Prediction of multi performance characteristics of wire EDM process using grey ANFIS

2017 International Conference on Materials and Intelligent Manufacturing

材料科学;机械制造

Kumanan, Somasundaram^1 ; Nair, Anish^1

Production Engineering Department, National Institute of Technology, Tiruchirappalli

620015, India^1

关键词: Adaptive neuro-fuzzy inference system;    Grey relational analysis;    Material removal rate;    Non-traditional machining;    Performance characteristics;    Performance optimizations;    Ultra-supercritical power plants;    Wire-cut electrical discharge machining;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/244/1/012003/pdf
DOI  :  10.1088/1757-899X/244/1/012003

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Super alloys are used to fabricate components in ultra-supercritical power plants. These hard to machine materials are processed using non-traditional machining methods like Wire cut electrical discharge machining and needs attention. This paper details about multi performance optimization of wire EDM process using Grey ANFIS. Experiments are designed to establish the performance characteristics of wire EDM such as surface roughness, material removal rate, wire wear rate and geometric tolerances. The control parameters are pulse on time, pulse off time, current, voltage, flushing pressure, wire tension, table feed and wire speed. Grey relational analysis is employed to optimise the multi objectives. Analysis of variance of the grey grades is used to identify the critical parameters. A regression model is developed and used to generate datasets for the training of proposed adaptive neuro fuzzy inference system. The developed prediction model is tested for its prediction ability.