Fuzzy Evaluation and Improvement Decision-Making Model for Machining Operation Performance
Taiwan is a major exporter and producer of machinery and machine tools in the world. There are at least hundreds of components for various machining machines. According to the concept of Taguchi loss function, when the process quality of the spare parts of machining machines is not good, the failure...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/3/1430 |
| _version_ | 1797625219450404864 |
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| author | Kuen-Suan Chen Chih-Feng Wu Ruey-Chyn Tsaur Tsun-Hung Huang |
| author_facet | Kuen-Suan Chen Chih-Feng Wu Ruey-Chyn Tsaur Tsun-Hung Huang |
| author_sort | Kuen-Suan Chen |
| collection | DOAJ |
| description | Taiwan is a major exporter and producer of machinery and machine tools in the world. There are at least hundreds of components for various machining machines. According to the concept of Taguchi loss function, when the process quality of the spare parts of machining machines is not good, the failure rate will increase after the product is sold, resulting in an increase in maintenance costs and carbon emissions. As the environment of the Internet of Things (IoT) becomes more common and mature, it is beneficial for manufacturers of machining machines to collect relevant information about process data from outsourcers, suppliers, and machining machine factories. Effective data analysis and application can help the machining machine industry move towards smart manufacturing and management, which can greatly reduce the average number of failures per unit time for all sold machines. Therefore, this paper developed a practical evaluation and improvement decision-making model for the machining operation performance to help machining machine manufacturers find out the components that often fail and improve them, so as to reduce the total loss caused by machine failures. This paper first defined the machining operation performance index for the machining machines and discussed the characteristics of this operation performance index. Subsequently, the confidence interval of the index was deduced, a fuzzy evaluation model based on this confidence interval was proposed, and decision-making rules regarding whether to make any improvement was established. The fuzzy evaluation and improvement decision-making model for the operation performance of machining machines proposed in this paper will contribute to various tool industries to boost their process quality, reduce costs, and lower carbon emissions, in order to achieve sustainable management of enterprises and the environment. |
| first_indexed | 2024-03-11T09:53:28Z |
| format | Article |
| id | doaj.art-dfe8148779834ac7936ef83496e8399f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:53:28Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-dfe8148779834ac7936ef83496e8399f2023-11-16T16:05:20ZengMDPI AGApplied Sciences2076-34172023-01-01133143010.3390/app13031430Fuzzy Evaluation and Improvement Decision-Making Model for Machining Operation PerformanceKuen-Suan Chen0Chih-Feng Wu1Ruey-Chyn Tsaur2Tsun-Hung Huang3Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, TaiwanDepartment of Digital Content Application and Management, Wenzao Ursuline University of Languages, Kaohsiung 807, TaiwanDepartment of Management Sciences, Tamkang University, New Taipei 25137, TaiwanDepartment of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, TaiwanTaiwan is a major exporter and producer of machinery and machine tools in the world. There are at least hundreds of components for various machining machines. According to the concept of Taguchi loss function, when the process quality of the spare parts of machining machines is not good, the failure rate will increase after the product is sold, resulting in an increase in maintenance costs and carbon emissions. As the environment of the Internet of Things (IoT) becomes more common and mature, it is beneficial for manufacturers of machining machines to collect relevant information about process data from outsourcers, suppliers, and machining machine factories. Effective data analysis and application can help the machining machine industry move towards smart manufacturing and management, which can greatly reduce the average number of failures per unit time for all sold machines. Therefore, this paper developed a practical evaluation and improvement decision-making model for the machining operation performance to help machining machine manufacturers find out the components that often fail and improve them, so as to reduce the total loss caused by machine failures. This paper first defined the machining operation performance index for the machining machines and discussed the characteristics of this operation performance index. Subsequently, the confidence interval of the index was deduced, a fuzzy evaluation model based on this confidence interval was proposed, and decision-making rules regarding whether to make any improvement was established. The fuzzy evaluation and improvement decision-making model for the operation performance of machining machines proposed in this paper will contribute to various tool industries to boost their process quality, reduce costs, and lower carbon emissions, in order to achieve sustainable management of enterprises and the environment.https://www.mdpi.com/2076-3417/13/3/1430operation performance indexconfidence intervalmembership functionfuzzy evaluation and improvement decision-making |
| spellingShingle | Kuen-Suan Chen Chih-Feng Wu Ruey-Chyn Tsaur Tsun-Hung Huang Fuzzy Evaluation and Improvement Decision-Making Model for Machining Operation Performance Applied Sciences operation performance index confidence interval membership function fuzzy evaluation and improvement decision-making |
| title | Fuzzy Evaluation and Improvement Decision-Making Model for Machining Operation Performance |
| title_full | Fuzzy Evaluation and Improvement Decision-Making Model for Machining Operation Performance |
| title_fullStr | Fuzzy Evaluation and Improvement Decision-Making Model for Machining Operation Performance |
| title_full_unstemmed | Fuzzy Evaluation and Improvement Decision-Making Model for Machining Operation Performance |
| title_short | Fuzzy Evaluation and Improvement Decision-Making Model for Machining Operation Performance |
| title_sort | fuzzy evaluation and improvement decision making model for machining operation performance |
| topic | operation performance index confidence interval membership function fuzzy evaluation and improvement decision-making |
| url | https://www.mdpi.com/2076-3417/13/3/1430 |
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