Bernard W. Ikua

Fuzzy logic based controller for micro-electro discharge machining servo systems

In this paper, a tunable fuzzy logic based servo controller for monitoring and control of the micro-electro discharge machining (micro-EDM) process has been developed, which uses the behavior of discharge pulses. The fuzzy logic applies a heuristic approach in dealing with the non-linear and time varying nature of the micro-EDM process. The control parameters affecting the performance of micro-EDM include gap voltage and gap current discharge pulses. It is important to discriminate between different levels of pulses for proper operation of micro-EDM. Discrimination of pulses from an RC-type power source is still an ill-defined problem relying on heuristics. The choice of appropriate values of discharge pulses is generally based on the knowledge and experiences of operators. In this study experiments were carried out to distinguish the discharge pulses, which were then classified into open, sparking, arcing, off and short circuit. The classified pulses were utilized as the input fuzzy sets of the fuzzy logic based controller that drives the servo system to maintain the desired gap width. The simulated results obtained demonstrate that the fuzzy logic controller is able to provide stable machining and improve the performance of the micro-EDM process.

Selection of optimum fish oil fuel blend to reduce the greenhouse gas emissions in an IC engine—A hybrid multiple criteria decision aid approach

ABSTRACT The increasing demand on energy due to population growth and rising of living standards has led to considerable use of fossil fuels which has in turn, had an adverse impact on environmental pollution and depletion of fossil fuels in Internal Combustion (IC) engine sector. Alternative fuel blend evaluation in IC engine fuel technologies is a very important strategic decision involving decisions balancing within a number of criteria and opinions from different decision maker of IC engine experts. The selection of appropriate source of biodiesel and proper blending of biodiesel plays a major role in alternate energy production. This paper describes an application of hybrid Multi Criteria Decision Making (MCDM) technique for the selection of optimum biodiesel blend in the IC engine. The proposed model, Analytical Network Process (ANP) is integrated with Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) to evaluate the optimum blend. Here the ANP is used to determine the relative weights of the criteria, whereas TOPSIS is used for obtaining the final ranking of alternative blends. An efficient pair-wise comparison process and ranking of alternatives can be achieved for optimum blend selection through the integration of ANP and TOPSIS. The obtained preference order for the blends are as B20 > B40 > Diesel > B60 > B80 > B100. This paper highlights a new insight into MCDM techniques to evaluate the best fuel blend for the decision makers such as engine manufactures and R&D engineers to meet the fuel economy and emission norms to empower the green revolution.