Material Removal Rate and Surface Roughness Prediction in Turning and Milling Operations Using Taguchi Analysis, Support Vector Machine and Gaussian Process Regression

2022 ◽  
pp. 515-524
Author(s):  
V. Vakharia ◽  
J. Sanghvi ◽  
H. Thakker
Keyword(s):  
Surface Roughness ◽  
Support Vector Machine ◽  
Gaussian Process ◽  
Material Removal Rate ◽  
Removal Rate ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Taguchi Analysis ◽  
Milling Operations ◽  
Roughness Prediction

Surface Roughness Prediction Modelling for WEDM of AA6063 Using Support Vector Machine Technique

2019 ◽  
Vol 969 ◽  
pp. 607-612 ◽  
Author(s):  
Thakur Singh ◽  
Pawan Kumar ◽  
Joy Prakash Misra
Keyword(s):  
Surface Roughness ◽  
Support Vector Machine ◽  
Research Work ◽  
Smart Manufacturing ◽  
Support Vector ◽  
Response Parameter ◽  
Roughness Prediction ◽  
Input Variables ◽  
Pulse On Time ◽  
Pulse Off Time

This research work presents an incorporated approach to modelling of WEDM of AA6063 (armour applications) using support vector machine technique. The experimental investigation has been carried out with four input variables namely pulse-on-time (Pon), pulse-off-time (Poff), servo-voltage (VS) and peak-current (IP). Surface roughness is measured as response parameter. The experimental runs are designed according to 3k full factorial design (k is number of input variables). It is apparent from this study that values anticipated by developed model are found closer to experimental results. Thus, it ensures appropriateness of model for prediction purpose and smart manufacturing. Machined surfaces are also examined by SEM to critically evaluate the process.

scholarly journals Integrated optimization model in wire electric discharge machining using Gaussian process regression and wolf pack algorithm approach while machining SiCp/Al composite

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401878740 ◽  
Author(s):  
Jun Ma ◽  
Wuyi Ming ◽  
Jinguang Du ◽  
Hao Huang ◽  
Wenbin He ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Material Removal Rate ◽  
Optimization Model ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Gaussian Process Regression ◽  
Wire Electrical Discharge Machining ◽  
Al Composite

To further improve prediction accuracy and optimization quality of wire electrical discharge machining of SiCp/Al composite, trim cuts were performed using Taguchi experiment method to investigate the influence of cutting parameters, such as pulse duration ( Ton), pulse interval ( Toff), water pressure ( Wp), and wire tension ( Wt)), on material removal rate and three-dimensional surface characteristics ( Sq and Sa). An optimization model to predict material removal rate and surface quality was developed using a novel hybrid Gaussian process regression and wolf pack algorithm approach based on experiment results. Compared with linear regression model and back propagation neural network, the availability of Gaussian process regression is confirmed by experimental data. Results show that the worst average predictive error of five independent tests for material removal rate, Sq, and Sa are not more than 10.66%, 19.85%, and 22.4%, respectively. The proposed method in this article is an effective method to optimize the process parameters for guiding the actual wire electrical discharge machining process.

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