| 1. |
- Guo, Junlang, et al.
(författare)
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Industrial metaverse towards Industry 5.0 : Connotation, architecture, enablers, and challenges
- 2024
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 76, s. 25-42
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Forskningsöversikt (refereegranskat)abstract
- The development of any industry cannot be done without social expectations. The industrial metaverse arises from customers' emphasis on their value, their desire for immersive experiences, and their vision for untrammeled economic transactions. This paper first introduces the definition, propositions, and metrics of the industrial metaverse towards Industry 5.0. Then, based on the understanding of physical space, cyber space, and social space, this paper proposes a five-layer framework for the industrial metaverse, which covers the perception layer, networking layer, fusion layer, interaction layer, and configuration layer. Subsequently, this paper further analyzes the key enablers and potential application scenarios of the industrial metaverse towards Industry 5.0. The technical challenges at different levels and social barriers from different perspectives are discussed. Finally, this paper highlights future research directions for the industrial metaverse towards Industry 5.0. It is expected that this framework study will provide researchers with an overview of the industrial metaverse and a deeper understanding of its development potential and obstacles.
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| 2. |
- Leng, Jiewu, et al.
(författare)
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Review of manufacturing system design in the interplay of Industry 4.0 and Industry 5.0 (Part I): Design thinking and modeling methods
- 2024
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 76, s. 158-187
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Forskningsöversikt (refereegranskat)abstract
- The concurrent societal goals and environmental challenges force industries to become more human-centric, sustainable, and resilient, which is envisioned as a value-oriented Industry 5.0 paradigm. The technology-driven Industry 4.0 paradigm is in a prosperous stage. Advanced information and manufacturing technologies have shown promising benefits, but challenges persist in the integration and implementation of smart manufacturing systems. Manufacturing system design (MSD) not only affects production efficiency and product quality but also is related to the strategic planning and sustainable development of manufacturers. New requirements for MSD appear and MSD has revealed new trends. MSD in the interplay of Industry 4.0 and Industry 5.0 is discussed in this two-part review, based on literature exploration within the Web of Science database. To obtain a systematic understanding of methodological, procedural, and technological exploration in a balanced way, this two-part review builds a Thinking-Modeling-Process-Enabler (TMPE) framework for reviewing MSD. In this paper (Part I of the two-part review), MSD methods are summarized and categorized from the design thinking viewpoint. Then, the manufacturing system modeling methods are categorized and discussed. Challenges and future research directions are identified in the MSD evolution towards Industry 5.0. Part II will detail the design processes and enablers (i.e., the P and E dimensions of the TMPE framework). This two-part review is anticipated to offer novel insights for advancing MSD research and engineering in the interplay of Industry 4.0 and Industry 5.0.
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| 3. |
- Leng, Jiewu, et al.
(författare)
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Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges
- 2024
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 73, s. 349-363
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Forskningsöversikt (refereegranskat)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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| 4. |
- Wang, Jinxin, et al.
(författare)
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Machining Properties of Stone-Plastic Composite Based on an Empirically Validated Finite Element Method
- 2023
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Ingår i: Advanced Engineering Materials. - : John Wiley & Sons. - 1438-1656 .- 1527-2648. ; 25:8
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Tidskriftsartikel (refereegranskat)abstract
- High-cutting performance is an essential metric for improving the suitability of materials for industrial applications. Herein, the machining properties of stone-plastic composite are assessed through a finite element method to explore orthogonal cutting behavior by diamond cutters. The key aspects examined in this work are the effects of tool geometry and cutting parameters on the cutting force, temperature, chip formation, von Mises stress, and surface quality finish. Primary findings show that chip continuity increases proportionally with increase in rake angle but decreases with cutting speed and depth. Meanwhile, both cutting stability and surface quality are negatively correlated with cutting speed and depth but positively correlated with rake angle. These results support the adoption of cutting conditions using greater rake angle, higher cutting speed, and shallower cutting depth to obtain higher cutting performance, that is, greater cutting stability and surface quality in the finishing machining of stone-plastic composites.
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| 5. |
- Zhu, Zhaolong, et al.
(författare)
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Enhancing face-milling efficiency of wood–plastic composites through the application of genetic algorithm–back propagation neural network
- 2024
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Ingår i: Wood Material Science & Engineering. - 1748-0272 .- 1748-0280.
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Tidskriftsartikel (refereegranskat)abstract
- Wood–plastic composites (WPCs) have advanced physicochemical properties and are widely applied in various fields. How to achieve high-efficiency, high-quality machining of WPC is a pressing issue that WPC manufacturing enterprises need to address. To this end, this research focused primarily on improving the machinability of WPC with end milling experiments. In this work, a single-factor method was used to analyse the impact of axial milling depth, spindle speed, and tool rake angle on resultant forces and surface roughness. Main effects analysis was applied to explore the degree of influence of axial milling depth, spindle speed, and tool rake angle on cutting forces and surface roughness. Furthermore, three-dimensional characterisation techniques were utilised to analyse the surface morphology characteristics of WPC during end milling. Finally, a genetic algorithm–back propagation neural network was applied to develop prediction models for resultant force and surface roughness, and optimal milling conditions were identified as axial milling depth of 0.50 mm, spindle speed of 7841 r/min, and rake angle of 15°; these produced the lowest resultant force and surface roughness. The findings of this work are proposed as a guide for better cutting performance in the industrial production of WPC.
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