Predicting tensile-shear strength of nugget using M5P model tree and random forest: An analysis

Abstract

Abstract Predicting the outcomes of a weld based on few metals with respect to its process parameters is a trivial phenomenon. However, the prediction requires complex mathematical formulation when the number of metals grows. The exponential rise in testing data of welding components in recent time, have increased the data inconsistency and complexity by manifolds. Further, the multi-physical characteristic of welding data adds to its chaotic nature. This makes manual or simulation-based extraction of useful information from welded data extremely challenging. Developing predictive models for tensile-shear strength of Resistance Spot Welding (RSW) is highly latency-bound. The recent success of machine learning approaches in variety of fields gives us motivation to address this issue. In this paper, we proposed a machine learning model inspired from random forest (RF) which predicts the tensile-shear strength of nugget from its input parameters and large number of metals. We trained the prediction model using data from 435 spot-welding cases and compared its performance with widely used M5P model tree. For all cases, RF-based prediction model outperforms the M5P model in terms of accuracy. Four different feature extraction techniques namely manual feature selection, correlation attribute eval., classification attribute eval., and reliefF attribute eval. were investigated to improve the performance of random forest model. From these methods, when model is very complex i.e. higher training size, classification attribute eval. provides greater accuracy with RMSETest of 0.5442. Moreover, no overfitting and underfitting was observed in this prediction.