Indian Journal of Science and Technology

Abstract

Background/Objectives: Surface roughness plays a major role in determining how an original component will interact with its environment. For getting a good surface finish industries spent huge cost for introducing new technologies. The use of advanced engineering ceramics and composites in the aerospace and defense industries is continuous and increases day by day. Surface Roughness (SR) prediction plays a vital role in improving the surface finish in industries. The present work deals with predicting the surface roughness of AISI1020 steel material in Electrical Discharge Machining (EDM) by using Adaptive Neuro Fuzzy Inference System (ANFIS). Methods/Statistical Analysis: The discharge voltage, discharge current, pulse on time, pulse off time, gap between tool and workpiece and oil pressure are taken as the input parameters, whereas SR is the output machining parameter. Design of Experiment (DoE) is based on Response Surface Methodology (RSM). ANFIS model has been constructed using Gaussian membership function (Gaussmf) with 2 membership function for each and every input parameter and linear membership function for output parameter SR and MRR. Findings: We employed both the back propagation method and the hybrid method for membership function parameter training. Based on the conclusion from the comparison of ANFIS with two types of membership function parameter training, hybrid method provides accurate results. Applications/Improvements: It is further used that the maximum error when the network is optimized by the intelligent technique has been reduced considerably. Sensitivity analysis is also done to find the relative influence of factors on the performance measures. It is observed that type of material is having more influence on the performance measures.

Keywords: Adaptive Steganography, Enhanced Canny Operator, Ensemble Classifier, Least Significant Bit, Positive Predictive Rate