eesti teaduste
akadeemia kirjastus
SINCE 1952
Proceeding cover
of the estonian academy of sciences
ISSN 1736-7530 (Electronic)
ISSN 1736-6046 (Print)
Impact Factor (2020): 1.045

Evaluation of surface roughness in the turning of mild steel under different cutting conditions using backpropagation neural network; pp. 109–115

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Mohamed Rafik Noor Mohamed Qureshi, Shubham Sharma, Jujhar Singh, Shaik Dawood Abdul Khadar, Rahmath Ulla Baig


This paper exhibits a model of feed­forward backpropagation neural network system for estimating surface roughness in the turning operation. The workpiece of mild steel (carbon content 0.2%; hardness125 BHN) has been taken for turning operation under different cutting conditions with high­speed steel (HSS) tool (carbon content 0.75%; vanadium content 1.1%, molybdenum content 0.65%, chromium content 4.3%, tungsten content 18%, cobalt content 5%, hardness 290 BHN). Experiments have been ex­ ecuted on lathe machine HMT LB20. In the neural network model, the speed, feed and depth of cut have been considered as process parameters and surface roughness was taken as a response parameter. The neural network was developed based on initial experimental data. The developed neural network model during testing and validation was found to be within acceptable limits. The estimated maximum error was expected to be 10.77%. Error below 20% was considered reasonable, taking into account the fact that there is an intrinsic irregularity in metal cutting procedure.

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