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Many engineering applications in this era require new age materials; however, some classic alloys like spring steel are still used in critical applications such as aerospace, defense and automobile. To machine spring steel material, there exist various difficulties such as rapid tool wear rate, the rough surface formation of a workpiece and higher power consumption. The purpose of this paper is to address these issues, various approaches in addition to electrical discharge machines (EDM) are used such as dry EDM (DEDM) and near dry EDM (NDEDM).

This study focuses on these two approaches and their comparative analysis with respect to tool wear during machining of spring steel material. For this study, current, gap voltage, cycle time and dielectric medium pressure are considered input variables. This study shows that the near dry EDM approach yields better results. Hence, the thermo-electrical model for this approach is developed using ANSYS workbench, which is further validated by comparing with experimental results. This thermo-electrical model covers spark radius variation and formation of temperature profile due to electric discharge. Transient thermal analysis is used to simulate the electric discharge machining.

It is observed from this study that discharge environment parameters such as debris concentration and fluid viscosity largely influences the dielectric fluid pressure value. Experimental results revealed that NDEDM yields better results in comparison with DEDM as it shows a 25% lesser tool wear rate in NDEDM.

The range of predicted results and the experimental results are in close agreement, authenticating the model.

A literature review revealed that government of various developing economies have put an effort on health-care supply chain through the executing critical factors (CFs) directly. Although they have attained some significant benefits in this tactic, but it was not up to satisfactory level. One of the reasons can be attributed to the fact that government/policy makers are not quantifying the impact of CFs on health-care supply chain. This paper aims to propose a methodology to quantify and estimate the impact of CFs on government-supported health-care supply chain (GHSC).

The Graph Theoretic Approach is proposed for estimating the impact and utility of CFs on an Indian GHSC. This study is also extended to scenario analysis for comparing results with different performance situation.

The results obtained from this study show that performance of Indian GHSC is satisfactory, but performance gaps exist which need to be reduced. In this research work, 12 CFs are identified under two significant categories (SCs), i.e. enablers and barriers and the intensity of enablers and barriers have been calculated to show the impact or influence of CFs on GHSC. The value of intensity shows that the role or impact of enabler category (i.e. performance measurement, employee recognition and reward, technology adoption, training cell, inbuilt analytical tool for IT system) is higher on Indian GHSC in comparison to barriers category to enhance the performance of GHSC.

The obtained numerical results are completely in specific to the Indian perspective only; hence, they cannot be generalized for other countries. Simultaneously, this study is related to government supported health-care system; hence, the selection of expert panel was crucial due to the unavailability of doctors and other stakeholders of government system.

The proposed approach is aimed at providing a procedure for evaluating the impact of CFs on HSC in general and GHSC in specific. This study is an attempt to assist government and top management of GHSC to assess the impact of CFs on GHSC and accordingly define its course of actions.

Although various issues related to the CFs have been broadly identified and analyzed, no dedicated study has been reported in the field for quantification of impacts of CFs. Furthermore, this proposed model has an ability to recognize the specific contribution of each CF and overall contribution.