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Abstract
With the rapid development of new-generation information technologies such as big data, cloud computing, Internet of Things, and mobile internet in traditional manufacturing, the development of intelligent manufacturing (IM) is accelerating. Digital twin is an important method to achieve the goal of IM, and provides an effective means for the integrated development of design and manufacturing (R & M). In view of the problems of long installation and debugging cycles, and process parameters requiring multiple trial and error in the research and development (R & D) process of laser melting deposition (LMD) equipment, this paper focuses on building an LMD equipment model based on digital twin technology. It involves performing virtual assembly, motion setting, collision inspection, and PLC debugging, thereby providing an innovative method and insights for improving the R & D efficiency of the IM of LMD equipment.
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Research on Task-Service Network Node Matching Method Based on Multi-Objective Optimization Model in Dynamic Hyper-Network Environment. MICROMACHINES 2021; 12:mi12111427. [PMID: 34832838 PMCID: PMC8624609 DOI: 10.3390/mi12111427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 11/11/2021] [Accepted: 11/15/2021] [Indexed: 11/17/2022]
Abstract
In order to reduce the cost of manufacturing and service for the Cloud 3D printing (C3DP) manufacturing grid, to solve the problem of resources optimization deployment for no-need preference under circumstance of cloud manufacturing, consider the interests of enterprises which need Cloud 3D printing resources and cloud platform operators, together with QoS and flexibility of both sides in the process of Cloud 3D printing resources configuration service, a task-service network node matching method based on Multi-Objective optimization model in dynamic hyper-network environment is built for resource allocation. This model represents interests of the above-mentioned two parties. In addition, the model examples are solved by modifying Mathematical algorithm of Node Matching and Evolutionary Solutions. Results prove that the model and the algorithm are feasible, effective and stable.
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Zhang C, Zhang C, Zhuang J, Han H, Yuan B, Liu J, Yang K, Zhuang S, Li R. Evaluation of Cloud 3D Printing Order Task Execution Based on the AHP-TOPSIS Optimal Set Algorithm and the Baldwin Effect. MICROMACHINES 2021; 12:mi12070801. [PMID: 34357211 PMCID: PMC8305594 DOI: 10.3390/mi12070801] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/07/2021] [Accepted: 06/28/2021] [Indexed: 11/17/2022]
Abstract
Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments, and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents’ dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.
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Affiliation(s)
- Chenglei Zhang
- School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China;
- School of Mechanical & Vehicle Engineering, Linyi University, Linyi 276000, China; (J.Z.); (H.H.)
- Shandong Longli Electronic Co., Ltd., Linyi 276000, China; (S.Z.); (R.L.)
- Correspondence: (C.Z.); (J.L.)
| | - Cunshan Zhang
- School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China;
| | - Jiaojiao Zhuang
- School of Mechanical & Vehicle Engineering, Linyi University, Linyi 276000, China; (J.Z.); (H.H.)
| | - Hu Han
- School of Mechanical & Vehicle Engineering, Linyi University, Linyi 276000, China; (J.Z.); (H.H.)
| | - Bo Yuan
- School of Mechanical and Electrical Engineering, Wuhan City Polytechnic, Wuhan 430064, China;
| | - Jiajia Liu
- School of Mechanical & Vehicle Engineering, Linyi University, Linyi 276000, China; (J.Z.); (H.H.)
- Correspondence: (C.Z.); (J.L.)
| | - Kang Yang
- College of Mechanical Engineering, Anyang Institute of Technology, Anyang 455000, China;
| | - Shenle Zhuang
- Shandong Longli Electronic Co., Ltd., Linyi 276000, China; (S.Z.); (R.L.)
| | - Ronglan Li
- Shandong Longli Electronic Co., Ltd., Linyi 276000, China; (S.Z.); (R.L.)
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Cheng Y, Tao F, Xu L, Zhao D. Advanced manufacturing systems: supply–demand matching of manufacturing resource based on complex networks and Internet of Things. ENTERP INF SYST-UK 2016. [DOI: 10.1080/17517575.2016.1183263] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Ying Cheng
- School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, P. R. China
- Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI, USA
| | - Fei Tao
- School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, P. R. China
| | - Lida Xu
- Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk, VA, USA
| | - Dongming Zhao
- Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI, USA
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Zhang W, Zhang S, Guo S. A PageRank-based reputation model for personalised manufacturing service recommendation. ENTERP INF SYST-UK 2015. [DOI: 10.1080/17517575.2015.1077998] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Appl Soft Comput 2014. [DOI: 10.1016/j.asoc.2014.01.036] [Citation(s) in RCA: 127] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Li L, Zhang L, Willamowska-Korsak M. The effects of collaboration on build-to-order supply chains: with a comparison of BTO, MTO, and MTS. INFORMATION TECHNOLOGY & MANAGEMENT 2014. [DOI: 10.1007/s10799-014-0179-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Ai Q, Shu T, Liu Q, Zhou Z, Xiao Z. A method for determining customer requirement weights based on TFMF and TLR. ENTERP INF SYST-UK 2013. [DOI: 10.1080/17517575.2012.763190] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Li Q, Wang ZY, Li WH, Li J, Wang C, Du RY. Applications integration in a hybrid cloud computing environment: modelling and platform. ENTERP INF SYST-UK 2013. [DOI: 10.1080/17517575.2012.677479] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Huang B, Li C, Tao F. A chaos control optimal algorithm for QoS-based service composition selection in cloud manufacturing system. ENTERP INF SYST-UK 2013. [DOI: 10.1080/17517575.2013.792396] [Citation(s) in RCA: 85] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Teran H, Hernandez JC, Vizán A, Ríos J. Performance measurement integrated information framework in e-Manufacturing. ENTERP INF SYST-UK 2013. [DOI: 10.1080/17517575.2012.754950] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Zhang W, Zhang S, Cai M, Jian W. An agent-based peer-to-peer architecture for semantic discovery of manufacturing services across virtual enterprises. ENTERP INF SYST-UK 2012. [DOI: 10.1080/17517575.2012.747002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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An analysis on the macroscopic growth process and stage of information systems development in Chinese enterprises. INFORMATION TECHNOLOGY & MANAGEMENT 2012. [DOI: 10.1007/s10799-012-0115-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Wang L, Zeng J, Xu L. A decision support system for substage-zoning filling design of rock-fill dams based on particle swarm optimization. INFORMATION TECHNOLOGY & MANAGEMENT 2011. [DOI: 10.1007/s10799-011-0092-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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