Due to the mechanical properties and machinability of AISI 1040 steel, it has a broad range of industrial applicability. The present work investigates the dependence of the average temperature generated at the tool work interface on machining parameters such as cutting speed, feed rate and depth of cut. The machining parameters were varied at three equally spaced levels. Experiments were carried out following combinations in Taguchi’s L₂₇ orthogonal array. A simple and cost-effective tool work thermocouple was devised and calibrated to obtain the tool work interface temperature. A 3-5-1 feed forward artificial neural network (ANN) model trained using the Levenberg-Marquardt (LM) back propagation algorithm resulted in efficient modelling of the complex relationship between the average cutting temperature and machining parameters (R-value of 0.99). The trained network was optimized using genetic algorithm (GA) to predict optimal turning parameters. This methodology has been termed as ANN-GA method. A significant reduction in the average cutting temperature could be realized due to optimization using ANN-GA method. Analysis of variance revealed highest contribution from the cutting speed and its square term in controlling the cutting temperature at tool work interface.

由于AISI 1040钢的机械性能和可加工性，它具有广泛的工业适用性。目前的工作研究了刀具工作界面产生的平均温度对切削速度、进给率和切削深度等加工参数的依赖性。加工参数在三个等距水平上变化。在田口的 L ₂₇中按照以下组合进行实验正交阵列。设计并校准了一种简单且具有成本效益的工具工作热电偶以获得工具工作界面温度。使用 Levenberg-Marquardt (LM) 反向传播算法训练的 3-5-1 前馈人工神经网络 (ANN) 模型可以对平均切削温度和加工参数之间的复杂关系进行有效建模（R 值为 0.99） . 使用遗传算法 (GA) 对训练后的网络进行优化，以预测最佳转弯参数。这种方法被称为 ANN-GA 方法。由于使用 ANN-GA 方法进行优化，可以实现平均切削温度的显着降低。方差分析表明，切削速度及其平方项对控制刀具工作界面切削温度的贡献最大。