Non-contact Inspection of Electrically Discharged Materials Using Machine Learning
A machine learning approach for predicting surface roughness in EDM-machined materials using process parameters instead of contact-based inspection.
Overview
This project predicts the surface roughness of metals machined by Electrical Discharge Machining using non-contact inspection methods and machine learning. Traditional inspection approaches are often contact-based, slow, and risky for precision surfaces.
The research introduces a data-driven alternative that uses regression algorithms on experimentally collected and augmented data to estimate surface roughness from process parameters.
What is EDM and Why It Matters
Electrical Discharge Machining is a non-conventional manufacturing process used for hard-to-machine materials. Surface roughness, represented as Ra, is a critical quality metric, but it is often measured with physical probes.
The aim was to remove the need for contact-based inspection by building a predictive model that estimates surface roughness from process parameters, enabling faster and safer evaluation.
Data Collection and Augmentation
Experimental data was collected using a real EDM machine setup at the university. Pulse on time, pulse off time, current, and voltage were recorded along with measured surface roughness.
- The initial dataset contained 31 experimental data points.
- Data augmentation used scaling, shifting, and controlled noise injection.
- The augmented dataset contained 9,300 samples for model training and evaluation.
Models Used
Three regression algorithms were trained and compared: K-Nearest Neighbors, Support Vector Regressor, and Random Forest Regressor. Each model used an 80:20 train-test split and was evaluated with R2 score and Mean Squared Error.
Key Findings
- K-Nearest Neighbors consistently outperformed the other models.
- KNN reached an R2 score of approximately 0.999.
- KNN produced an MSE of approximately 0.00157.
- Predicted-vs-actual plots and residual histograms confirmed tight, unbiased predictions.
Benchmarking
The work compared the results with approaches such as neural networks, W-ELM, SinGAN, Taguchi-ANN hybrids, and fuzzy logic systems. The simpler KNN-based approach matched or exceeded several reported results while remaining easier to deploy.
My Role
- Conducted EDM experiments.
- Designed the data augmentation strategy.
- Implemented all regression models.
- Analyzed and visualized model performance.
- Compared outcomes with state-of-the-art methods.
Future Scope
- Integrate the model with EDM machines for live surface monitoring.
- Use deep learning or transformers on surface image data for broader generalization.
- Explore 3D surface reconstruction with non-contact sensors.
Final Takeaway
This research shows how simple but well-prepared machine learning models can replace invasive inspection workflows, making manufacturing evaluation faster, safer, and easier to automate.