Purpose

Hardened steels are being extensively used in aerospace, automobile industries, bearing and die industries. High hardness of such steels makes it difficult to machine. During turning process of such materials, the cutting tool is subjected to heavy mechanical load and thus creates vibrations throughout machining process. It affects the surface quality of machined part and provokes higher rate of tool wear with lowering tool life. Therefore, measurement and prediction of vibration induced is of prime importance.

Objective

The aim of this paper is to evaluate vibration acceleration and surface roughness with varying machining parameters such as cutting speed, feed and depth of cut to develop predictive mathematical model.

Methods

The central composite rotatable design (CCRD) method is used in designing the experimental runs. The experimental results are further used to develop mathematical models using regression analysis. It is performed using Design Expert tool. The ANN model is developed using MATLAB tool and the predictions are obtained with acceptable deviations. The comparison of predictive model with experimentation is performed to report the deviation.

Results

The examination of the outcomes revealed that the cutting conditions are having prominent and mixed-type effect on vibration signals. The regression and ANN models have been found to be acceptable for prediction of vibration induced and the surface roughness. The coefficient of regression (\(R^{2}\)) is found to 0.92 which shows that the developed mathematical models have a good approximation in correlating the effect of cutting parameters on vibration of a cutting tool. The obtained correlations are verified by conformity test and have reported the close degree of agreement with respect to experimental values. It registered a lowest deviation of 3.3%. The ANN model is effective in reproducing experimental results through simplifying the complex machining process. The investigation reports predictions of ANN are more accurate than regression analysis. The surface roughness predictions agreed well with experimental results and registered the acceptable deviation of 4.33% using regression analysis and 1.37% using ANN approach.

中文翻译：

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AISI 52100钢硬车削中刀具振动和表面粗糙度的评估：一种实验和人工神经网络方法

目的

硬化钢被广泛用于航空航天，汽车工业，轴承和模具工业。这种钢的高硬度使其难以加工。在这种材料的车削过程中，切削工具承受着很大的机械负荷，因此在整个加工过程中都会产生振动。它会影响加工零件的表面质量，并导致更高的刀具磨损率并降低刀具寿命。因此，引起的振动的测量和预测至关重要。

目的

本文的目的是评估具有变化的加工参数（例如切削速度，进给和切削深度）的振动加速度和表面粗糙度，以开发预测性数学模型。

方法

中央复合可旋转设计（CCRD）方法用于设计实验运行。实验结果进一步用于通过回归分析建立数学模型。它是使用Design Expert工具执行的。使用MATLAB工具开发了ANN模型，并以可接受的偏差获得了预测。将预测模型与实验进行比较以报告偏差。

结果

对结果的检查表明，切削条件对振动信号具有明显的混合影响。已经发现，回归模型和ANN模型对于预测振动和表面粗糙度是可以接受的。回归系数（\（R ^ {2} \））被发现为0.92，这表明开发的数学模型在关联切削参数对切削工具振动的影响方面具有很好的近似性。所获得的相关性通过一致性测试进行了验证，并报告了与实验值的接近程度。它的最低偏差为3.3％。通过简化复杂的加工过程，ANN模型可有效地再现实验结果。调查报告对ANN的预测比回归分析更准确。表面粗糙度的预测与实验结果吻合得很好，使用回归分析得出的可接受偏差为4.33％，使用ANN方法得出的偏差为1.37％。