Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques
Milling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. Inserts for compound cutting tools can reduce the cost of operations by optimizing their lifespan. This study analyzes the flank wear of cutting tools in milling machines, with an empha...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/24/11840 |
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| author | Sonia Val María Pilar Lambán Javier Lucia Jesús Royo |
| author_facet | Sonia Val María Pilar Lambán Javier Lucia Jesús Royo |
| author_sort | Sonia Val |
| collection | DOAJ |
| description | Milling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. Inserts for compound cutting tools can reduce the cost of operations by optimizing their lifespan. This study analyzes the flank wear of cutting tools in milling machines, with an emphasis on evaluating different approaches to predict their lifespan. It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. Each model is evaluated independently, and this comparative analysis aims to determine which modeling strategy best captures the intricate interactions between various process variables affecting tool wear. This method ensures greater efficiency and accuracy than conventional techniques, providing a scalable, resource-efficient solution for reliable and insightful tool wear predictions. The results obtained from the dataset of an insert tool can be extrapolated to other milling inserts and demonstrate the progression of tool wear over time under varying cutting parameters, providing critical insights for optimizing milling operations. The integration of uncertainty awareness in the predictive outputs is a unique feature of this research and enhances decision-making for smarter manufacturing. This proactive approach enhances operational efficiency and reduces overall production costs. Furthermore, the data-driven, AI-centric methodology developed in this study offers a transferable approach that can be adapted to other machining processes, advancing state-of-the-art tool wear prediction. |
| format | Article |
| id | doaj-art-62a6b348fd574536a6e372c8bc0e0d4f |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-62a6b348fd574536a6e372c8bc0e0d4f2024-12-27T14:08:31ZengMDPI AGApplied Sciences2076-34172024-12-0114241184010.3390/app142411840Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence TechniquesSonia Val0María Pilar Lambán1Javier Lucia2Jesús Royo3Department of Design and Manufacturing Engineering, University of Zaragoza, 50009 Zaragoza, SpainDepartment of Design and Manufacturing Engineering, University of Zaragoza, 50009 Zaragoza, SpainDepartment of Design and Manufacturing Engineering, University of Zaragoza, 50009 Zaragoza, SpainDepartment of Design and Manufacturing Engineering, University of Zaragoza, 50009 Zaragoza, SpainMilling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. Inserts for compound cutting tools can reduce the cost of operations by optimizing their lifespan. This study analyzes the flank wear of cutting tools in milling machines, with an emphasis on evaluating different approaches to predict their lifespan. It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. Each model is evaluated independently, and this comparative analysis aims to determine which modeling strategy best captures the intricate interactions between various process variables affecting tool wear. This method ensures greater efficiency and accuracy than conventional techniques, providing a scalable, resource-efficient solution for reliable and insightful tool wear predictions. The results obtained from the dataset of an insert tool can be extrapolated to other milling inserts and demonstrate the progression of tool wear over time under varying cutting parameters, providing critical insights for optimizing milling operations. The integration of uncertainty awareness in the predictive outputs is a unique feature of this research and enhances decision-making for smarter manufacturing. This proactive approach enhances operational efficiency and reduces overall production costs. Furthermore, the data-driven, AI-centric methodology developed in this study offers a transferable approach that can be adapted to other machining processes, advancing state-of-the-art tool wear prediction.https://www.mdpi.com/2076-3417/14/24/11840artificial intelligencecutting toolsmachine learningmilling machinespredictive maintenancetool wear |
| spellingShingle | Sonia Val María Pilar Lambán Javier Lucia Jesús Royo Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques Applied Sciences artificial intelligence cutting tools machine learning milling machines predictive maintenance tool wear |
| title | Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques |
| title_full | Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques |
| title_fullStr | Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques |
| title_full_unstemmed | Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques |
| title_short | Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques |
| title_sort | analysis and prediction of wear in interchangeable milling insert tools using artificial intelligence techniques |
| topic | artificial intelligence cutting tools machine learning milling machines predictive maintenance tool wear |
| url | https://www.mdpi.com/2076-3417/14/24/11840 |
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