Department of Mechanical Engineering, National Chung Hsing University, Taichung 402, Taiwan.
Sensors (Basel). 2023 Jul 5;23(13):6174. doi: 10.3390/s23136174.
Machining is a crucial constituent of the manufacturing industry, which has begun to transition from precision machinery to smart machinery. Particularly, the introduction of artificial intelligence into computer numerically controlled (CNC) machine tools will enable machine tools to self-diagnose during operation, improving the quality of finished products. In this study, feature engineering and principal component analysis were combined with the online and real-time Gaussian mixture model (GMM) based on the Kullback-Leibler divergence's measure to achieve the real-time monitoring of changes in manufacturing parameters. Based on the attached accelerometer device's vibration signals and current sensing of the spindle, the developed GMM unsupervised learning was successfully used to diagnose the spindle speed changes of a CNC machine tool during milling. The F1-scores with improved experimental results for X, Y, and Z axes were 0.95, 0.88, and 0.93, respectively. The established FE-PCA-GMM/KLD method can be applied to issue warnings when it predicts a change in the manufacturing process parameter. A smart sensing device for diagnosing the machining status can be fabricated for implementation. The effectiveness of the developed method for determining the manufacturing parameter changes was successfully verified by experiments.
加工是制造业的一个重要组成部分，制造业已经开始从精密机械向智能机械转变。特别是将人工智能引入数控机床（CNC）将使机床能够在运行过程中自我诊断，从而提高成品的质量。在这项研究中，特征工程和主成分分析与在线和实时基于 Kullback-Leibler 散度的高斯混合模型（GMM）相结合，实现了制造参数变化的实时监测。基于附加的加速度计装置的振动信号和主轴的电流感应，成功地使用开发的 GMM 无监督学习来诊断数控机床铣削过程中主轴速度的变化。改进后的 X、Y 和 Z 轴的 F1 分数分别为 0.95、0.88 和 0.93。建立的 FE-PCA-GMM/KLD 方法可以在预测制造过程参数变化时发出警告。可以制造用于诊断加工状态的智能传感设备来实施。通过实验成功验证了所开发的方法确定制造参数变化的有效性。
