Nonconventional or advanced machining processes such as electric discharge machining, electrochemical machining, abrasive water jet machining, and laser beam machining have been widely used as a vi...

Developments in nonconventional machining for sustainable production: A state-of-the-art review:

Experimental comparison of near-dry and cryogenically cooled near-dry machining in wire-cut electrical discharge machining processes

\n Electrical discharge machining (EDM) is very essential unconventional electro-thermal machining process to machine the contour profile of hard materials in modern production industries. The liquid dielectric fluid has been replaced by the gas and minimum quantity of liquid mixed with gas (gas-mist) to encourage the green machining processes. The various gases and gas-mist have been used as the working fluid in dry and near-dry EDM respectively. The research-contextual, various dielectric fluids, sustainable and innovative developments, process parameters, machining characteristics, and optimization techniques applied in various dry and near-dry EDM have been illustrated through an extensive literature survey. Future research opportunities in both dry and near-dry EDM have been summarized to promote eco-friendly EDM research activities.

An Extensive Review on Sustainable Developments of Dry and Near-Dry Electrical Discharge Machining Processes

A review on sustainability, health, and safety issues of electrical discharge machining

Superalloys are typical “difficult to machine” materials, which could be processed by electrical discharge machining (EDM). The studies about the machining performance of superalloys mainly concent...

Machining performance of high energy die-sinking electrical discharge machining on GH2132

Dry electrical discharge machining (EDM) is a modification of the oil EDM process in which the liquid dielectric is replaced by a gaseous dielectric. This study investigates the effects of differen...

Analysis of dry electrical discharge machining in different dielectric mediums

Dry electrical discharge machining (DEDM) is considered as a green format of the conventional oil dielectric EDM where a gas is used as the dielectric medium. This paper details experimental results when DEDM of high speed steel workpieces using multi-response optimisation. Experimental trials were performed to investigate the influence of current, pulse on-time, duty factor, gas pressure and spindle rotational speed on material removal rate (MRR), surface roughness (Ra) and radial over cut (ROC). Grey relational analysis (GRA) based on Taguchi approach was employed for multi-objective optimisation of DEDM responses. Confirmation trials were subsequently conducted and results showed that process responses can be successfully optimised using GRA. Results showed that current, duty factor and air pressure were found to be statistically significant factors at 90% confidence level, with percentage contribution ratios (PCRs) of 72.8%, 4.3% and 3.7% respectively. Micro pores and minor surface cracks were visible in the majority of specimens.

Optimisation of Dry Electrical Discharge Machining of High Speed Steel using Grey-Relational Analysis

Policymakers, practitioners, and researchers around the globe have been acting in a coordinated manner, yet remaining independent, to achieve the seventeen Sustainable Development Goals (SDGs) defined by the United Nations. Remarkably, SDG-centric activities have manifested a huge information silo known as big data. In most cases, a relevant subset of big data is visualized using several two-dimensional plots. These plots are then used to decide a course of action for achieving the relevant SDGs, and the whole process remains rather informal. Consequently, the question of how to make a formal decision using big data-generated two-dimensional plots is a critical one. This article fills this gap by presenting a novel decision-making approach (method and tool). The approach formally makes decisions where the decision-relevant information is two-dimensional plots rather than numerical data. The efficacy of the proposed approach is demonstrated by conducting two case studies relevant to SDG 12 (responsible consumption and production). The first case study confirms whether or not the proposed decision-making approach produces reliable results. In this case study, datasets of wooden and polymeric materials regarding two eco-indicators (CO2 footprint and water usage) are represented using two two-dimensional plots. The plots show that wooden and polymeric materials are indifferent in water usage, whereas wooden materials are better than polymeric materials in terms of CO2 footprint. The proposed decision-making approach correctly captures this fact and correctly ranks the materials. For the other case study, three materials (mild steel, aluminum alloys, and magnesium alloys) are ranked using six criteria (strength, modulus of elasticity, cost, density, CO2 footprint, and water usage) and their relative weights. The datasets relevant to the six criteria are made available using three two-dimensional plots. The plots show the relative positions of mild steel, aluminum alloys, and magnesium alloys. The proposed decision-making approach correctly captures the decision-relevant information of these three plots and correctly ranks the materials. Thus, the outcomes of this article can help those who wish to develop pragmatic decision support systems leveraging the capacity of big data in fulfilling SDGs.

https://www.mdpi.com/2504-2289/6/2/64/pdf?version=1654674382

Saman Fattahi

Papers

Analysis of dry electrical discharge machining in different dielectric mediums

Optimisation of Dry Electrical Discharge Machining of High Speed Steel using Grey-Relational Analysis

Decision-Making Using Big Data Relevant to Sustainable Development Goals (SDGs)

Optimization of Dry Electrical Discharge Machining of Stainless Steel using Big Data Analytics