Edm Process Parameter Optimization for Efficient Machining of Inconel-718

Singh, Ankit; Ghadai, Ranjan Kumar; Kalita, Kanak; Chatterjee, Prasenjit; Pamučar, Dragan

doi:10.22190/fume200406035s

Cited by 30 publications

(19 citation statements)

References 17 publications

(21 reference statements)

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“…Compared with other MCDM methods, such as Decision-making Trial and Evaluation Laboratory(DEMATEL), VIKOR, Multi-Attribute Border Approximation Area Comparison(MABAC), the TOPSIS method has a better result in index ranking [ 53 ]. Moreover, TOPSIS is a widely used MCDM method, which has been used by many scholars [ 8 , 12 ]. The calculation process of TOPSIS is clear and can measure the relative performance of each solution in simple mathematical form [ 54 ].…”

Section: Methodsmentioning

confidence: 99%

“…The calculation process of TOPSIS is clear and can measure the relative performance of each solution in simple mathematical form [ 54 ]. The EW method can effectively improve the performance of TOPSIS through discrete probability distribution [ 12 ]. The evaluation accuracy can be enhanced by combining the EW method and TOPSIS method.…”

Section: Methodsmentioning

confidence: 99%

“…[ 8 ] establish the objective and measurable patterns to evaluate the shallow geothermal energy implementation through EW method and TOPSIS method. [ 12 ] developed TOPSIS and five different strategies to optimize the electro-discharge machining process. [ 13 ] illustrate a new hybrid methodology for the selection of offshore wind power station locations based on MCDM.…”

Section: Literature Reviewmentioning

confidence: 99%

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A social network analysis in dynamic evaluate critical industries based on input-output data of China

Wang

Yang

2022

PLoS ONE

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As the Chinese economy grows, the imbalance of industrial structure is prominent, and the optimization of industrial structure has become an urgent problem. Evaluation of industry is an important step in industry optimization. To this end, this study proposes an integrated evaluation method combining social network analysis (SNA) and the multi-criteria decision making (MCDM) method. Specifically, SNA method are used to calculate indicators, the measurement weights are calculated by the Entropy Weight (EW) Method, and the rank of each industry is determined by the TOPSIS method. Critical industries are identified based on China’s input-output data from 2002 to 2017. The results indicate that Manufacturing Industry and the Metal products have a high evaluation, but the Research and Development have a low evaluation value at all times. According to the results, we suggest that the government should optimize the allocation of resources and promote the transfer of resources to balance industrial development.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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A social network analysis in dynamic evaluate critical industries based on input-output data of China

Wang

Yang

2022

PLoS ONE

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“…For the Entropy method, a statistical approach to assessing the probability of states is proposed, which partially eliminates the contradictions of the formal method. The essence of the integrated methods consists in combining the weighing results obtained by different methods with the subsequent use of the normalization of values by the Sum-method (Ma et al, 1999;Xu, 2004;Ustinovičius, 2001;Jahan et al, 2012;Vinogradova et al, 2018;Đalić et al, 2020;Singh et al, 2020). It is believed that integration can overcome the disadvantages of subjective and objective weighing methods.…”

Section: Introductionmentioning

confidence: 99%

Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD

Mukhametzyanov

2021

Decis. Mak. Appl. Manag. Eng.

129

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The comparative analysis of objective methods for determining the weights of criteria in the problems of multi-criteria decision-making is carried out. It is shown that the use of methods for determining the weights of criteria, based on formal processing of the decision matrix (Entropy, CRITIC, Standard deviation) for MCDM problems in some cases is not correct. It is demonstrated that the Entropy weighting method (EWM) is highly sensitive to evaluation of probabilities of states based on the decision matrix. For the Entropy method two modifications of estimation of probabilities of states are proposed that partially eliminate the contradictions of the basic EWM method. The first modification (EWM.df) is based on a statistical approach and it estimates the probabilities of states based on attribute distribution function. The second modification (EWM.dsp) estimates the probabilities of states based on the relative dispositions of attributes. Two options both have their supporting rationale. The analysis of integrated weighing methods is carried out and various options for aggregation of weights are given. An integrated EWM-Corr-method is proposed which allows to re-allocate the weights obtained by the Entropy method among correlated criteria.

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“…It is an NTM where an electric spark is used to erode material immersed in dielectric fluid. It works with electrically conductive material [1]. The principle behind EDM is the ability to control sparks to erode materials.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining

Shanmugasundar¹,

Vanitha²,

Čep

et al. 2021

Processes

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Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs involve several process parameters, the appropriate tweaking of which is necessary to obtain economical and suitable results. However, the costly and time-consuming nature of the NTMs makes it a tedious and expensive task to manually investigate the appropriate process parameters. The NTM process parameters and responses are often not linearly related and thus, conventional statistical tools might not be enough to derive functional knowledge. Thus, in this paper, three popular machine learning (ML) methods (viz. linear regression, random forest regression and AdaBoost regression) are employed to develop predictive models for NTM processes. By considering two high-fidelity datasets from the literature on electro-discharge machining and wire electro-discharge machining, case studies are shown in the paper for the effectiveness of the ML methods. Linear regression is observed to be insufficient in accurately mapping the complex relationship between the process parameters and responses. Both random forest regression and AdaBoost regression are found to be suitable for predictive modelling of NTMs. However, AdaBoost regression is recommended as it is found to be insensitive to the number of regressors and thus is more readily deployable.

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Edm Process Parameter Optimization for Efficient Machining of Inconel-718

Cited by 30 publications

References 17 publications

A social network analysis in dynamic evaluate critical industries based on input-output data of China

A social network analysis in dynamic evaluate critical industries based on input-output data of China

Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD

A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining

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