Research Status and Development Trend of Machining Quality Prediction
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摘要:
机械加工质量预测是智能制造的重要组成内容，也是实现质量闭环控制的前提条件，对推动智能制造系统真正落地应用具有极其重要的作用. 在对机械加工质量预测的历史进行简要回顾时发现，学者多将研究重点放在机床某一关键部件对加工质量影响的机理研究，却鲜见部件耦合影响的关联性研究. 基于上述难题，本文首先剖析影响机械加工质量的7类要素，包括刀具几何参数、切削参数、切削液类型、热误差与热变形、数控机床零部件性能退化、切削颤振以及系统特性；随后，根据各要素数据种类和测量方式的不同，将机械加工质量监测与预测方法划分为4大类，包括机器视觉测量、功率测量、振动测量以及其他测量方法，并对各方法的技术特点、局限性和发展动态进行了阐述；最后，考虑各机械加工质量监测与预测方法的不足，指出材料切削机制研究、数据质量评估方法、面向工业现场数据库构建的标准以及质量预测信息的智能表征与可视化等方面可能是未来的发展趋势.
Abstract:The prediction of machining quality is a vital component of intelligent manufacturing and a prerequisite for achieving quality closed-loop control, playing an extremely important role in promoting the practical application of intelligent manufacturing systems. A brief review of the history of machining quality prediction reveals that scholars have mostly focused on the mechanism of the influence of a key component of the machine tool on machining quality, while research on the correlation between the coupling effects of machine components is rare. Based on the aforementioned challenges, firstly, seven types of factors that affect machining quality are analyzed, including tool geometry parameters, cutting parameters, cutting fluid type, thermal errors and deformations, degradation of CNC machine tool components, cutting chatter, and system characteristics. Subsequently, according to the different types of data and measurement methods for each factor, the monitoring and prediction methods of machining quality are divided into four categories, including machine vision measurement, power measurement, vibration measurement, and other measurement methods. The technical characteristics, limitations, and development trends of each method are then expounded. Finally, considering the deficiencies of various machining quality monitoring and prediction methods, this paper points out that research on material cutting mechanisms, data quality assessment methods, standards for constructing industry site databases, and intelligent representation and visualization of quality prediction information may be future development trends.
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图 2 基于视觉测量的加工质量预测方法现状
Figure 2. Machining quality prediction methods based on visual measurement
图 8 不同粗糙度下的时域/频域信号
Figure 8. Time domain/frequency domain signals with different roughness
表 2 2种主要的视觉测量粗糙度等表面质量识别方法
Table 2. Comparison of two main visual measurement methods for surface quality recognition such as roughness
测量方式 优点 缺点 光学测量
（白光干涉仪、超景深三维显微系统）非接触式测量，对被测工件无损伤；对区域的微观三维形貌的准确测量 单次检测区域较小；设备测试、维护较为昂贵；对检测环境要求高，容易受到现场加工环境影响 视觉间接测量（基于机器视觉的加工纹理图样分析方法） 容易满足批量化测试需求，进而实现在线或在机测量；测试与监测过程自动化程度高，能够实现快速、高效地反馈工件加工质量情况 准确性和对于实际加工过程的多变工况、光源设置差异等情形的鲁棒性有待优化；对于颤振纹理识别效果
较好
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表 4 振动信号预测表面粗糙度方法
Table 4. Surface roughness prediction method based on vibration signal
作者 采用信号 方法 结果 数据集（工件数量） Hessainia Z等 [114] 轴向和径向振动信号、切削条件 TDA $R = 99.9{\text{%}} $ 训练集：27，测试集=训练集 Upadhay V等[115] 三向振动信号、切削条件 TDA $R_{{\rm{adj}}}^2 = 93.2{\text{%}} ,{\bar e_{\rm{r}}} = 3.5{\text{%}} $ 训练集：15，测试集=训练集 Salgado D R等[116]
三向振动信号、切削条件、刀具参数SSA ${\bar e_{\rm{r}}} = 5.74{\text{%}} $ 训练集：35，测试集：20 García P E等[117] 三向振动信号 SSA $R_{ {\rm{adj} } }^2 = 87.8{\text{%} } ,{\bar e_{\rm{r} } } = 14.60{\text{%} }$
$R = 92{\text{%}} $训练集：270，测试集：90 Kirby E D等[118] 轴向加速度信号、切削条件 TDA ${\bar e_{\rm{r} } } = 5.00{\text{%} }$ 训练集：83，测试集：7 Risbod K A等[119] 径向振动信号、切削条件 TDA $e_{\rm{r},\max } = 5.00{\text{%} }$ 训练集：—，测试集：20 Plaza E G等[120] 三向加速度信号、三向振动信号 FFT $R_{ {\rm{adj} } }^2 = 86.7{\text{%} } ,{\bar e_{\rm{r} } } = 9.80{\text{%} }$ 训练集：52，测试集：12
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