A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Investigation and prediction of machining characteristics of aerospace material through WEDM process using machine learning
https://orcid.org/http://orcid.org/0000-0001-8773-0767
In this study, pure titanium is used as the workpiece material and a machine learning approach is used to forecast the material removal rate (MRR) during wire electrical discharge machining (WEDM). The novelty of present research work was to perform machining operation on pure titanium through the WEDM process as the pure titanium is majorly used in aviation and aircraft industries. The machining industries could get help through this study to run the machining process based on machine learning approach for increased productivity considering the scope of Industry 4.0. This study's goal is to create a precise prediction model that can anticipate MRR based on a variety of input factors, such as pulse-on time, pulse-off time, wire feed rate, wire tension, servo voltage, and peak current. Experimental data was collected through a series of WEDM experiments on pure titanium using an L-27 orthogonal array based on Taguchi's design of experiments. MRR was selected from the pre-processed data to train and evaluate the machine learning model. The prediction model was developed using a variety of regression techniques, including Linear Regression using scikit-learn, support vector regression (SVR), random forest, K-nearest neighbors regression using Python in Jupyter notebook. Coefficient of determination (R-squared) and root mean squared error were used to assess the model's performance. The results show that the linear regression using scikit-learn and SVR algorithm performs better in terms of prediction accuracy than the other algorithms. The surface integrity analysis was performed to determine the effects of process parameters on machined surface. The proposed study helps to increase the efficacy and efficiency of WEDM operations by offering a trustworthy tool for MRR predictions. The proposed research depicts a good agreement between experimental and predicted values.
Investigation and prediction of machining characteristics of aerospace material through WEDM process using machine learning
https://orcid.org/http://orcid.org/0000-0001-8773-0767
In this study, pure titanium is used as the workpiece material and a machine learning approach is used to forecast the material removal rate (MRR) during wire electrical discharge machining (WEDM). The novelty of present research work was to perform machining operation on pure titanium through the WEDM process as the pure titanium is majorly used in aviation and aircraft industries. The machining industries could get help through this study to run the machining process based on machine learning approach for increased productivity considering the scope of Industry 4.0. This study's goal is to create a precise prediction model that can anticipate MRR based on a variety of input factors, such as pulse-on time, pulse-off time, wire feed rate, wire tension, servo voltage, and peak current. Experimental data was collected through a series of WEDM experiments on pure titanium using an L-27 orthogonal array based on Taguchi's design of experiments. MRR was selected from the pre-processed data to train and evaluate the machine learning model. The prediction model was developed using a variety of regression techniques, including Linear Regression using scikit-learn, support vector regression (SVR), random forest, K-nearest neighbors regression using Python in Jupyter notebook. Coefficient of determination (R-squared) and root mean squared error were used to assess the model's performance. The results show that the linear regression using scikit-learn and SVR algorithm performs better in terms of prediction accuracy than the other algorithms. The surface integrity analysis was performed to determine the effects of process parameters on machined surface. The proposed study helps to increase the efficacy and efficiency of WEDM operations by offering a trustworthy tool for MRR predictions. The proposed research depicts a good agreement between experimental and predicted values.
Investigation and prediction of machining characteristics of aerospace material through WEDM process using machine learning
https://orcid.org/http://orcid.org/0000-0001-8773-0767
In this study, pure titanium is used as the workpiece material and a machine learning approach is used to forecast the material removal rate (MRR) during wire electrical discharge machining (WEDM). The novelty of present research work was to perform machining operation on pure titanium through the WEDM process as the pure titanium is majorly used in aviation and aircraft industries. The machining industries could get help through this study to run the machining process based on machine learning approach for increased productivity considering the scope of Industry 4.0. This study's goal is to create a precise prediction model that can anticipate MRR based on a variety of input factors, such as pulse-on time, pulse-off time, wire feed rate, wire tension, servo voltage, and peak current. Experimental data was collected through a series of WEDM experiments on pure titanium using an L-27 orthogonal array based on Taguchi's design of experiments. MRR was selected from the pre-processed data to train and evaluate the machine learning model. The prediction model was developed using a variety of regression techniques, including Linear Regression using scikit-learn, support vector regression (SVR), random forest, K-nearest neighbors regression using Python in Jupyter notebook. Coefficient of determination (R-squared) and root mean squared error were used to assess the model's performance. The results show that the linear regression using scikit-learn and SVR algorithm performs better in terms of prediction accuracy than the other algorithms. The surface integrity analysis was performed to determine the effects of process parameters on machined surface. The proposed study helps to increase the efficacy and efficiency of WEDM operations by offering a trustworthy tool for MRR predictions. The proposed research depicts a good agreement between experimental and predicted values.
Investigation and prediction of machining characteristics of aerospace material through WEDM process using machine learning
Int J Interact Des Manuf
Chalisgaonkar, Rupesh (author) / Sirohi, Sachin (author) / Kumar, Jatinder (author) / Rathore, Sachin (author)
2024-10-01
21 pages
Article (Journal)
Electronic Resource
English
Support vector regression , Pure titanium , Material removal rate , Wire electrical discharge machine , Machine learning Engineering , Engineering, general , Engineering Design , Mechanical Engineering , Computer-Aided Engineering (CAD, CAE) and Design , Electronics and Microelectronics, Instrumentation , Industrial Design
WEDM of nickel based aerospace alloy: optimization of process parameters and modelling
| Springer Verlag | 2016