Abstract
This paper is an attempt to compare artificial neural networks and response surface methodology for modeling surface roughness and cutting force in terms of better coefficient of determination (R2), lower root mean square error (RMSE) and model predictive error (MPE). Models were developed based on three-level Box-Behnken design (BBD) of experiments with 15 experimental runs composed of three center points, conducted on Inconel 718 work material using coated carbide insert with cutting speed, feed rate and depth of cut as the process parameters under dry environment. Results show that the artificial neural network (ANN) compared with RSM is a better reliable and accurate approach for predicting and detecting the non-linearity of surface roughness and cutting force mathematical models in terms of correlation and errors. Indeed, the ANN prediction model provides a maximal benefit in terms of precision of 10.1% for cutting force (Fv) and 24.38% for surface roughness (Ra) compared with the RSM prediction model.
Highlights
The Inconel 718 is one of the most important materials used in modern industries
4 Conclusion This study compares the performance of surface response (RSM) and neural network (ANN) methodologies with their modeling, prediction and generalization capabilities using the experimental data based on the Box-Behnken design for surface roughness and cutting force
The following conclusions are drawn from this work: From analysis of variance (ANOVA) it can be concluded that the surface roughness is significantly affected by feed rate and cutting speed with the contribution of 45% and 20% respectively
Summary
In addition of the best properties in terms of high strength, corrosion resistance, heat resistance and fatigue resistance, the Inconel 718 has, a low thermal conductivity as it is mentioned by Lynch [1] This type of alloy is difficult to machine for the following reasons as it is presented by Alauddin [2]: High work hardening rates at machining, strain rates leading to high cutting forces; abrasiveness; toughness, gummy and strong tendency to weld to the tool with forming the built-up edge; low thermal properties leading to high cutting temperatures. Sahoo et al [6], confirmed this conclusion when studying the development of flank wear model in turning hardened EN 24 steel with PVD TiN coated mixed ceramic insert under dry environment
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