Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625
Roughness is a prime parameter in any process/operation as it aids in confirming the quality status of the product. The insert and workpiece would develop a lot of friction and as a result, it generates heat in the cutting zone, which affects the machined surface. The speed, feed, and depth of cut w...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/6038804 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567516152463360 |
|---|---|
| author | A. S. Rajesh M. S. Prabhuswamy M. Rudra Naik |
| author_facet | A. S. Rajesh M. S. Prabhuswamy M. Rudra Naik |
| author_sort | A. S. Rajesh |
| collection | DOAJ |
| description | Roughness is a prime parameter in any process/operation as it aids in confirming the quality status of the product. The insert and workpiece would develop a lot of friction and as a result, it generates heat in the cutting zone, which affects the machined surface. The speed, feed, and depth of cut were chosen as processing factors. L27 Orthogonal array is used based on the Taguchi technique. The regression analysis is used to develop an equation to predict the roughness. The impact of the processing factors on the machined surface is studied with help of ANOVA (Analysis of Variance). Furthermore, the estimation of surface roughness is carried out using a machine learning-based model-feed forward (nonlinear autoregressive network) NARX network, and the evaluated surface roughness is compared with the values predicted by the regression model and experimental results. The average percentage error observed with the predicted values by NARX is observed as 3.01%, which is lower than the average percentage error observed by the regression model 5.131%. Thus, this work provides the best machine learning approach to the prognosis of the roughness in dry turning of Inconel 625, which would save a lot of time and unnecessary wastage of the work material. |
| format | Article |
| id | doaj-art-54ce20e38b3a449391062f402bde4f90 |
| 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-54ce20e38b3a449391062f402bde4f902025-02-03T01:01:21ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/6038804Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625A. S. Rajesh0M. S. Prabhuswamy1M. Rudra Naik2Department of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Electro-Mechanical EngineeringRoughness is a prime parameter in any process/operation as it aids in confirming the quality status of the product. The insert and workpiece would develop a lot of friction and as a result, it generates heat in the cutting zone, which affects the machined surface. The speed, feed, and depth of cut were chosen as processing factors. L27 Orthogonal array is used based on the Taguchi technique. The regression analysis is used to develop an equation to predict the roughness. The impact of the processing factors on the machined surface is studied with help of ANOVA (Analysis of Variance). Furthermore, the estimation of surface roughness is carried out using a machine learning-based model-feed forward (nonlinear autoregressive network) NARX network, and the evaluated surface roughness is compared with the values predicted by the regression model and experimental results. The average percentage error observed with the predicted values by NARX is observed as 3.01%, which is lower than the average percentage error observed by the regression model 5.131%. Thus, this work provides the best machine learning approach to the prognosis of the roughness in dry turning of Inconel 625, which would save a lot of time and unnecessary wastage of the work material.http://dx.doi.org/10.1155/2022/6038804 |
| spellingShingle | A. S. Rajesh M. S. Prabhuswamy M. Rudra Naik Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625 Advances in Materials Science and Engineering |
| title | Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625 |
| title_full | Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625 |
| title_fullStr | Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625 |
| title_full_unstemmed | Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625 |
| title_short | Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625 |
| title_sort | machine learning approach prediction of surface roughness in dry turning inconel 625 |
| url | http://dx.doi.org/10.1155/2022/6038804 |
| work_keys_str_mv | AT asrajesh machinelearningapproachpredictionofsurfaceroughnessindryturninginconel625 AT msprabhuswamy machinelearningapproachpredictionofsurfaceroughnessindryturninginconel625 AT mrudranaik machinelearningapproachpredictionofsurfaceroughnessindryturninginconel625 |