A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263
In this present paper, the machine learning approach is used to optimize, model, and predict the factors during drilling Nimonic C263 under dry mode. Nimonic C263 is tough to machine aero alloys, and it is required to find a predictive model and to optimize the factors in drilling this alloy before...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/4856089 |
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| author | S. Lakshmana Kumar V. Jacintha A. Mahendran R. M. Bommi M. Nagaraj Umamahesawari Kandasamy |
| author_facet | S. Lakshmana Kumar V. Jacintha A. Mahendran R. M. Bommi M. Nagaraj Umamahesawari Kandasamy |
| author_sort | S. Lakshmana Kumar |
| collection | DOAJ |
| description | In this present paper, the machine learning approach is used to optimize, model, and predict the factors during drilling Nimonic C263 under dry mode. Nimonic C263 is tough to machine aero alloys, and it is required to find a predictive model and to optimize the factors in drilling this alloy before the actual machining process. It helps to avoid the actual machining cost and material cost. Experimental trails are planned based on Taguchi analysis, and L27 orthogonal array was chosen. Speed, feed, and approach angle of drill were considered as controlling factors, and cutting force and surface roughness were considered as responses. The feed forward neural network (FFNN) was used to develop a predictive model. The prediction capability was validated with a predictive model developed by Taguchi analysis. Furthermore, ANOVA (analysis of variance) analysis was done to find out the most influence factor on the responses. |
| format | Article |
| id | doaj-art-622fc7ad785445aaab4b771d8ecb2681 |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-622fc7ad785445aaab4b771d8ecb26812025-02-03T01:07:35ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/4856089A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263S. Lakshmana Kumar0V. Jacintha1A. Mahendran2R. M. Bommi3M. Nagaraj4Umamahesawari Kandasamy5Department of Mechanical EngineeringDepartment of Electronics and Communication EngineeringDepartment of Mechanical EngineeringInstitute of ECEInstitute of Agriculture EngineeringKebridehar UniversityIn this present paper, the machine learning approach is used to optimize, model, and predict the factors during drilling Nimonic C263 under dry mode. Nimonic C263 is tough to machine aero alloys, and it is required to find a predictive model and to optimize the factors in drilling this alloy before the actual machining process. It helps to avoid the actual machining cost and material cost. Experimental trails are planned based on Taguchi analysis, and L27 orthogonal array was chosen. Speed, feed, and approach angle of drill were considered as controlling factors, and cutting force and surface roughness were considered as responses. The feed forward neural network (FFNN) was used to develop a predictive model. The prediction capability was validated with a predictive model developed by Taguchi analysis. Furthermore, ANOVA (analysis of variance) analysis was done to find out the most influence factor on the responses.http://dx.doi.org/10.1155/2022/4856089 |
| spellingShingle | S. Lakshmana Kumar V. Jacintha A. Mahendran R. M. Bommi M. Nagaraj Umamahesawari Kandasamy A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263 Advances in Materials Science and Engineering |
| title | A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263 |
| title_full | A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263 |
| title_fullStr | A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263 |
| title_full_unstemmed | A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263 |
| title_short | A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263 |
| title_sort | machine learning approach to optimize model and predict the machining factors in dry drilling of nimonic c263 |
| url | http://dx.doi.org/10.1155/2022/4856089 |
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