Response surface and artificial neural network prediction model and optimization for surface roughness in machining

• Published in: International journal of industrial engineering computations Vol. 6; no. 2; pp. 229 - 240
• Main Authors: Sahoo, Ashok Kumar, Rout, Arun Kumar, Das, Dipti Kanta
• Format: Journal Article
• Language: English
• Published: Growing Science 01.01.2015
• Subjects: ANN; Artificial neural networks; Errors; Factorial design; Learning theory; Machining; Mathematical models; Medium carbon steels; Neural networks; Optimization; Response surface model; Surface roughness; Three dimensional
• ISSN: 1923-2926; 1923-2934
• DOI: 10.5267/j.ijiec.2014.11.001

This present article deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R2 = 0.99 close to unity). It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02%, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 ie depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min, respectively.