Journal ArticleDOI
Multi-response optimization of Micro-EDM process parameters on AISI304 steel using TOPSIS
R. Manivannan,M. Pradeep Kumar +1 more
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TLDR
In this paper, the TOPSIS method was used to analyze the process parameters of the micro-Electrical discharge machining (micro-EDM) of an AISI 304 steel with multi-performance characteristics.Abstract:
The Technique for order preference by similarity to ideal solution (TOPSIS) method of optimization is used to analyze the process parameters of the micro-Electrical discharge machining (micro-EDM) of an AISI 304 steel with multi-performance characteristics. The Taguchi method of experimental design L27 is performed to obtain the optimal parameters for inputs, including feed rate, current, pulse on time, and gap voltage. Several output responses, such as the material removal rate, electrode wear rate, overcut, taper angle, and circularity at entry and exit points, are analyzed for the optimal conditions. Among all the investigated parameters, feed rate exerts a greater influence on the hole quality. ANOVA is employed to identify the contribution of each experiment. The optimal level of parameter setting is maintained at a feed rate of 4 μm/s, a current of 10 A, a pulse on time of 10 μs, and a gap voltage of 10 V. Scanning electron microscope analysis is conducted to examine the hole quality. The experimental results indicate that the optimal level of the process parameter setting over the overall performance of the micro-EDM is improved through TOPSIS.read more
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Journal ArticleDOI
Applications of TOPSIS Algorithm on various Manufacturing Processes: A Review☆
TL;DR: Different manufacturing processes are optimized by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm as discussed by the authors, and the main focus of this review paper is on the optimization of the various manufacturing processes that have been optimized by TOPSIS method.
Journal ArticleDOI
Application of TOPSIS to Taguchi method for multi-characteristic optimization of electrical discharge machining with titanium powder mixed into dielectric fluid
TL;DR: In this paper, the authors used the Taguchi-TOPSIS method to optimize material removal rate (MRR), surface roughness (SR), and the micro-hardness of a machined surface (HV) in electrical discharge machining of die steels in dielectric fluid with mixed powder.
Journal ArticleDOI
Influence of process factors on surface measures on electrical discharge machined stainless steel using TOPSIS
Journal ArticleDOI
Assessment of critical failure factors (CFFs) of Lean Six Sigma in real life scenario: Evidence from manufacturing and service industries
TL;DR: In this article, the authors identified 44 critical failure factors (CFFs) of Lean Six Sigma (LSS) projects and ranked them through decision makers' ratings and weight for each factor.
Journal ArticleDOI
Measurement and optimization of multi-response characteristics in plasma arc cutting of Monel 400™ using RSM and TOPSIS
TL;DR: In this article, the effect of PAC parameters such as arc current, cutting speed, stand-off distance and gas pressure on evaluating the part quality characteristics such as material removal rate (MRR), kerf taper (KT) at top and bottom surface and heat affected zone (HAZ) are investigated.
References
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Book
Multiple Attribute Decision Making: Methods and Applications
TL;DR: In this paper, the authors present a classification of MADM methods by data type and propose a ranking method based on the degree of similarity of the MADM method to the original MADM algorithm.
Journal ArticleDOI
Comparison of weights in TOPSIS models
TL;DR: Several applications of TOPSIS using different weighting schemes and different distance metrics are reviewed, and results of different sets of weights applied to a previously used set of multiple criteria data are compared.
Journal ArticleDOI
Determining the weights of criteria in the ELECTRE type methods with a revised Simos' procedure
TL;DR: The purpose of this paper is to explain why the above method needs to be revised, and to propose a new version that takes into account a new kind of information from the DM and changes certain computing rules of the former method.
Journal ArticleDOI
Optimization of Material Removal Rate in Micro-EDM Using Artificial Neural Network and Genetic Algorithms
TL;DR: This well-trained neural network model is shown to be effective in estimating the MRR and is improved using optimized machining parameters.
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