Predictive maintenance of cutting tools using artificial neural networks
In the manufacturing industry, preventative maintenance of cutting tools plays a critical role in ensuring operational efficiency and minimizing downtime. This paper addresses the problem of accurately predicting the wear level of a cutting tool by applying artificial neural networks. The study uses...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
|
| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/01/e3sconf_titds2023_02021.pdf |
| _version_ | 1797343659681644544 |
|---|---|
| author | Karimova Nazokat Ochilov Ulugbek Yakhshiev Sherali Egamberdiev Ilhom |
| author_facet | Karimova Nazokat Ochilov Ulugbek Yakhshiev Sherali Egamberdiev Ilhom |
| author_sort | Karimova Nazokat |
| collection | DOAJ |
| description | In the manufacturing industry, preventative maintenance of cutting tools plays a critical role in ensuring operational efficiency and minimizing downtime. This paper addresses the problem of accurately predicting the wear level of a cutting tool by applying artificial neural networks. The study uses an extensive dataset derived from real-life manufacturing scenarios and uses synthetic and experimental data for illustrative purposes. The use of depth of cut, continuous vibration monitoring using an accelerometer, spindle speed, feed rate and cutting speed contribute to a holistic approach to predicting tool wear in milling processes. This comprehensive set of features is designed to capture the nuanced interactions between machining conditions and tool degradation, thereby improving the model’s predictive accuracy. The architecture, features and algorithm for training the network are described. A neural network has been created and configured to determine tool wear during the milling process. The process of training and debugging a neural network is clearly displayed on the graphs. The performance of the network was tested using test data, which allowed us to obtain good results. |
| first_indexed | 2024-03-08T10:50:59Z |
| format | Article |
| id | doaj.art-11f1f5863e8845a8b4cc588632a24567 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T10:50:59Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-11f1f5863e8845a8b4cc588632a245672024-01-26T16:46:41ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014710202110.1051/e3sconf/202447102021e3sconf_titds2023_02021Predictive maintenance of cutting tools using artificial neural networksKarimova Nazokat0Ochilov Ulugbek1Yakhshiev Sherali2Egamberdiev Ilhom3Tashkent State Technical University named after Islam Karimov, Mechanical Engineering departmentNavoi State University of Mining and Technology, Mechanical Engineering departmentNavoi State University of Mining and Technology, Mechanical Engineering departmentNavoi State University of Mining and Technology, Mechanical Engineering departmentIn the manufacturing industry, preventative maintenance of cutting tools plays a critical role in ensuring operational efficiency and minimizing downtime. This paper addresses the problem of accurately predicting the wear level of a cutting tool by applying artificial neural networks. The study uses an extensive dataset derived from real-life manufacturing scenarios and uses synthetic and experimental data for illustrative purposes. The use of depth of cut, continuous vibration monitoring using an accelerometer, spindle speed, feed rate and cutting speed contribute to a holistic approach to predicting tool wear in milling processes. This comprehensive set of features is designed to capture the nuanced interactions between machining conditions and tool degradation, thereby improving the model’s predictive accuracy. The architecture, features and algorithm for training the network are described. A neural network has been created and configured to determine tool wear during the milling process. The process of training and debugging a neural network is clearly displayed on the graphs. The performance of the network was tested using test data, which allowed us to obtain good results.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/01/e3sconf_titds2023_02021.pdf |
| spellingShingle | Karimova Nazokat Ochilov Ulugbek Yakhshiev Sherali Egamberdiev Ilhom Predictive maintenance of cutting tools using artificial neural networks E3S Web of Conferences |
| title | Predictive maintenance of cutting tools using artificial neural networks |
| title_full | Predictive maintenance of cutting tools using artificial neural networks |
| title_fullStr | Predictive maintenance of cutting tools using artificial neural networks |
| title_full_unstemmed | Predictive maintenance of cutting tools using artificial neural networks |
| title_short | Predictive maintenance of cutting tools using artificial neural networks |
| title_sort | predictive maintenance of cutting tools using artificial neural networks |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/01/e3sconf_titds2023_02021.pdf |
| work_keys_str_mv | AT karimovanazokat predictivemaintenanceofcuttingtoolsusingartificialneuralnetworks AT ochilovulugbek predictivemaintenanceofcuttingtoolsusingartificialneuralnetworks AT yakhshievsherali predictivemaintenanceofcuttingtoolsusingartificialneuralnetworks AT egamberdievilhom predictivemaintenanceofcuttingtoolsusingartificialneuralnetworks |