Information field in a manufacturing System: Concepts,measurements and applications |
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| Affiliation: | 1. Department of Industrial Engineering, School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China;2. School of Business and Management, Jilin University, Changchun 130022, China;3. Management School, The University of Sheffield, Conduit Road, Sheffield S10 1FL, UK;4. Jilin Provincial Key Laboratory of Designing and Planning for Future Factory, Changchun 130022, China;1. Department of Mechanical Engineering, Tianjin University, Tianjin 300350, China;2. Department of Mechanical Engineering, Tianjin Ren’ai College, Tianjin 301636, China;3. Institute of Advance Design and Manufacturing, Southwest Jiaotong University, Chengdu 610031, China;1. College of Transportation Engineering, Dalian Maritime University, 1 Linghai Rd, Dalian 116026, China;2. Collaborative Innovation Center for Transport Studies, Dalian Maritime University, 1 Linghai Rd, Dalian 116026, China;1. PHM Laboratory, Department of Mechanical Engineering, Ben-Gurion University of the Negev, P.O. Box 653, Beer Sheva 8410501, Israel;2. Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, United States;1. 5-080 NREF, University of Alberta, 9105 116 St., Edmonton, Alberta T6G 2W2, Canada;2. Department of Engineering, Durham University, South Road, Durham DH1 3LE, UK;1. School of Software Engineering, Huazhong University of Science and Technology, Hubei, PR China;2. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Hubei, PR China;1. Institute of Artificial Intelligence & Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, Hunan, China;2. School of Civil Engineering, University of Leeds, LS2 9JT Leeds, UK |
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| Abstract: | As the core of the Industry 4.0 era, information can improve the efficiency of production as well as bring cognitive load to operators. In this study, we aim to develop a unified information field analysis model for manufacturing systems and to estimate the cognitive load of operators through the action features of the information field. The qualitative and quantitative analytical framework model of the information field in manufacturing systems is established using information entropy and fuzzy mathematics. Furthermore, the cognitive load mechanism in manufacturing systems is clarified. The information principles that must be exercised to improve and optimise manufacturing systems under the information field framework are proposed for implementing lean production and digital transformation. Results of a case study show that the proposed information field analysis model for manufacturing systems reveal the change law of information field of manufacturing system in time and space, which has considerable guiding values for enhancing the management efficiency of shop floors and the sustainable development of enterprises. |
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| Keywords: | Information field Cognitive load Concept Measurement Manufacturing System |
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