Comparative Analysis of Abrasive Wear Using Response Surface Method and Artificial Neural Network

Abstract

This research work deals with the application of response surface methodology and artificial neural network-based mathematical modelling of abrasive wear volume for a dry sliding wear of PTFE pin. The experiments were designed based on central composite design. The disc speed, load and sliding distance have been selected as parameters of the process, while the abrasive wear volume has been selected as an output. The ANNOVA test revealed that the disc speed has maximum influence and contributes 28.21% of abrasive wear volume followed by load, which contributes 12.83% of abrasive wear volume. The two models were compared using root mean square error and absolute standard deviation. The artificial neural network-predicted values of abrasive wear volume were found in close agreement with the actual experimental results as compared to response surface methodology predicted results and hence recommended for the similar studies.

Understanding the effect of solid lubricants on scratch behavior in epoxy resins: An experimental and statistical approach

“…It is a very beneficial tool for evaluating industrial problems where many parameters will affect the so-called response output. 20 Large quantities of tests need to be performed using conventional experimental data collection methods in order to evaluate the influence of control factors on response (output). RSM is generally used to reduce the number of experimental runs to a great extent.…”

“…The response surface methodology (RSM) is a useful tool for modeling that integrates both computational and mathematical approaches. It is a very beneficial tool for evaluating industrial problems where many parameters will affect the so‐called response output 20 . Large quantities of tests need to be performed using conventional experimental data collection methods in order to evaluate the influence of control factors on response (output).…”

Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models

“…In the analysis of tribology, many mathematical modeling methods have been built. Among them are atomic and molecular kinetics, finite element method, symptom modelling, continuum mechanics, dimension reduction, analysis, boundary element system, stochastic models 26 . Nevertheless, since tribological behaviours are complex and nonlinear, mathematical models are limited.…”