• Praveen Vishwakarma
• Ishan Patel

The main purpose of today’s manufacturing industries is to produce low cost, high quality products in short time. They mainly focused on achieving high quality, in term of part accuracy, surface finish, high production rate etc. So, the selection of optimal cutting parameters is a very important issue for every machining process in order to reduce the machining costs and increase the quality of machining products. In this project the cutting of Ductile Iron under dry condition is carried out using CNC lathe machine. Taguchi method is used to formulate the experimental layout. The effect of cutting condition (spindle speed, feed rate and depth of cut) on surface roughness were studied and analysed. The CNC turning machine is used to conduct experiments based on the Taguchi design of experiments (DOE) with orthogonal L9 array. Optimal cutting parameters for each performance measure were obtained employing Taguchi techniques. The orthogonal array, signal to noise ratio and analysis of variance were employed to find minimum surface roughness. Optimum results are finally verified with the help of confirmation experiments. Machine learning algorithms are used to verify the predicted versus actual surface roughness values under dry turning conditions.

Dry Turning, Taguchi Method, Machine learning algorithm, CNC Lathe

"Optimization Of Turning Parameters Using Taguchi & Machine Learning Algorithms Under Dry Turning Conditions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.c455-c462, July-2024, Available :http://www.jetir.org/papers/JETIR2407256.pdf

"Optimization Of Turning Parameters Using Taguchi & Machine Learning Algorithms Under Dry Turning Conditions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppc455-c462, July-2024, Available at : http://www.jetir.org/papers/JETIR2407256.pdf