Investigating the effects of electric discharge machining parameters on material removal rate and surface roughness on AISI D2 steel using RSM-GRA integrated approach

Hanif, Muhammad; Wasim, Ahmad; Hussain, Salman; Jahanzaib, Mirza; Shah, Abdul Hakim

doi:10.1007/s00170-018-3019-1

Cited by 42 publications

(11 citation statements)

References 28 publications

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“…This method compensated for the gap that other studies may only partially consider or the studies that may only consider one or several factors. Similar approaches have been used in different study fields, such as chemical materials and food safety [33,34].…”

Section: Discussionmentioning

confidence: 99%

Nitrogen Fertilizer Levels Affect the Growth and Quality Parameters of Astragalus mongolica

et al. 2020

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Owing to overexploitation, wild resources of Astragalus mongolica, a Chinese herbal plant that is widely distributed in the arid and semi-arid areas of Northern China, have gradually become exhausted, and therefore, commercial cultivation is increasingly important to meet the growing demand for astragalus and reduce the pressure on wild populations. Nitrogen level is an important factor that affects the yield and quality of A. mongolica. However, uniform standards for fertilization among production areas have not yet been determined. In this study, the effect of nitrogen fertilizer treatment on the yield and quality of A. mongolica in the Qinghai-Tibet Plateau was explored using a control treatment (no added nitrogen, N0) and five different nutrient levels: 37.5 kg/ha (N1), 75 kg/ha (N2), 112.5 kg/ha (N3), 150 kg/ha (N4), and 187.5 kg/ha (N5). According to grey relational analysis, the optimal nitrogen fertilizer treatment was the N4 level followed by the N5 and N2 levels. Nitrogen fertilizer significantly increased the root biomass, plant height, root length, and root diameter. However, nitrogen fertilization had no significant effect on the content of Astragaloside IV and mullein isoflavone glucoside. The content of ononin and calycosin continually accumulated throughout the growing period. The results showed that the ononin and calycosin content under N4 and N2 is higher than other levels and there is not significantly different between different nitrogen fertilizer levels about them. The content of formononetin decreased gradually with the progression of the growing season. The optimal nitrogen fertilizer treatment for A. mongolica is recommended to be 150 kg/ha and the content of active compounds and yield were observed to reach the maximum in October.

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Section: Discussionmentioning

confidence: 99%

Nitrogen Fertilizer Levels Affect the Growth and Quality Parameters of Astragalus mongolica

et al. 2020

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“…Since the thermal energy required by the EDM process is generated through the applied electrical energy, the electrical parameters are crucial in improving the efficiency of electrical discharge machining [21]. In addition to the various electrical factors, non-electronic parameters are also needed much attention for the EDM process [22]. Table 1 summarizes the recent papers published in EDM-based work using various types of non-electronic parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent System for Improving Electric Discharge Machining Efficiency Using Artificial Neural Network and Adaptive Control of Debris Removal Operations

Lee

Lai

2021

IEEE Access

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Electrical discharge machining (EDM) can effectively solve the shortcomings of traditional machining processes that cannot process special materials, so it is widely used on workpieces with strong hardness materials, such as titanium alloys and tool steels to produce various molds and dies. However, the operating procedures of EDM are quite complicated and low machining productivity. To improve machining efficiency, this study develops an intelligent system that adaptively controls debris removal operations instead of using preset debris removal parameters. A feature extraction method is proposed in this study to effectively identify the machining states from streaming images of the machining curve for evaluating the appropriate time of the debris removal operation. Then, the extracted features feed into the artificial neural network model to establish a debris removal predicted model. The preliminary experimental result shows that the established predicted model can achieve an accuracy of 96.93% for a testing dataset containing 750 machining curve images. To further verify the effectiveness of the proposed intelligent system in improving EDM efficiency, we integrate the debris removal predicted model into the EDM machine and test it on the manufacturing site. Compared with the preset debris removal parameter, the proposed intelligent system can save nearly 38.60% of machining time for the machining depth of 6.45mm under specific EDM conditions. INDEX TERMS Artificial intelligence, debris removal operation, electric discharge machining, intelligent system, machining efficiency.

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“…Once the desirability of individual performance measure has been obtained, they were combined into a single value using geometric mean. The desirability functions for maximizing MRR, minimizing EWR and SR and combined desirability geometric mean (CDGM) have been presented in Equations (8), (9) and (10) respectively [51][52][53].…”

Section: Parametersmentioning

confidence: 99%

Parametric Modelling and Multi-Objective Optimization of Electro Discharge Machining Process Parameters for Sustainable Production

et al. 2019

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Electro Discharge Machining (EDM) can be an element of a sustainable manufacturing system. In the present study, the sustainability implications of EDM of special-purpose steels are investigated. The machining quality (minimum surface roughness), productivity (material removal rate) improvement and cost (electrode wear rate) minimization are considered. The influence and correlation of the three most important machining parameters including pulse on time, current and pulse off time have been investigated on sustainable production. Empirical models have been established based on response surface methodology for material removal rate, electrode wear rate and surface roughness. The investigation, validation and deeper insights of developed models have been performed using ANOVA, validation experiments and microstructure analysis respectively. Pulse on time and current both appeared as the prominent process parameters having a significant influence on all three measured performance metrics. Multi-objective optimization has been performed in order to achieve sustainability by establishing a compromise between minimum quality, minimum cost and maximum productivity. Sustainability contour plots have been developed to select suitable desirability. The sustainability results indicated that a high level of 75.5% sustainable desirability can be achieved for AISI L3 tool steel. The developed models can be practiced on the shop floor practically to attain a certain desirability appropriate for particular machine limits.

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Investigating the effects of electric discharge machining parameters on material removal rate and surface roughness on AISI D2 steel using RSM-GRA integrated approach

Cited by 42 publications

References 28 publications

Nitrogen Fertilizer Levels Affect the Growth and Quality Parameters of Astragalus mongolica

Nitrogen Fertilizer Levels Affect the Growth and Quality Parameters of Astragalus mongolica

An Intelligent System for Improving Electric Discharge Machining Efficiency Using Artificial Neural Network and Adaptive Control of Debris Removal Operations

Parametric Modelling and Multi-Objective Optimization of Electro Discharge Machining Process Parameters for Sustainable Production

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