Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin 150080, China.
Postdoctoral Research Station of Electrical Engineering, Harbin University of Science and Technology, Harbin 150080, China.
Sensors (Basel). 2023 Jul 3;23(13):6123. doi: 10.3390/s23136123.
Due to its low stiffness, the boring bar used in deep-hole-boring is prone to violent vibration during the cutting process. It is often inaccurate and inefficient to judge the vibration state of the boring bar through artificial experience. To detect the change of the vibration state of the boring bar over time, guide the adjustment of the processing parameters, and avoid wastage of the workpiece and the loss of equipment, it is particularly important to intelligently monitor the vibration state of the boring bar during processing. In this paper, the boring bar is taken as the research object, and an intelligent monitoring technology of the boring bar's vibration state based on deep learning is proposed. Based on grouping convolution, channel shuffle, and BiLSTM, a shuffle-BiLSTM NET model is constructed, which is both lightweight and has a high classification accuracy. The boring experiment platform is built, and 192 groups of cutting experiments are carried out. The three-way acceleration and sound pressure signals are collected, and the signals are processed by smoothed pseudo-Wigner-Ville distribution. The original signals are transformed into a 256 × 256 × 3 matrix obtained by a two-dimensional time-frequency spectrum diagram. The matrix is input into the model to recognize the boring bar's vibration state. The final classification accuracy is 91.2%. A variety of typical deep learning models are introduced for performance comparison, which proves the superiority of the models and methods used in this paper.
由于其低刚度，深孔镗削用的镗杆在切削过程中容易发生剧烈振动。通过人工经验判断镗杆的振动状态往往不准确、效率低。为了检测镗杆振动状态随时间的变化，指导加工参数的调整，避免工件浪费和设备损失，智能监测加工过程中镗杆的振动状态尤为重要。本文以镗杆为研究对象，提出了一种基于深度学习的镗杆振动状态智能监测技术。基于分组卷积、通道洗牌和 BiLSTM，构建了一个轻量级且具有较高分类精度的洗牌-BiLSTM NET 模型。搭建了镗削实验平台，进行了 192 组切削实验，采集了三路加速度和声压信号，并对信号进行了平滑伪魏格纳-维尔分布处理。将原始信号转换为通过二维时频谱图获得的 256×256×3 矩阵。将矩阵输入模型以识别镗杆的振动状态。最终分类准确率为 91.2%。引入了多种典型的深度学习模型进行性能比较，证明了本文所采用的模型和方法的优越性。
