Due to their high flexibility, large workspace, and high repeatability, industrial robots are widely used in roughing and semifinishing fields. However, their low machining accuracy and low stability limit further development of industrial robots in the machining field, with low stiffness being the most significant factor. The stiffness of industrial robots is affected by the joint deformation, transmission mechanism, friction, environment, and coupling of these factors. Moreover, the stiffness of a robot has a nonlinear distribution throughout the workspace, and external forces during processing cause irregular deviations of the robot, thereby affecting the machining accuracy and surface quality of the workpiece. Many scholars have researched identifying the stiffness of industrial robots and have proposed methods for improving the performance of industrial robots, mainly by optimizing the body structure of the robot and compensating for deformation errors with stiffness models. This paper reviews recent research on the stiffness modelling of industrial robots, which can be broadly classified as finite element analysis (FEA), matrix structure analysis (MSA), and virtual joint modelling (VJM) methods. Each method is studied from three aspects: algorithms, implementation, and limitations. In addition, common measurement techniques have been introduced for measuring deformation. Further research directions are also discussed.

When industrial robots are used in various machining processes, higher requirements for processing accuracy and performance stability are put forward. Low-frequency chatter occurs in the machining process. Because of the articulated mechanism of industrial robots, the low frequency varies in the whole workspace. This is related to the eigenfrequency of industrial robots. To investigate the low-frequency distribution, the first eigenfrequency of an industrial robot is obtained through the impact modal experiment, which is used to evaluate the machining field and robot pose. To obtain the eigenfrequency distribution of the workspace with limited experimental data, an artificial neural network (ANN) is used to establish a prediction model. The structure and prediction accuracy of the ANN model are discussed.