Point set registration (PSR) is a key component of computer vision and pattern recognition tasks, with the goal of assigning correspondence and recovering the transformation that maps one point set to another, to achieve optimal alignment. The geometric quality inspection of aviation complex parts is mainly based on the digitization of the object, and realizes the effective evaluation of geometric quality by analyzing the digital information that characterizes the shape of object, which is of great significance for the high performance and reliability service of aircraft. However, PSR is the mathematical foundation for solving the point cloud alignment problems in the geometric quality inspection of aviation complex parts, the association between the two has not been systematically discussed, which leads to unfavorable research results. Therefore, this paper first gives a mathematical description of PSR. Secondly, the representative fundamental algorithms for PSR are introduced, mainly include: distance-based PSR algorithms, Kernel correlation-based PSR algorithm, mixture model-based PSR algorithms, global-local structure preservation-based PSR algorithms, feature-based PSR algorithms and learning-based PSR algorithms. Besides, the ideas, basic steps, and limitations of these are revealed. Thirdly, the works on point cloud alignment problems in geometric quality inspection of aviation complex parts and the PSR algorithms used are reviewed, i.e. the application of PSR. Finally, the development direction of PSR and the challenges faced in the geometric quality inspection of aviation complex parts are discussed.

Geometric error, mainly due to imperfect geometry and dimensions of machine components, is one of the major error sources of machine tools. Considering that geometric error has significant effects on the machining quality of manufactured parts, it has been a popular topic for academic and industrial research for many years. A great deal of research work has been carried out since the 1970 s for solving the problem and improving the machining accuracy. Researchers have studied how to measure, detect, model, identify, reduce, and compensate the geometric errors. This paper presents a thorough review of the latest research activities and gives an overview of the state of the art in understanding changes in machine tool performance due to geometric errors. Recent advances in measuring the geometrical errors of machine tools are summarized, and different kinds of error identification methods of translational axes and rotation axes are illustrated respectively. Besides, volumetric geometric error modeling, tracing, and compensation techniques for five-axis machine tools are emphatically introduced. Finally, research challenges in order to improve the volumetric accuracy of machine tools are also highlighted.

Machining data have been increasingly crucial with the development of modern manufacturing strategies, and the explosive growth of data amount revolutionizes how to collect and analyze data. In machining process, anomalies such as machining chatter and tool wear occur frequently, which strongly affect the process by reducing accuracy and quality as well as increasing the time and cost. As a typical type of machining data, signals acquired in real time by advanced sensor techniques are widely embraced to detect those anomalies. This paper reviews the recent development and applications of process monitoring technologies in machining processes, and typical application scenarios in machining processes are discussed with the latest literatures and current research issues. Potential future trends of process data monitoring and analysis for intelligent machining are put forward at the end of the paper.