“Surface Roughness Analysis And Compare Prediction And Experimental Value For Cylindrical Stainless Steel Pipe (Ss 316l) In CNC Lathe Turning Process Using ANN Method For Re- Optimization And Cutting Fluid”

---------------------------------------------------------------ABSTRACT------------------------------------------------------Surface quality of the machined parts is one of the most important product quality indicators and most frequent customer requirements. Metal cutting processes are important due to increased consumer demands for quality metal cutting related products more precise tolerances and better product surface roughness that has driven the metal cutting industry to continuously improve quality control of metal cutting processes. The average surface roughness (Ra) represents a measure of the surface quality, and it is mostly influenced by the following cutting parameters: the cutting speed, feed rate, and depth of cut. This paper presents optimum surface roughness by using CNC Lathe for 316L stainless steel pipe with Artificial Neural Networks Optimization (ANNO). The approach is based on Multiple Regression Analysis (MRA) Method and Artificial Neural Networks (ANN). The main objectives is to find the optimized parameters and the most dominant variables cutting speed, feed rate, axial depth and radial depth. The ANN model indicates that the feed rate is the most significant factor affecting surface roughness. The mathematical model developed by using multiple regression method shows the accuracy of surface roughness Prediction. The result from this research is useful to be implemented in both timeconsuming and laborious works in industry to reduce time and cost in surface roughness prediction.