Gillani Fouzia, Zahid Taiba, Bibi Sameena, Khan Rana Sami Ullah, Bhutta Muhammad Raheel, Ghafoor Usman
Department of Mechanical Engineering and Technology, Government College University, Faisalabad 38000, Pakistan.
Department of Mechanical Engineering, Institute of Space Technology, Islamabad 44000, Pakistan.
Materials (Basel). 2022 Mar 16;15(6):2205. doi: 10.3390/ma15062205.
The electrical discharge machining (EDM) process is one of the most efficient non-conventional precise material removal processes. It is a smart process used to intricately shape hard metals by creating spark erosion in electroconductive materials. Sparking occurs in the gap between the tool and workpiece. This erosion removes the material from the workpiece by melting and vaporizing the metal in the presence of dielectric fluid. In recent years, EDM has evolved widely on the basis of its electrical and non-electrical parameters. Recent research has sought to investigate the optimal machining parameters for EDM in order to make intricate shapes with greater accuracy and better finishes. Every method employed in the EDM process has intended to enhance the capability of machining performance by adopting better working conditions and developing techniques to machine new materials with more refinement. This new research aims to optimize EDM's electrical parameters on the basis of multi-shaped electrodes in order to obtain a good surface finish and high dimensional accuracy and to improve the post-machining hardness in order to improve the overall quality of the machined profile. The optimization of electrical parameters, i.e., spark voltage, current, pulse-on time and depth of cut, has been achieved by conducting the experimentation on die steel D2 with a specifically designed multi-shaped copper electrode. An experimental design is generated using a statistical tool, and actual machining is performed to observe the surface roughness, variations on the surface hardness and dimensional stability. A full factorial design of experiment (DOE) approach has been followed (as there are more than two process parameters) to prepare the samples via EDM. Regression analysis and analysis of variance (ANOVA) for the interpretation and optimization of results has been carried out using Minitab as a statistical tool. The validation of experimental findings with statistical ones confirms the significance of each operating parameter on the output parameters. Hence, the most optimized relationships were found and presented in the current study.
电火花加工（EDM）工艺是最有效的非常规精密材料去除工艺之一。它是一种智能工艺，通过在导电材料中产生火花腐蚀来精确加工硬质金属。火花在工具和工件之间的间隙中产生。这种腐蚀通过在介电流体存在的情况下熔化和汽化金属，从工件上去除材料。近年来，电火花加工在其电气和非电气参数的基础上得到了广泛发展。最近的研究试图探究电火花加工的最佳加工参数，以便更精确地制造复杂形状并获得更好的表面光洁度。电火花加工过程中采用的每种方法都旨在通过采用更好的工作条件和开发更精细地加工新材料的技术来提高加工性能。这项新研究旨在基于多形状电极优化电火花加工的电气参数，以获得良好的表面光洁度和高尺寸精度，并提高加工后的硬度，从而提高加工轮廓的整体质量。通过使用专门设计的多形状铜电极对模具钢D2进行实验，实现了对电气参数（即火花电压、电流、脉冲导通时间和切削深度）的优化。使用统计工具生成实验设计，并进行实际加工以观察表面粗糙度、表面硬度变化和尺寸稳定性。遵循全因子实验设计（DOE）方法（因为有两个以上的工艺参数）通过电火花加工制备样品。使用Minitab作为统计工具进行回归分析和方差分析（ANOVA），以解释和优化结果。实验结果与统计结果的验证证实了每个操作参数对输出参数的重要性。因此，在本研究中发现并呈现了最优化的关系。
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