DSS has low machinability characteristics due to its high strength, machining is complicated, and careful attention is required when selecting machining parameters. The main criteria discussed in this paper concern the turning optimization parameters and machining time reduction of DSS 2205 as the work material. The input parameters are cutting velocity, feeds, cutting depth, and tooltip nose radius of the cutting tool. The design of experiments methodology is employed to design the experiments using Design-Expert V12 software. The second-order mathematical model was developed, and analysis of variance was performed to analyze the performance characteristics to recognize the critical variables influencing the output parameter. An artificial neural network (ANN) backpropagation algorithm using MATLAB software was used to develop the mathematical model and optimize the output. The model was developed, and the results were optimized using MATLAB software’s ANN back propagation method to find the best possible solutions. The generated models were significant based on the analysis of variance and the R-squared value, and these results indicate that the cutting velocity is the most critical factor. For a low machining time, the cutting velocity should be between 100 and 140 m/min, and the tooltip nose radius should be 2.8 mm. The optimal parameter settings are validated by performing a lower is better confirmation test using gray relational analysis (GRA). The GRA exposed the lower machining time at a cutting velocity of 140 m/min, rate of feed of 0.5 mm/rev, cutting depth of 0.5 mm, and tooltip nose radius of 2.4 mm. The predicted values were close to the experimental values, and the result indicates the optimal level of the highest GRA grade of the machining variable.

中文翻译：

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使用 RSM-ANN 和灰色关联分析研究 DSS-2205 的加工特性

DSS由于强度高，具有切削加工性差的特点，加工比较复杂，选择加工参数时需要仔细注意。本文讨论的主要标准涉及以 DSS 2205 作为工件材料的车削优化参数和加工时间缩短。输入参数是切削刀具的切削速度、进给量、切削深度和刀尖半径。实验设计方法用于使用 Design-Expert V12 软件来设计实验。开发了二阶数学模型，并进行方差分析来分析性能特征，以识别影响输出参数的关键变量。使用 MATLAB 软件的人工神经网络 (ANN) 反向传播算法用于开发数学模型并优化输出。该模型已开发完毕，并使用 MATLAB 软件的 ANN 反向传播方法对结果进行优化，以找到最佳的解决方案。基于方差分析和R平方值，生成的模型是显着的，这些结果表明切削速度是最关键的因素。对于较短的加工时间，切削速度应在 100 至 140 m/min 之间，刀尖半径应为 2.8 mm。通过使用灰色关联分析 (GRA) 执行越低越好的确认测试来验证最佳参数设置。GRA 在切削速度为 140 m/min、进给率为 0.5 mm/rev、切削深度为 0.5 mm、刀尖半径为 2.4 mm 时显示出较短的加工时间。预测值与实验值接近，结果表明加工变量最高GRA等级的最佳水平。