Machine learning data augmentation by electrical contact resistance technique for edge effect correction in nanoindentation

This research describes a novel method to correct for the biased measurement of contact area due to edge-effect phenomena in Instrumented Indentation Test (IIT) process. IIT is a widely used depth-sensing technique to evaluate the mechanical characteristics of materials e.g., Indentation modulus, Indentation hardness, residuals stresses, damping, creep. The proposed procedure utilizes the Electrical Contact Resistance approach to augment the data on contact area. Monte Carlo Markov Chain model was used to randomly generate the relatively small dataset to robustly predict projected area. The advantage of the method is demonstrated through the application for different conductive metals.