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Prediction of surface roughness in turning of Ti-6Al-4V using cutting parameters, forces and tool vibration
| Content Provider | Scilit |
|---|---|
| Author | Sahu, Neelesh Kumar Andhare, Atul B. Andhale, Sandip Abraham, Roja Raju |
| Copyright Year | 2018 |
| Description | Journal: Iop Conference Series: Materials Science and Engineering Present work deals with prediction of surface roughness using cutting parameters along with in-process measured cutting force and tool vibration (acceleration) during turning of Ti-6Al-4V with cubic boron nitride (CBN) inserts. Full factorial design is used for design of experiments using cutting speed, feed rate and depth of cut as design variables. Prediction model for surface roughness is developed using response surface methodology with cutting speed, feed rate, depth of cut, resultant cutting force and acceleration as control variables. Analysis of variance (ANOVA) is performed to find out significant terms in the model. Insignificant terms are removed after performing statistical test using backward elimination approach. Effect of each control variables on surface roughness is also studied. Correlation coefficient $(R^{2} _{pred}$) of 99.4% shows that model correctly explains the experiment results and it behaves well even when adjustment is made in factors or new factors are added or eliminated. Validation of model is done with five fresh experiments and measured forces and acceleration values. Average absolute error between RSM model and experimental measured surface roughness is found to be 10.2%. Additionally, an artificial neural network model is also developed for prediction of surface roughness. The prediction results of modified regression model are compared with ANN. It is found that RSM model and ANN (average absolute error 7.5%) are predicting roughness with more than 90% accuracy. From the results obtained it is found that including cutting force and vibration for prediction of surface roughness gives better prediction than considering only cutting parameters. Also, ANN gives better prediction over RSM models. |
| Related Links | https://iopscience.iop.org/article/10.1088/1757-899X/346/1/012037/pdf http://iopscience.iop.org/article/10.1088/1757-899X/346/1/012037/pdf |
| ISSN | 17578981 |
| e-ISSN | 1757899X |
| DOI | 10.1088/1757-899x/346/1/012037 |
| Journal | Iop Conference Series: Materials Science and Engineering |
| Issue Number | 1 |
| Volume Number | 346 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2018-04-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Materials Science and Engineering Automotive Engineering Surface Roughness Artificial Neural Network Forces and Acceleration Cutting Force |
| Content Type | Text |
| Resource Type | Article |
