ANN MODELLING OF SMALL HOLE DRILLING ON MONEL METAL BY USING ELECTRICAL DISCHARGE MACHINING

The selection of best combination of the process parameters in small hole drilling by Electrical Discharge Machining for an optimum material removal rate with a reduced tool wear rate can reduce machining time and yield better performances. Artificial Neural Network (ANN) has emerged as a powerful tool for modelling complex processes is used for achieving better performance parameter. Artificial Neural Network (ANN) with back propagation algorithm have been used for optimizing and modelling process. The experiments have been designed according to Taguchi L9 orthogonal array. The input parameters were considered for conducting experimentation are namely Discharge Current, Pulse off time and Pulse on time respectively. The performance measures were Material Removal Rate (MRR) and Tool Wear Rate (TWR). ANN models have been developed with varying number of neurons in the hidden layer from 5 to 10. It was found that one hidden layer with 9 neurons predicted the best results. The predicted values were compared with actual experimental results and the predicted values were almost equal to the expected with very less error.Â

MAINTENANCE STRATEGY EVALUATION BASED ON AHP – TOPSIS

This paper presents an application of Analytical Hierarchy Process (AHP) is combined with Technique for Order Preference by Similarly to Ideal Solution (TOPSIS) model for selection of the best maintenance strategy for pump in paper industry. AHP is used to compute the criteria weights whereas TOPSIS is used to ranking the maintenance strategy alternatives. This study focuses on four maintenance strategies such as Corrective Maintenance (CM), Predictive maintenance (PM), Time based preventive Maintenance (TM) & Condition Based Maintenance (CBM) and four main criteria such as safety, cost, added value and feasibility are used to evaluate the optimum maintenance strategy

Parametric optimization for the production of nanostructure in high carbon steel chips via machining

Nano crystalline materials are an area of interest for the researchers all over the world due to its superior mechanical properties such as high strength and high hardness. But the cost of nano-crystals is high because of the complexity and cost incurred during its production. This paper focuses on the application of Taguchi method with Fuzzy logic for optimizing the machining parameters of nano-crystalline structured chips production in High Carbon Steel (HCS) through machining. An orthogonal array, multi-response performance index, signals to noise ratio and analysis of variance are used to study the machining process with multi-response performance characteristics. The machining parameters namely rake angle, depth of cut, heat treatment, feed and cutting velocity are optimized with considerations of the multi-response performance characteristics. Using the Taguchi and Fuzzy logic method optimum cutting conditions are identified in order to obtain the smallest nanocrystalline structure via machining

A hybrid multi-criteria decision modeling approach for the best biodiesel blend selection based on ANP-TOPSIS analysis

The ever increasing demand and depletion of fossil fuels had an adverse impact on environmental pollution. 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 fuel blend in fish oil biodiesel for the IC engine. The proposed model, Analytical Network Process (ANP) is integrated with Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (in Serbian) (VIKOR) to evaluate the optimum blend. Evaluation of suitable blend is based on the exploratory analysis of the performance, emission and combustion parameters of the single cylinder, constant speed direct injection diesel engine at different load conditions. Here the ANP is used to determine the relative weights of the criteria, whereas TOPSIS and VIKOR are 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 with TOPSIS and VIKOR. The obtained preference order of the blends for ANP-VIKOR and ANP-TOPSIS are B20 > Diesel > B40 > B60 > B80 > B100 and B20 > B40 > Diesel > B60 > B80 > B100 respectively. Hence by comparing both these methods, B20 is selected as the best blend to operate the internal combustion engines. 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