfigurable manufacturing (RM) system provides cost-effective, rapid response to abrupt market changes. It provides a solution by its flexibility in repurposing tools, adding machines, and modifying software to rapidly respond to changing demands at low unit costs. The ability of SM technologies through self-programming and cloud computation may significantly complements RM initiatives. There is increasing evidence that SM and RM may augment each other through their complementary strengths, leading to the new paradigm of smart reconfigurable manufacturing (SRM). To highlight the complementary strengths, this paper investigates the converging trend of RM and SM based on natural language processing, e.g., topic modeling and semantic embedding. Key characteristics and industrial use cases are subsequently summarized to systematically delineate the new SRM paradigm and illustrate its advantages and feasibility in practice.
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| 8. |
- Mourtzis, Dimitris, et al.
(author)
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Human centric platforms for personalized value creation in metaverse
- 2022
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In: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 65, s. 653-659
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Journal article (peer-reviewed)abstract
- The term "Metaverse" first used in Neal Stephenson's sci-fi book Snow Crash in 1992, refers to a fusion of virtual and real existence. Nearly 30 years later, that definition is taking shape and promises to alter how people live and operate. This next evolution of Internet also known as Web3.0 will combine digital and physical elements. Multiple definitions can be found in the literature, with the most prevalent being the "new internet", among others such as "democratized virtual society", "persistent virtual spaces", "a digital twin of our own world for personalized value creation". Consequently, the common consensus dictates that Metaverse can be realized as a new form of the Internet, totally reshaped from what is already known. As we are heading towards the coexistence of Industry 5.0 and Society 5.0 (super smart and intelligent society), this paper attempts to present the definition of Metaverse, its evolution, the advantages and disadvantages, the pillars for the technological advancement which could be the fuel to spark future investigation and discussion as well as to accelerate the development of Metaverse towards the human centric and personalized society. Furthermore, in this manuscript, challenges and opportunities are presented (including Manufacturing), a brief comparison is performed versus Virtual Reality, and a conceptual framework for integrating Metaverse in Manufacturing is also presented.
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| 9. |
- Wang, Baicun, et al.
(author)
-
Human Digital Twin in the context of Industry 5.0
- 2024
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In: Robotics and Computer-Integrated Manufacturing. - : Elsevier Ltd. - 0736-5845 .- 1879-2537. ; 85
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Research review (peer-reviewed)abstract
- Human-centricity, a core value of Industry 5.0, places humans in the center of production. It leads to the prioritization of human needs, spanning from health and safety to self-actualization and personal growth. The concept of the Human Digital Twin (HDT) is proposed as a critical method to realize human-centricity in smart manufacturing systems towards Industry 5.0. HDTs are digital representations of humans, aiming to change the practice of human-system integration by coupling humans’ characteristics directly to the system design and its performance. In-depth analysis, critical insights, and application guidelines of HDT are essential to realize the concept of Industry 5.0 in practice and evolve the smart manufacturing paradigm in modern factories. However, the investigation on the development of HDT to evolve humans’ roles and develop humans to their full potential is limited to date. Recent studies are rarely geared towards designing a standardized framework and architecture of HDT for diverse real-world applications. Thus, this work aims to close this research gap by carrying out a comprehensive survey on HDT in the context of Industry 5.0, summarizing the ongoing evolution, and proposing a proper connotation of HDT, before discussing the conceptual framework and system architecture of HDT and analyzing enabling technologies and industrial applications. This work provides guidance on possible avenues as well as challenges for the further development of HDT and its related concepts, allowing humans to reach their potential and accommodating their diverse needs in the futuristic smart manufacturing systems shaped by Industry 5.0.
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| 10. |
- Wang, Baicun, et al.
(author)
-
Toward human-centric smart manufacturing : A human-cyber-physical systems (HCPS) perspective
- 2022
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In: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 63, s. 471-490
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Journal article (peer-reviewed)abstract
- Advances in human-centric smart manufacturing (HSM) reflect a trend towards the integration of human-in-the loop with technologies, to address challenges of human-machine relationships. In this context, the human-cyberphysical systems (HCPS), as an emerging human-centric system paradigm, can bring insights to the development and implementation of HSM. This study presents a systematic review of HCPS theories and technologies on HSM with a focus on the human-aspect is conducted. First, the concepts, key components, and taxonomy of HCPS are discussed. HCPS system framework and subsystems are analyzed. Enabling technologies (e.g., domain technologies, unit-level technologies, and system-level technologies) and core features (e.g., connectivity, integration, intelligence, adaptation, and socialization) of HCPS are presented. Applications of HCPS in smart manufacturing are illustrated with the human in the design, production, and service perspectives. This research offers key knowledge and a reference model for the human-centric design, evaluation, and implementation of HCPS-based HSM.
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