Application of an Adaptive “Neuro-Fuzzy” Inference System in Modeling Cutting Temperature during Hard Turning

Abstract

The machining of hard materials with the most economical process is a challenge that is the aim of production systems. Increasing demands of the market require a hard processing hardened steel in order to avoid finishing grinding. This research considers the turning of hardened steel without cooling with two types of tools: cubic boron nitride (CBN) and hard metal (HM) inserts. To estimate the influence of machining conditions on cutting temperature, a central composition design with three factors on five levels was used. The development of advanced models allows one to meet the accelerated demands in terms of productivity, product quality, and reduced production costs. Based on experimental data, three input regimes (cutting speed, feed, and depth of cut), and one attributive factor (tool material) were used as input variables, while cutting temperature was used as the output of the adaptive neuro-fuzzy inference systems (ANFIS). The model was trained, tested, and validated with a combined input/output data set. The obtained ANFIS model could be applied with high precision to determine the cutting temperature in machining of hardened steel. From an economic point of view, the obtained model can directly affect the cost of processing because cutting temperature and tool life are directly relieved.

An advance of optimization by Simulated Annealing Algorithm and ANFIS to minimize delamination during drilling Chopped Strand Mat GFRP

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Estimation of Australia’s Outbound Airline Passenger Demand Using an Adaptive Neuro-Fuzzy Inference System

“…In Layer 1, the parameters are referred to as the premise parameters (Savkovic et al, 2019) whilst the parameters in Layer 4 are called the consequent parameters in Layer 4 (Kumar and Vaidehi, 2017).…”

A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy

“…In recent years, several researchers have focused on developing comprehensive modeling techniques to predict the machining performance through considering diverse input parameters. Although different statistical approaches have been proposed for modeling the machining processes, such as regression models [ 14 ], support vector machines [ 15 ], and finite element models [ 16 ], soft computing techniques such as fuzzy logic [ 17 ], artificial neural networks [ 18 ], and adaptive neuro-fuzzy inference system (ANFIS) [ 19 ] are prominent in predicting the performance of machining processes because of their progressive computational capability.…”