Prediction and Analysis of the Surface Roughness in CNC End Milling Using Neural Networks

In the metal cutting process of machine tools, the quality of the surface roughness of the product is very important to improve the friction performance, corrosion resistance, and aesthetics of the product. Therefore, low surface roughness is ideal for mechanical cutting. If the surface roughness of the product can be predicted, not only the quality of the product can be improved but also the processing cost can be reduced. In this study a back propagation neural network (BPNN) was proposed to predict the surface roughness of the processed workpiece. ANOVA was used to analyze the influence of milling parameters, such as spindle speed, feed rate, cutting depth, and milling distance. The experimental results show that the root mean square error (RMSE) obtained by using the back propagation neural network is 0.008, which is much smaller than the 0.021 obtained by the traditional linear regression method.

Optimization of CNC End Milling Process Parameters of Low-Carbon Mold Steel Using Response Surface Methodology and Grey Relational Analysis

The manufacturing sectors are consistently striving to figure out ways to minimize the consumption of natural resources through rational utilization. This is achieved by a proper understanding of every minute influence of parameters on the entire process. Understanding the influencing parameters in determining the machining process efficacy is inevitable. Technological advancement has drastically improved the machining process through various means by providing better quality products with minimum machining cost and energy consumption. Specifically, the machining factors such as cutting speed, spindle speed, depth of cut, rate of feed, and coolant flow rate are found to be the governing factors in determining the economy of the machining process. This study is focused on improving the machining economy by enhancing the surface integrity and tool life with minimum resources. The study is carried out on low-carbon mold steel (UNS T51620) using Box–Behnken design and grey regression analysis. The optimized multiobjective solution for surface roughness (Ra), material removal rate (MRR), and power consumed (Pc) and tool life is determined and validated through the confirmatory run. The optimized set of parameters in Box–Behnken design and grey regression analysis with that of confirmatory runs shows a 10% deviation that proves the reliability of the optimization techniques employed.

INFLUENCE OF PROCESS PARAMETERS ON 6065 – T6 ALUMINUM ALLOY USING CNC END MILLING – A FUZZY APPROACH

The present work deals with the investigation of performance parameters of surface roughness and material removal rate of the machined parts during milling of Aluminum alloy 6065-T6 using CNC vertical milling machine with High speed steel milling, Carbide tool cutter by optimizing the process parameters such as speed, feed, cutting environment, depth of cut and cutting tool. The experiments are conducted based on Taguchi design of experiments with an orthogonal array (L16) the optimization of process parameters based on performance measures are done by using Fuzzy Logic. Also, the most influential process parameters are finding out by using ANOVA technique. Ideal execution parameters are found for smaller surface roughness and larger MRR utilizing the MINITAB and MATLAB software’s.