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Predictive modeling and optimization of dry turning of hardened steel
https://orcid.org/http://orcid.org/0000-0003-2552-1518
High-hardness material (45–65 HRC) is turned into the desired shape using a single-point cutting tool during the dry turning process.The purpose of this study is to correlate tool-induced vibration with progressive tool wear. Experiments are executed according to a Central Composite Rotatable Design. The results of the experiment showed that vibration response can be used to relate tool wear to its various stages, including early breakdown, gradual wear, and abrupt wear rate. A new model based on real-time tool acceleration is suggested to assess tool wear. The adequacy of the model is verified with the help of residual values that closely resemble the straight line. The R-squared value of the model is observed to be 0.93, which proves that the model is reliable and helpful for wear prediction. In addition, an Artificial Neural Network (ANN) examination is exhibited. The ANN model’s regression value for tool wear is found in close agreement with the proposed model. This study helps to develop the method of real-time process monitoring which is the planned future work.
Predictive modeling and optimization of dry turning of hardened steel
https://orcid.org/http://orcid.org/0000-0003-2552-1518
High-hardness material (45–65 HRC) is turned into the desired shape using a single-point cutting tool during the dry turning process.The purpose of this study is to correlate tool-induced vibration with progressive tool wear. Experiments are executed according to a Central Composite Rotatable Design. The results of the experiment showed that vibration response can be used to relate tool wear to its various stages, including early breakdown, gradual wear, and abrupt wear rate. A new model based on real-time tool acceleration is suggested to assess tool wear. The adequacy of the model is verified with the help of residual values that closely resemble the straight line. The R-squared value of the model is observed to be 0.93, which proves that the model is reliable and helpful for wear prediction. In addition, an Artificial Neural Network (ANN) examination is exhibited. The ANN model’s regression value for tool wear is found in close agreement with the proposed model. This study helps to develop the method of real-time process monitoring which is the planned future work.
Predictive modeling and optimization of dry turning of hardened steel
https://orcid.org/http://orcid.org/0000-0003-2552-1518
High-hardness material (45–65 HRC) is turned into the desired shape using a single-point cutting tool during the dry turning process.The purpose of this study is to correlate tool-induced vibration with progressive tool wear. Experiments are executed according to a Central Composite Rotatable Design. The results of the experiment showed that vibration response can be used to relate tool wear to its various stages, including early breakdown, gradual wear, and abrupt wear rate. A new model based on real-time tool acceleration is suggested to assess tool wear. The adequacy of the model is verified with the help of residual values that closely resemble the straight line. The R-squared value of the model is observed to be 0.93, which proves that the model is reliable and helpful for wear prediction. In addition, an Artificial Neural Network (ANN) examination is exhibited. The ANN model’s regression value for tool wear is found in close agreement with the proposed model. This study helps to develop the method of real-time process monitoring which is the planned future work.
Predictive modeling and optimization of dry turning of hardened steel
Int J Interact Des Manuf
Ambhore, Nitin (author) / Gaikwad, Mahendra (author) / Patil, Amit (author) / Sharma, Yogita (author) / Manikjade, Akshay (author)
2024-10-01
7 pages
Article (Journal)
Electronic Resource
English
Predictive modeling and optimization of dry turning of hardened steel
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