Perspective and Prospects of Wire Electric Discharge Machining (WEDM)

Wire Electric Discharge Machining (WEDM) is a non-traditional machining method that is widely used in the manufacture of aerospace/aircraft and medical equipment for conductive materials. WEDM products are expected to have good dimensional accuracy, surface roughness, and geometry. Many researchers have done experiments on various materials to optimize the process, which has many parameters and response characteristics. This paper provides an overview of the WEDM process on alloy steels in order to understand the impact of input process variables on output responses and optimization techniques for selecting optimal process parameters. This paper also highlights WEDM process trends as well as workpiece materials, wire varieties, wire diameters, and optimization approaches. This work is expected to be useful in initiating further research on WEDM by documenting substantial research works confirming the latest scenario

Perspective and Prospects of Wire Electric Discharge Machining (WEDM)

Wire Electric Discharge Machining (WEDM) is a non-traditional machining method that is widely used in the manufacture of aerospace/aircraft and medical equipment for conductive materials. WEDM products are expected to have good dimensional accuracy, surface roughness, and geometry. Many researchers have done experiments on various materials to optimize the process, which has many parameters and response characteristics. This paper provides an overview of the WEDM process on alloy steels in order to understand the impact of input process variables on output responses and optimization techniques for selecting optimal process parameters. This paper also highlights WEDM process trends as well as workpiece materials, wire varieties, wire diameters, and optimization approaches. This work is expected to be useful in initiating further research on WEDM by documenting substantial research works confirming the latest scenario

Multi-Response Optimization of WEDM Process Parameters for Machining of Superelastic Nitinol Shape-Memory Alloy Using a Heat-Transfer Search Algorithm

Nitinol, a shape-memory alloy (SMA), is gaining popularity for use in various applications. Machining of these SMAs poses a challenge during conventional machining. Henceforth, in the current study, the wire-electric discharge process has been attempted to machine nickel-titanium (Ni55.8Ti) super-elastic SMA. Furthermore, to render the process viable for industry, a systematic approach comprising response surface methodology (RSM) and a heat-transfer search (HTS) algorithm has been strategized for optimization of process parameters. Pulse-on time, pulse-off time and current were considered as input process parameters, whereas material removal rate (MRR), surface roughness, and micro-hardness were considered as output responses. Residual plots were generated to check the robustness of analysis of variance (ANOVA) results and generated mathematical models. A multi-objective HTS algorithm was executed for generating 2-D and 3-D Pareto optimal points indicating the non-dominant feasible solutions. The proposed combined approach proved to be highly effective in predicting and optimizing the wire electrical discharge machining (WEDM) process parameters. Validation trials were carried out and the error between measured and predicted values was negligible. To ensure the existence of a shape-memory effect even after machining, a differential scanning calorimetry (DSC) test was carried out. The optimized parameters were found to machine the alloy appropriately with the intact shape memory effect

The Impact of COVID-19 Shocks on Business and GDP of Global Economy

This study examines the relationship between COVID-19 shocks and GDP loss of different countries worldwide based on the seven scenarios of the epidemiological DSGE/CGE model of [McKibbin, W., & Fernando, R. (2020). The Global Macroeconomic Impacts of COVID-19: Seven Scenarios. Asian Economic Papers, 20(2): 1-30, MIT Press]. We implemented a panel data approach for 24 cross-sectional units with three periods and a general regression neural network. The economic and financial shocks consist of labor supply, equity risk premium, consumption demand, and government expenditure. The findings show that the consumption demand and equity risk premium shocks on GDP are more influential than the other shocks. Moreover, the results reveal that the most significant GDP loss is associated with Japan, Germany, and the US, respectively, which are industrialized countries with the most prominent automobile manufacturers. The lowest GDP loss is linked to Saudi Arabia, one of the world\u27s biggest oil producer countries