In-process measurement of machining precision is of great importance to advanced manufacturing, which is an essential technology to realize compensation machining. In terms of cost-effectiveness and repeatability of computer vision, it has become a trend to replace traditional manual measurement with computer vision measurement. In this paper, an in-process measurement method is proposed to improve precision and reduce the costs of machining precision. Firstly, a universal features model framework of machining parts is established to analyze the CAD model and give standard information on the machining features. Secondly, a window generator is proposed to adaptively crop the image of the machining part according to the size of features. Then, the automatic detection of the edges of machining features is performed based on regions of interest (ROIs) from the cropped image. Finally, the measurement of machining precision is realized through a Hough transform on the detected edges. To verify the effectiveness of the proposed method, a series of in-process measurement experiments were carried out on machined parts with various features and sheet metal parts, such as dimensional accuracy measurement tests, straightness measurement tests, and roundness measurement tests under the same part conditions. The best measurement accuracy of this method for dimensional accuracy, straightness, and roundness were 99%, 97%, and 96%, respectively. In comparison, precision measurement experiments were conducted under the same conditions using the Canny edge detection algorithm, the sub-pixel edge detection algorithm, and the Otsu–Canny edge detection algorithm. Experimental results show that the feature-model-based in-process measurement of machining precision using computer vision demonstrates superiority and effectiveness among various measurement methods.

This paper proposes a method for improving machine tool linear feed system accuracy by considering the geometric error shape of the guideway. First, a mathematical model relating guideway errors to worktable pose errors is established using static force equilibrium principles and deformation coordination equations. The impact of different guideway geometric error-shape combinations of linear feed system accuracy is analyzed. It is determined under which combination of guideway error shapes the linear feed system achieves the highest accuracy. Second, a finite element analysis model of the machine tool linear feed system is developed to examine how guideway geometric error shapes affect the error-averaging effect. This is compared with conventional design methods that disregard error shapes. Finally, experimental verification confirms both the effectiveness and broader applicability of the guideway error-shape design methodology. The results show that controlling the error shape of guideways can relax the requirement for guideway amplitude, thereby reducing machining difficulty and production costs.

This paper investigates the thermal characteristics of machine tools under the influence of multiple heat sources and proposes an active temperature control method. The study was conducted under steady-state conditions with a constant feed rate of 10 m/min and no cutting load. First, finite element simulations are performed to analyze the thermal characteristics of the machine tool subjected to internal heat sources, yielding the corresponding temperature and deformation fields. Second, within the tested temperature range, the thermal characteristics are examined with respect to the layout of temperature control devices, and the best-performing layout schemes for the bed and column are determined. Subsequently, the thermal error resulting from the coupling effect of multiple temperature control devices is studied, and an active control strategy is proposed to obtain the best layout configuration within the tested range. Finally, experiments are conducted using a multi-loop differential active temperature control system, and the validity of the proposed method is verified through comparison between simulation and experimental results. The findings demonstrate that the proposed active control method effectively reduces thermal deformation in machine tool structures.