Multicharacteristics optimization of electrical discharge micro hole drilling in Mg alloy using hybrid approach of GRA–regression–PSO

Ahuja, Neeraj; Batra, Uma; Kumar, Kaushal

doi:10.1108/gs-03-2020-0029

Cited by 13 publications

(4 citation statements)

References 28 publications

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“…The results showed that the suggested optimization approach may adapt to the automated environment in both scenarios of externally imposed material or processing performance. For optimizing the multi-objective performance of the circularity at the entrance, the hole overcut, and the circularity at the exit of drilled micro holes, a hybrid approach of the PSO integrating gray relational analysis was used in the process of micro hole drilling in Mg alloy [107]. Regarding the prediction of micro-hole quality (radial over-cutting, recast layer thickness, and MRR) on Inconel 718 superalloy treated by micro-EDM, Rao [108] suggested a prediction model based on the bionic intelligent hybrid algorithm.…”

Section: Parameters Optimizationmentioning

confidence: 99%

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Chen

et al. 2023

Metals

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Electrical discharge machining (EDM) can use soft tool electrodes to process hard workpieces to achieve “soft against hard”, because it directly uses electrical energy and thermal energy to remove metal materials. Then, it can generate complex features on harder materials and meet the requirements of excellent surface quality. Since EDM involves many process parameters, including electrical parameters, non-electrical parameters, and materials properties, it is essential to optimize its process parameters to obtain good performance. In this direction, the application of the swarm intelligence (SI) technique has become popular. In this paper, the existing literature is comprehensively reviewed, and the application of the SI technique in the optimization of EDM process parameters is summarized. Sinker-EDM (SEDM), wire-EDM (WEDM), and micro-EDM (MEDM) with various hybrid techniques are among the EDM methods considered in this study because of their broad adoption in industrial sections. The fundamental nature of all review articles will assist engineers/workers in determining the process parameters and processing performance, the SI algorithm, and the optimal technique by which to obtain the desired process parameters. In addition, discussions from the perspectives of the similarity, individuality, and complementarity of various SI algorithms are proposed, and necessary outlooks are predicted, which provides references for the high performance of the EDM processes in the future.

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Section: Parameters Optimizationmentioning

confidence: 99%

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Chen

et al. 2023

Metals

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“…There are two types of optimization techniques: the first one is statistical, and the second is an artificial intelligence technique. These include Taguchi technique [20][21][22][23], response surface methodology [24][25][26], gray relational analysis [27][28][29], utility concept [30], entropy measurement technique [31,32], genetic algorithm [33], non-dominated genetic algorithm [34,35], particle swarm optimization [36,37], teaching learning-based optimization [25], artificial neural network [38,39], gray wolf optimization [40,41], etc. The above-mentioned techniques are used for the planning of experiments, modeling, and optimization of the machining indicators.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Perspective to the Investigation of Surface Integrity of Al/SiC/Gr Composite on EDM

Abbas,

Sharma,

Al-Bahkali

et al. 2023

JMMP

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Conventional mechanical machining of composite is a challenging task, and thus, electric discharge machining (EDM) was used for the processing of the developed material. The processing of developed composite using different electrodes on EDM generates different surface characteristics. In the current work, the effect of tool material on the surface characteristics, along with other input parameters, is investigated as per the experimental design. The experimental design followed is an RSM-based Box–Behnken design, and the input parameters in the current research are tool material, current, voltage, pulse-off time, and pulse-on time. Three levels of each parameter are selected, and 46 experiments are conducted. The surface roughness (Ra) is investigated for each experimental setting. The machine learning approach is used for the prediction of surface integrity by different techniques, namely Xgboost, random forest, and decision tree. Out of all the techniques, the Xgboost technique shows maximum accuracy as compared to other techniques. The analysis of variance of the predicted solutions is investigated. The empirical model is developed using RSM and is further solved with the help of a teaching learning-based algorithm (TLBO). The SR value predicted after RSM and integrated approach of RSM-ML-TLBO are 2.51 and 2.47 µm corresponding to Ton: 45 µs; Toff: 73 µs; SV:8V; I: 10A; tool: brass and Ton: 47 µs; Toff: 76 µs; SV:8V; I: 10A; tool: brass, respectively. The surface integrity at the optimized setting reveals the presence of microcracks, globules, deposited lumps, and sub-surface formation due to different amounts of discharge energy.

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“…The grey system theory established by Professor Deng Julong provides a theoretical basis for studying the system of “partial information known and partial information unknown” (Deng, 1982). Its main research contents include grey algebra (Liu et al , 2010; Su et al , 2017; Aydemir, 2020; Mierzwiak et al , 2021), grey relational analysis (Yang et al , 2021a; Zhong et al , 2021; Cao et al , 2021), grey cluster (Luo et al , 2021), grey prediction (Li et al , 2021; Yang et al , 2021b; Quartey-Papafio et al , 2021), grey decision-making (Liu et al , 2017; Ma et al , 2021; Ali et al , 2022), grey control (Ahuja et al , 2021; Yang and Xue, 2017), grey programming (Mahmoudi et al , 2018; Asghari et al , 2021; Darvishi et al , 2021) and so on. Since the establishment of grey system theory, many scholars have paid great attention and obtained rich research results.…”

Section: Introductionmentioning

confidence: 99%

The method and its applications of determining possibility function of grey set

Li²

2022

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PurposeGrey set is the important foundation of the grey mathematics and grey system theory, and the possibility function is the way of expressing grey set. This paper aims to establish the method of determining the possibility function of grey set and discusses its extended applications.Design/methodology/approachFirst, the grey kernel and the grey support set of grey set are defined, and the properties of grey kernel are analyzed. Second, according to the decomposition theorem of grey set, a method of determining the possibility function of grey set is put forward in this paper, which is called the method of increasing information and taking maximum and minimum (IITMM), and then it is further simplified as the method of increasing information and taking maximum (IITM), and an simple example is given to illustrate the calculation procedure. Finally, the grey information cluster method (GICM) based on IITM is proposed and applied to the ecological and geographical environment analysis of pine caterpillar.FindingsThe results show that the grey kernel of grey set still has grey uncertainty; the method of IITM has simple calculation and strict mathematical basis, and it can synthesize the information of the research object and accords with the principle of using minimum information; the GICM and the fuzzy cluster method have the same classification effect.Practical implicationsThe researches show that method of IITM can be used not only to determine the possibility function of the grey set effectively, but also be used for the evaluation and cluster analysis of connotative objects. The classification result of the GICM presented in this paper is more precise than that of the fuzzy cluster method.Originality/valueThe paper succeeds in realizing both the IITM method for determining the possibility function of grey set and the GICM based on IITM for the connotative objects.

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Multicharacteristics optimization of electrical discharge micro hole drilling in Mg alloy using hybrid approach of GRA–regression–PSO

Cited by 13 publications

References 28 publications

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

A Machine Learning Perspective to the Investigation of Surface Integrity of Al/SiC/Gr Composite on EDM

The method and its applications of determining possibility function of grey set

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