High product quality is one of key demands of customers in the field of manufacturing such as computer numerical control (CNC) machining. Quality monitoring and prediction is of great importance to assure high-quality or zero defect production. In this work, we consider roughness parameter R_a, profile deviation P_t and roundness deviation R_ONt of the machined products by a lathe. Intrinsically, these three parameters are much related to the machine spindle parameters of preload, temperature, and rotations per minute (RPMs), while in this paper, spindle vibration and cutting force are taken as inputs and used to predict the three quality parameters. Power spectral density (PSD) based feature extraction, the method to generate compact and well-correlated features, is proposed in details in this paper. Using the efficient features, neural network based machine learning technique turns out to be able to result in high prediction accuracy with R² score of 0.92 for roughness, 0.86 for profile, and 0.95 for roundness.

高质量的产品是客户在计算机数控（CNC）加工等制造领域中的关键要求之一。质量监控和预测对于确保高质量或零缺陷生产至关重要。在这项工作中，我们考虑粗糙度参数R _a，轮廓偏差P _t和圆度偏差R _ONt。车床加工的产品。从本质上讲，这三个参数与机床主轴的预紧力，温度和每分钟转数（RPMs）密切相关，而在本文中，主轴振动和切削力作为输入并用于预测这三个质量参数。本文详细提出了基于功率谱密度（PSD）的特征提取方法，该方法用于生成紧凑且相关性强的特征。利用高效的功能，基于神经网络的机器学习技术最终能够实现较高的预测精度，R ²得分的粗糙度为0.92，轮廓的R ⁶值为0.86，圆度为0.95。