This study throws light on developing a new prediction model using transfer learning (TL) to predict the material removal rate (MRR) of the electrical discharge machining (EDM) process. The input parameters are pulse on time, current, and voltage. The developed model is based on Deep Neural Network (DNN). The proposed method overcomes the limitations of traditional machine learning (ML) models, which require many experimental datasets for accurately predicting the responses. The results show that transfer learning (TL) can be used to overcome the issue of limited data in the manufacturing process.
Transfer learning, Deep Neural Network, Electrical Discharge Machining, Material Removal Rate, Smart Manufacturing
The code is written in Python 3.8.6. The following libraries are required to run the code beyond the Python's standard libraries:
pip install -r requirements.txt
Run TL_EDM.ipynb in Jupyter Notebook or Google Colab.
pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change and please read the CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
Aquib Iqbal : aquibiqbal@umass.edu
Abid Hasan Zim : abidhasanzim39@gmail.com