Non-conventional micromachining control based on multivariable parameteranalysis using a fuzzy inference system

Abstract

Non-conventional manufacturing processes, such as pulsed electrochemical machining, are strategies for manufacturing metal components that optimize tool life. This is achieved by implementing non-traditional techniques based on electrolysis to remove metal in a controlled environment without generating contact between the tool and the workpiece. These characteristics allow a reduction in the resources required for the development of components with complex morphology on high-strength materials in many application fields, such as aerospace, medical, and precision tools. However, the characteristic features of non-linearity in this kind of process increase the complexity of the development of automatic control through traditional algebraic techniques in the regulation of uniform material wear. In this context, a non-traditional solution based on techniques derived from artificial intelligence bounded by fuzzy logic and genetic algorithms is presented for the tuning and automatic control of the pulsed electrochemical machining process. Furthermore, the efficiency is evaluated by analyzing the over-cutting phenomenon and the precision in developing linear polygonal machining on a micrometer scale.