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

Research highlights? 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.

[1]  Shih-Yu Huang,et al.  Improvement of machining accuracy by fuzzy logic at corner parts for wire-EDM , 2001, Fuzzy Sets Syst..

[2]  J. A. McGeough,et al.  Process monitoring of electrochemical micromachining , 1998 .

[3]  Srichand Hinduja.  Proceedings of the 34th International MATADOR Conference , 2000 .

[4]  A. W. Labib,et al.  Optimal control of an unreliable machine using fuzzy-logic control: from design to implementation , 2005 .

[5]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[6]  Hartmut Surmann,et al.  Automatic electropolishing of cobalt chromium dental cast alloys with a fuzzy logic controller , 1998 .

[7]  Zheng Yunfei,et al.  Localized electrochemical micromachining with gap control , 2003 .

[8]  S Mitra,et al.  Electrochemical machining: new possibilities for micromachining , 2002 .

[9]  B. Kosko.  Fuzzy Thinking: The New Science of Fuzzy Logic , 1993 .

[10]  M. Reza Abdi,et al.  Grouping and selecting products: the design key of Reconfigurable Manufacturing Systems (RMSs) , 2004 .

[11]  B. Bhattacharyya,et al.  Experimental investigation into electrochemical micromachining (EMM) process , 2003 .

[12]  J. Kozak.  Computer simulation system for electrochemical shaping , 2001 .

[13]  Ashraf Labib,et al.  Fuzzy adaptive preventive maintenance in a manufacturing control system: a step towards self-maintenance , 2006 .

[14]  M. R. Abdi,et al.  Feasibility study of the tactical design justification for reconfigurable manufacturing systems using the fuzzy analytical hierarchical process , 2004 .

[15]  Meng You Huo,et al.  Adaptive fuzzy control system of a servomechanism for electro-discharge machining combined with ultrasonic vibration , 2002 .

[16]  Andi Sudiarso,et al.  A fuzzy logic approach to an integrated maintenance/production scheduling algorithm , 2002 .

[17]  Ashraf Labib,et al.  A fuzzy logic control approach to electrochemical machining (ECM) , 2004 .

[18]  B. Bhattacharyya,et al.  Experimental investigation on the influence of electrochemical machining parameters on machining rate and accuracy in micromachining domain , 2003 .

[19]  Ashraf Labib,et al.  Fuzzy Approaches to Evaluation in Engineering Design , 2005 .