Towards next generation electrochemical machining controllers: A fuzzy logic control approach to ECM

► We develop a fuzzy logic controller to to add intelligence to the ECM process. ► We built an experimental ECM drilling rig that demonstrates the improvement through the integration of a fuzzy logic controller into the existing control system. ► Proposed model allows for “human like” decision-making intelligence to be incorporated into this manufacturing process. ► The proposed system is proven to be highly adaptive and accurate. Electrochemical machining (ECM) is a manufacturing process that offers a number of advantages (e.g. no mechanical stress) over its nearest competitors as certain trends in production move towards the micro scale. Maintaining optimum ECM process conditions ensures higher machining efficiency and performance. This paper presents the development of a fuzzy logic controller to add intelligence to the ECM process. An experimental ECM drilling rig, at University of Manchester, was improved through the integration of a fuzzy logic controller into the existing control system. Matlab (Fuzzy Logic Toolbox) was used to build a fuzzy logic controller system, which controls the feed rate of the tool and the flow rate of the electrolyte. The objective of the fuzzy logic controller was to improve machining performance and accuracy by controlling the ECM process variables. The results serve to introduce innovative possibilities and provide potential for future applications of fuzzy logic control (FLC) in ECM. Hybrid controllers that integrate fuzzy logic into the control system allow for “human like” decision-making intelligence to be incorporated into ECM controllers. The focus of this paper is the feasibility of FLC in ECM, but the results have the potential of being applied to EMM. As the future of ECM moves towards electrochemical micromachining (EMM), the need for process uncertainty control in this area may be met by FLC, which has advantages over conventional methods of process control.