The wastage of metal chips from conventional drilling process creates massive environmental pollution. In order to reduce emission pollution, this process should be replaced by the green technology of thermal drilling process. Thermal drilling is an energy efficient, clean and chip less drilling method that has attracted more automotive and aerospace manufacturers in recent years. The processing time, tool failure and manufacturing cost of drilling are reduced and the bushing formation is three times thicker than the workpiece, which offers a prolonged bearing area that fits a shaft firmly. However, achieving these objectives is time consuming and leads to material waste for industrial sectors. Here, we propose a robust methodology that combines experiments with modern optimization technique in order to solve the industrial challenge and further improve the drilling quality. The experiments were conducted on galvanized steel material with different thicknesses (1 mm, 1.5 mm and 2 mm). Three thermal drilling tools are developed using M2 tool steel with three different geometry angles such as ${30}^{\text{o}}$, ${37.5}^{\text{o}}$ and ${45}^{\text{o}}$. The recommended level (A3 B1 C2) identified in this experimental research, allows to minimize the thermal drilling parameters with intended benefits of the output parameters. It permits to identify the best solution of minimum surface roughness of 1.088 μm with the roundness error of 0.080 mm and 0.145 mm run-out. Further, the multi-objective decision technique designed offer contribution details of critical input parameters contribution of rotational speed, tool angle, and workpiece as 76.58%, 10.56%, and 1.982%, respectively.

传统钻孔工艺浪费的金属屑会造成严重的环境污染。为了减少排放污染，该过程应由热钻工艺的绿色技术代替。热钻孔是一种节能，清洁和无屑的钻孔方法，近年来吸引了越来越多的汽车和航空航天制造商。减少了加工时间，工具故障和钻孔的制造成本，并且衬套的厚度是工件的三倍，从而提供了延长的轴承区域，可牢固地固定在轴上。然而，实现这些目标是耗时的，并且导致工业部门的材料浪费。这里，我们提出了一种将实验与现代优化技术相结合的鲁棒方法，以解决工业挑战并进一步提高钻探质量。实验是在具有不同厚度（1毫米，1.5毫米和2毫米）的镀锌钢材上进行的。使用M3工具钢开发了三种热钻工具，它们具有三种不同的几何角度，例如${30}^{\text{Ø}}$， ${37。5}^{\text{Ø}}$ 和 ${45}^{\text{Ø}}$。在本实验研究中确定的推荐等级（A3 B1 C2）可以最大程度地减少热钻参数，并具有预期的输出参数优势。它可以确定最小表面粗糙度为1.088μm的最佳解决方案，其圆度误差为0.080 mm，跳动误差为0.145 mm。此外，设计的多目标决策技术提供了关键输入参数对转速，刀具角度和工件的贡献的贡献细节，分别为76.58％，10.56％和1.982％。