Optimization of Machining Condition in WEDM for Titanium Grade 6 Using MOORA Coupled with PCA — A Multivariate Hybrid Approach

Majumder, Himadri; Maity, Kalipada

doi:10.1142/s0219686717500068

Cited by 59 publications

(17 citation statements)

References 39 publications

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“…As the range of the response characteristics is very wide therefore normalization converts them into some comparable numbers. This helps to obtain a dimensionless number from the diverse measurement of response which is further utilized for ranking purposes in decision-making problems [19]. The below-mentioned steps were followed to convert the number of contrary responses into a single dimensionless performance index:…”

Section: Optimization Methodologymentioning

confidence: 99%

Multi-Response Optimization in High-Speed Machining of Ti-6Al-4V Using TOPSIS-Fuzzy Integrated Approach

et al. 2020

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Titanium alloys are widely used in various applications including biomedicine, aerospace, marine, energy, and chemical industries because of their superior characteristics such as high hot strength and hardness, low density, and superior fracture toughness and corrosion resistance. However, there are different challenges when machining titanium alloys because of the high heat generated during cutting processes which adversely affects the product quality and process performance in general. Thus, optimization of the machining conditions while machining such alloys is necessary. In this work, an experimental investigation into the influence of different cutting parameters (i.e., depth of cut, cutting length, feed rate, and cutting speed) on surface roughness (Rz), flank wear (VB), power consumption as well as the material removal rate (MRR) during high-speed turning of Ti-6Al-4V alloy is presented and discussed. In addition, a backpropagation neural network (BPNN) along with the technique for order of preference by similarity to ideal solution (TOPSIS)-fuzzy integrated approach was employed to model and optimize the overall cutting performance. It should be stated that the predicted values for all machining outputs demonstrated excellent agreement with the experimental values at the selected optimal solution. In addition, the selected optimal solution did not provide the best performance for each measured output, but it achieved a balance among all studied responses.

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Section: Optimization Methodologymentioning

confidence: 99%

Multi-Response Optimization in High-Speed Machining of Ti-6Al-4V Using TOPSIS-Fuzzy Integrated Approach

et al. 2020

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“…Multi-criteria decision making (MCDM) is one of the heuristic techniques to select the best cutting parameters. Literatures where MCDM techniques such as TOPSIS [18][19][20], VIKOR [21,22], MOORA [23][24][25], ARAS [26] and others [27][28][29] are used in selecting the best machining parameters. Meta-heuristic techniques such as non-dominated sorting algorithm-II [30][31][32], particle swarm optimization [33,34], teaching-learning based optimization [35], artificial bee colony algorithm [11] etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface methodology

Singh

Hussain

Dash

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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Composite is an artificial multiphase material that constituent phases which are chemically dissimilar and separated by a distinct interface. As a result of such anisotropic and non-homogeneous formation machining of composites is a strenuous task. Therefore, the need arose for formulating a stable predictive model. The comprehensive intention of this study is to develop a highly robust and stable predictive soft-computing model for forecasting the machining performance of Al-5083 alloy reinforced with B 4 C particles (Al5083/B 4 C). The study mostly emphasize on selecting the best machining parameters among the conducted experiments and evaluating the optimal machining parameters for Al5083/B 4 C composite in wireelectro discharge machin. The paper is an integration of equally weighted experimental as well as computational study. In the experimental part of the study, 5 different specimen of Al5083/B 4 C is prepared by the ex situ technique through stir casting process. The experimental part includes design of experiment by Taguchi's method. The computational part of the study comprised of three different stages. The first stage involves the mathematical modelling of the performance measures and statistical scrutiny of the models. In the second stage, the best machining parameters are selected based on the fuzzy IFthen rules. The final stage of the manuscript is the trade-off analysis conducted to obtain the optimal machining parameters. In order to test the robustness of the formulated model an experimental validation is carried out at the optimal machining combination. The error calculated from the comparison is within the range of 2-5% which justifies the objective of the study.

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“…SiC Ceramic as a membrane for water filtration is very easy to use and clean. They are extremely hard and durable [1,2]. Due to the high usage of SiC Ceramic-composite in industrial practice, attention has been also focused on the improvement in their physical properties by fabricating ceramic composite.…”

Section: Introductionmentioning

confidence: 99%

Multi-optimization of process parameters for machining of a non-conductive SiC ceramic composite by non-conventional machining method

Chaudhury

Samantaray

2020

Manufacturing Rev.

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The objective of the study is to predict the optimized set of the input parameters for the machining of non-conductive silicon carbide (SiC) by electric discharge machining (EDM). The insulated SiC ceramic composite machining was performed with 4 volumes (by percentage) of carbon nano (CNT) the SiC, which makes it electrically conductive. SiC has very good mechanical properties due to its widespread application in the aerospace, MEMS, and bio-sensor industries. This application requires a highly precise machining hole with a good surface quality that can be processed by machining processes such as EDM. The input parameters in this study are differing by three levels and the experimentation has been done by L27 orthogonal array. Four output parameters such as material removal rate (MRR), plasma flushing efficiency (PFE), surface-roughness (SR) and recast layer thickness (Rlt) for has been calculated for the detailed experimental analyses. In this research, a comparative analysis between the Multi-attribute management mechanisms (MADM) i.e. WPCA, MOORA & MOORA, and WPCA was conducted. The statistical analysis was also conducted to determine the impact of input parameters on performance measures. The study concluded that by integrating MOORA 's method with a PCA, the highest MRRs of 2.56 mm3/min & 78% PFE, lowest SR 2.1 µm, and Rlt 2.56 µm were obtained, with an experimental testing error of 5 percent.

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Optimization of Machining Condition in WEDM for Titanium Grade 6 Using MOORA Coupled with PCA — A Multivariate Hybrid Approach

Cited by 59 publications

References 39 publications

Multi-Response Optimization in High-Speed Machining of Ti-6Al-4V Using TOPSIS-Fuzzy Integrated Approach

Multi-Response Optimization in High-Speed Machining of Ti-6Al-4V Using TOPSIS-Fuzzy Integrated Approach

Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface methodology

Multi-optimization of process parameters for machining of a non-conductive SiC ceramic composite by non-conventional machining method

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