Part deformation prediction and control is a crucial issue for obtaining tight dimensional accuracy so as to ensure product quality with high performance, and deformation prediction is the fundamental of the deformation control. However, existing machining deformation prediction methods are based on the prediction or measurement of residual stress and suffering from two challenges: (i) the measurement accuracy of residual stress field is limited by physical principle and (ii) low prediction in accuracy. In order to address these issues, this paper presents a method for predicting part machining deformation based on deformation force using the proposed Physics-informed Latent Variable Model involved physics knowledge. Deformation force is introduced to represent the inner unbalanced residual stress state of the workpiece, and it is a much easier and more accurate signal compared with residual stress. Machining deformation is predicted by fusing the data-driven method and the prior knowledge of deformation mechanical relationship by taking advantage of the latent variable. The proposed method was verified both in simulation and actual machining environment, and accurate machining deformation prediction has been achieved. The proposed method can be readily extended to the prediction problems involved with difficult-to-measure physical quantities.

零件变形预测与控制是获得严格尺寸精度从而保证高性能产品质量的关键问题，变形预测是变形控制的基础。然而，现有的加工变形预测方法基于残余应力的预测或测量，存在两个挑战：（i）残余应力场的测量精度受物理原理限制；（ii）预测精度低。为了解决这些问题，本文提出了一种基于变形力预测零件加工变形的方法，该方法使用所提出的包含物理知识的基于物理的潜在变量模型。引入变形力来表示工件内部不平衡的残余应力状态，与残余应力相比，这是一个更容易和更准确的信号。利用潜在变量，通过融合数据驱动方法和变形力学关系的先验知识来预测加工变形。该方法在仿真和实际加工环境中得到验证，实现了精确的加工变形预测。所提出的方法可以很容易地扩展到涉及难以测量的物理量的预测问题。并实现了精确的加工变形预测。所提出的方法可以很容易地扩展到涉及难以测量的物理量的预测问题。并实现了精确的加工变形预测。所提出的方法可以很容易地扩展到涉及难以测量的物理量的预测问题。