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Prediction of workpiece elastic deflections under cutting forces in turning

机译:切削时切削力作用下工件弹性挠度的预测

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One of the problems faced in turning processes is the elastic deformation of the workpiece due to the cutting forces resulting in the actual depth of cut being different than the desirable one. In this paper, a cutting mechanism is described suggesting that the above problem results in an over-dimensioned part. Consequently, the problem of determining the workpiece elastic deflection is addressed from two different points of view. The first approach is based on solving the analytical equations of the elastic line, in discretized segments of the workpiece, by considering a stored modal energy formulation due to the cutting forces. Given the mechanical properties of the workpiece material, the geometry of the final part and the cutting force values, this numerical method can predict the elastic deflection. The whole approach is implemented through a Microsoft Excel workbook. The second approach involves the use of artificial neural networks (ANNs) in order to develop a model that can predict the dimensional deviation of the final part by correlating the cutting parameters and certain workpiece geometrical characteristics with the deviations of the depth of cut. These deviations are calculated with reference to final diameter values measured with precision micrometers or on a CMM. The verification of the numerical method and the development of the ANN model were based on data gathered from turning experiments conducted on a CNC lathe. The results support the proposed cutting mechanism. The numerical method qualitatively agrees with the experimental data while the ANN model is accurate and consistent in its predictions.
机译:车削过程中面临的问题之一是由于切削力导致的工件弹性变形,导致实际切削深度与理想切削深度不同。在本文中,描述了一种切割机制,表明上述问题导致零件尺寸过大。因此,从两个不同的角度解决了确定工件弹性挠度的问题。第一种方法是通过考虑由于切削力而存储的模态能量公式来求解工件离散段中弹性线的解析方程。给定工件材料的机械性能,最终零件的几何形状和切削力值,此数值方法可以预测弹性挠度。整个方法是通过Microsoft Excel工作簿实现的。第二种方法涉及使用人工神经网络(ANN),以开发一种模型,该模型可以通过将切削参数和某些工件的几何特征与切削深度的偏差相关联来预测最终零件的尺寸偏差。这些偏差是参考使用精密千分尺或CMM测得的最终直径值计算得出的。数值方法的验证和ANN模型的开发是基于在CNC车床上进行车削实验收集的数据。结果支持提出的切割机制。数值方法在质量上与实验数据吻合,而人工神经网络模型的预测准确且一致。

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