Modeling of Surface Roughness in Turning Operation Using Extreme Learning Machine

Abstract

Prediction model allows the machinist to determine the values of the cutting performance before machining. According to the literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Response surface methodology (RSM) is a statistical method that only predicts effectively within the observed data provided. Most artificial intelligent systems mostly had an issue with user-defined data and long processing time. Recently, the extreme learning machine (ELM) method has been introduced, combining the single hidden layer feedforward neural network with analytically determined output weights. The advantage of this method is that it can overcome the limitations due to the previous methods which include too many engineers' judgment and slow iterative learning phase. Therefore, in this study, the ELM was proposed to model the surface roughness based on RSM design of experiment. The results indicate that ELM can yield satisfactory solution for predicting the response within a few seconds and with small amount of error.

Machining Chatter Prediction Using a Data Learning Model

“…Chatter prediction using machine learning techniques has also been studied, including neural networks, support vector machines, and others. Ahmad et al [17] developed two different models of extreme learning techniques using random weights and hidden nodes. Lamraoui et al [18] applied a neural network (NN) and the input data based on signal analysis was used to predict milling stability.…”

The Optimisation of Surface Roughness using Extreme Learning Machine and Particle Swarm Optimization

“…ELM also requires less user-defined parameters and possesses similar structure as single layer feedforward neural network (SLFN) with an analytically determined output weight. In 2015, Ahmad et al conducted a research on modelling techniques using ELM, Neural Network (ANN), Support Vector Machine (SVM) and Response Surface Methodology (RSM) [18]. The results reveal that ELM performs better when compared to other techniques in terms of the prediction accuracy and the training speed.…”