The selection of appropriate drilling parameters is essential for improving productivity and part quality, therefore, this work mainly concentrates on the investigation of drilling time, burr height, burr thickness, roundness and surface roughness. The drilling experiments were carried out on Magnesium (Mg) AZ91 with High Speed Steel (HSS) tool using the Vertical Milling Machine (VMM). The parameters reckoned are spindle speed and feed rate. Artificial Neural Network (ANN) was concerned with the building of the model that will be used to forecast the responses following the consideration of Response Surface Methodology (RSM). Conventional method of modeling (RSM) yields poorer results which redirected the study with ANN. The Genetic Algorithm (GA)-based ANN has been reckoned for developing the model. With two nodes in the parameter layer and seven nodes in the response layer, six different networks were constructed using variety of nodes in the hidden layers which are 2–6–7, 2–7–7, 2–8–7, 2–6–6–7, 2–7–6–7 and 2–8–6–7. It is observed that the 2–8–7 network offers the best ANN model in predicting the various responses. The prediction results ensure the reliability of the ANN model to analyze the effect of drilling parameters over the various responses.

中文翻译：

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遗传算法辅助的人工神经网络估计立式铣床中AZ91镁的钻孔参数

选择合适的钻孔参数对于提高生产率和零件质量至关重要，因此，这项工作主要集中在钻孔时间、毛刺高度、毛刺厚度、圆度和表面粗糙度的研究上。使用立式铣床 (VMM) 在高速钢 (HSS) 工具上对镁 (Mg) AZ91 进行钻孔实验。计算的参数是主轴转速和进给速度。人工神经网络 (ANN) 关注模型的构建，该模型将用于在考虑响应面方法 (RSM) 之后预测响应。传统的建模方法 (RSM) 产生较差的结果，从而将研究重定向到 ANN。基于遗传算法 (GA) 的人工神经网络已被认为用于开发该模型。参数层有两个节点，响应层有七个节点，使用隐藏层中的各种节点构建了六个不同的网络，即 2-6-7、2-7-7、2-8-7、2- 6-6-7、2-7-6-7 和 2-8-6-7。据观察，2-8-7 网络在预测各种响应方面提供了最好的 ANN 模型。预测结果保证了人工神经网络模型分析钻井参数对各种响应的影响的可靠性。