Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing
This article presents a discussion of the application of machine learning methods to enhance the quality of drive shaft production, with a particular focus on the identification of critical quality issues, including cracks, scratches, and dimensional deviations, which have been observed in the final...
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| author | Katarzyna Antosz Lucia Knapčíková Jozef Husár |
| author_facet | Katarzyna Antosz Lucia Knapčíková Jozef Husár |
| author_sort | Katarzyna Antosz |
| collection | DOAJ |
| description | This article presents a discussion of the application of machine learning methods to enhance the quality of drive shaft production, with a particular focus on the identification of critical quality issues, including cracks, scratches, and dimensional deviations, which have been observed in the final stages of machining. A variety of classification algorithms, including neural networks (NNs), bagged trees (BT), and support vector machines (SVMs), were employed to efficiently analyse and predict defects. The results show that neural networks achieved the highest accuracy (94.7%) and the fastest prediction time, thereby underscoring their efficiency in processing complex production data. The BT model demonstrated stability in its predictions with a slower prediction time, while the SVM model exhibited superior training speed, though with slightly lower accuracy. This article proposes that optimising key process parameters, such as temperature, machining speed, and the type of coolant used, can markedly reduce the prevalence of production defects. It also recommends integrating machine learning with traditional quality management techniques to create a more flexible and adaptive control system, which could help reduce production losses and enhance customer satisfaction. |
| format | Article |
| id | doaj-art-5afa39b26c654da687fa64ee9ffa9832 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5afa39b26c654da687fa64ee9ffa98322025-08-20T02:26:45ZengMDPI AGApplied Sciences2076-34172024-11-0114221045010.3390/app142210450Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product ManufacturingKatarzyna Antosz0Lucia Knapčíková1Jozef Husár2Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, Powstańców Warszawy 8, 35-959 Rzeszów, PolandDepartment of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies, Technical University of Košice, Bayerova, 1, 08001 Prešov, SlovakiaDepartment of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies, Technical University of Košice, Bayerova, 1, 08001 Prešov, SlovakiaThis article presents a discussion of the application of machine learning methods to enhance the quality of drive shaft production, with a particular focus on the identification of critical quality issues, including cracks, scratches, and dimensional deviations, which have been observed in the final stages of machining. A variety of classification algorithms, including neural networks (NNs), bagged trees (BT), and support vector machines (SVMs), were employed to efficiently analyse and predict defects. The results show that neural networks achieved the highest accuracy (94.7%) and the fastest prediction time, thereby underscoring their efficiency in processing complex production data. The BT model demonstrated stability in its predictions with a slower prediction time, while the SVM model exhibited superior training speed, though with slightly lower accuracy. This article proposes that optimising key process parameters, such as temperature, machining speed, and the type of coolant used, can markedly reduce the prevalence of production defects. It also recommends integrating machine learning with traditional quality management techniques to create a more flexible and adaptive control system, which could help reduce production losses and enhance customer satisfaction.https://www.mdpi.com/2076-3417/14/22/10450machine learningSVMbagged treesNN modelquality managementmetal manufacturing |
| spellingShingle | Katarzyna Antosz Lucia Knapčíková Jozef Husár Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing Applied Sciences machine learning SVM bagged trees NN model quality management metal manufacturing |
| title | Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing |
| title_full | Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing |
| title_fullStr | Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing |
| title_full_unstemmed | Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing |
| title_short | Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing |
| title_sort | evaluation and application of machine learning techniques for quality improvement in metal product manufacturing |
| topic | machine learning SVM bagged trees NN model quality management metal manufacturing |
| url | https://www.mdpi.com/2076-3417/14/22/10450 |
| work_keys_str_mv | AT katarzynaantosz evaluationandapplicationofmachinelearningtechniquesforqualityimprovementinmetalproductmanufacturing AT luciaknapcikova evaluationandapplicationofmachinelearningtechniquesforqualityimprovementinmetalproductmanufacturing AT jozefhusar evaluationandapplicationofmachinelearningtechniquesforqualityimprovementinmetalproductmanufacturing |