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Study on the Key Drivers to Improve the Circular Economy of Manufacturing Industry - An Application of Fuzzy Delphi Method & Grey Relational Analysis

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International Journal of Information and Management Sciences 2023年第2期34卷 117-133页

作者： Chang, An-Yuan Lai, Po-Yen Department of Industrial Management National Formosa University Taiwan Doctoral Program in Smart Industry Technology Research and Design at National Formosa University Taiwan

In order to implement Circular Economy (CE) smoothly, it is necessary to understand what kind of support the manufacturing industry needs on the road to CE, and to find out the technical indicators that can improve CE performance. This use the Multiple Attribute Decision Making (MADM) method to fill in the gaps in this research field. Through the distribution of questionnaires, experts are invited to provide data with reference to their professional experience. After data integration, the Fuzzy Delphi Method (FDM) method is used to screen out important driving factors and 9R indicators. Then, Grey Relational Analysis (GRA) is used to get important and key driving factors. According to the FDM results of this study, the top 9R indicators are Reduce, Reuse, and Rethink. As for the GRA results, the top-ranking driving factors are the support of the parent company (headquarters), the promotion of the company’s reputation and energy saving. © 2023, Tamkang University. All rights reserved.

关键词： Data integration

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Promoting the Application of Lean Automation - Take the Automobile Oil Seal Manufacturing Industry as an Example 4

Promoting the Application of Lean Automation - Take the Auto...

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4th IEEE Eurasia Conference on IoT, Communication and Engineering, ECICE 2022

作者： Chang, An-Yuan Lai, Po-Yen National Formosa University Department of Industrial Management Yunlin Taiwan National Formosa University Department of Smart Industry Technology R&d Doctoral Program Yunlin Taiwan

ISBN: (纸本)9781665482080

With increasingly sophisticated global manufacturing technology and fierce competition in the international market, the automobile manufacturing industry has entered the stage of internationalization. Therefore, it is necessary to innovate and change, improve production technology continuously, strengthen physical fitness, and maintain competitiveness to survive in the rapidly changing market. Lean Management (LM) and automation equipment are important methods to achieve the above goals. Among them, the use of Automated Guided Vehicles (AGV) has become a crucial equipment in the industry that many manufacturers have widely used. AGVs are unmanned vehicles that can travel according to a program or a predetermined path. AGVs can replace the original logistics, shortens the handling time greatly, and enhances the competitiveness of *** research focuses on the introduction of LM in an automobile oil seal manufacturing industry in Taiwan and focuses on improving logistics efficiency. It uses Value Stream Mapping (VSM) to discuss the logistics problems existing on the production line and proposes specific improvement plans. This study aims to improve logistics efficiency using AGVs and logistics improvement based on its cooperation with Jidoka. This study proposes a new route transportation method, which uses multiple AGVs to perform transportation tasks at the same time, combined with Karakuri's base-running path transportation mode of multi-functional *** results include (1) proposing a new route transportation method, AGVs combined with Karakuri's running route transportation mode;(2) saving two full-time logistics manpower;and (3) increasing the Cycle Time (CT) to within 5 minutes. © 2022 IEEE.

关键词： Mapping

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Application of Recurrence Plots and VGG Deep Learning Model to the Study of Condition Monitoring of Robotic Grinding

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International Journal of Precision Engineering and Manufacturing 2023年第9期24卷 1675-1683页

作者： Chung, Kuan-Jung Dai, Cheng-Han Chiang, Tung-Chun Xie, June-Jia Lin, Ming-Tzer Department of Mechatronics Engineering National Changhua University of Education Changhua500 Taiwan Graduate Institute of Precision Engineering National Chung Hsing University Taichung402 Taiwan Doctoral Program in Industrial and Smart Technology Academy of Circular Economy National Chung Hsing University Nantou540 Taiwan

This study adopted AI to identify the normal and abnormal vibration signals generated during robotic grinding. This study classified four fundamental factors affecting grinding into three levels to obtain a widely used result and designed an L9(3 4) orthogonal array for the grinding experiment. During experimentation, part of the grinding wheels was added weight to produce abnormal vibration signals, which an accelerometer would measure. The study transformed the collected vibration signals into recurrence plots and conducted model training with VGG16 CNN architecture. Finally, this study tested a model with 89.6% training accuracy. The results showed the model could identify whether the recurrence plots stand for normal or abnormal vibrations, with an accuracy of 85%. This means it could predict normal and abnormal grinding conditions and help avoid problems caused by abnormal vibrations. © 2023, The Author(s), under exclusive licence to Korean Society for Precision Engineering.

关键词： Grinding (machining)

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Promoting the Application of Lean Automation - Take the Automobile Oil Seal Manufacturing Industry as an Example

Promoting the Application of Lean Automation - Take the Auto...

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IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)

作者： An-Yuan Chang Po-Yen Lai Department of Industrial Management National Formosa University Yunlin Taiwan Department of Smart Industry Technology R&D Doctoral Program National Formosa University Yunlin Taiwan

ISBN: (纸本)9781665482097

With increasingly sophisticated global manufacturing technology and fierce competition in the international market, the automobile manufacturing industry has entered the stage of internationalization. Therefore, it is necessary to innovate and change, improve production technology continuously, strengthen physical fitness, and maintain competitiveness to survive in the rapidly changing market. Lean Management (LM) and automation equipment are important methods to achieve the above goals. Among them, the use of Automated Guided Vehicles (AGV) has become a crucial equipment in the industry that many manufacturers have widely used. AGVs are unmanned vehicles that can travel according to a program or a predetermined path. AGVs can replace the original logistics, shortens the handling time greatly, and enhances the competitiveness of *** research focuses on the introduction of LM in an automobile oil seal manufacturing industry in Taiwan and focuses on improving logistics efficiency. It uses Value Stream Mapping (VSM) to discuss the logistics problems existing on the production line and proposes specific improvement plans. This study aims to improve logistics efficiency using AGVs and logistics improvement based on its cooperation with Jidoka. This study proposes a new route transportation method, which uses multiple AGVs to perform transportation tasks at the same time, combined with Karakuri’s base-running path transportation mode of multi-functional *** results include (1) proposing a new route transportation method, AGVs combined with Karakuri’s running route transportation mode; (2) saving two full-time logistics manpower; and (3) increasing the Cycle Time (CT) to within 5 minutes.

关键词： Manufacturing industries Automation Remotely guided vehicles Oils Transportation Seals Production

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