# Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision
## 1 Introduction  
Intelligent manufacturing is characterized by autonomy and self-optimization, which requires as few people as possible to participate in decision-making in the manufacturing process [1]. A digital workshop, known as the minimum implementation unit for intelligent manufacturing, is a combination of a group of machine tools that perform a specific processing sequence of products [2]. The measurement of the machining precision of the workpiece is necessary for forming a closed-loop control in a digital workshop. Commonly used measurement methods of machining precision include off-line measurement and on-line measurement [3]. Off-line measurement requires moving the workpiece outside the production line. In this method, additional errors such as clamp error, spindle/slide motion errors, thermal deformations, and vibration are inevitable, making it difficult to ensure measurement accuracy. In contrast, on-line measurement can be carried out when the workpiece is on the production line. Under this condition, if the measurement can be carried out in the process of the machining operation, it is referred to as in-process measurement. High efficiency is required for in-process measurement, toensure that the measurement process is completed in the time interval from one cutting operation to the next. This is necessary for achieving compensation machining by matching the compensation results with the CAD model. Among various measurement methods, contact measurement is widely used for its stability and anti-interference capability. However, it is often difficult to mount a contact measuring instrument on machine tools. Thus, non-contact measurement is receiving increasing attention due to its advantages, such as wide measurement range, fast measurement speed, and low cost. Commonly used non-contact measurement techniques include LiDAR [4], machine vision technology [5], etc. Machine vision technology [6] is widely used in sorting, positioning, feature recognition, size measurement, and other aspects of industrial products. Compared with traditional laser measurement, machine vision technology has the advantages of lower cost, better stability, and higher measurement efficiency [7]. Machine-vision-based position measurement has the characteristics of non-contact, high accuracy, and high stability [8], and has very important applications in areas such as vehicles [9; 10], aerospace [11; 12], robots [13], and industrial production [14; 15]. Automated industrial inspection relies on computer vision technology to achieve highly efficient and safe production [16]. The application of machine vision detection technology is expanding from simple measurement of the size of automotive gaskets [17] to more complex evaluation of the plane size of mechanical components [18] and is undergoing further development to include distance measurement [19] and visual monitoring of the rolling process [20]. The countersink detection method proposed by [PERSON] further improves the detection speed during the machining process [21]. Many studies have shown that the continuous evolution of machine vision technology has successfully achieved on-line detection of industrial measurement technology [22; 23; 24]. Precision measurement plays a crucial role in ensuring machining quality. [PERSON], BC proposed a high-precision bearing size measurement method using unidirectional matrices and partial domain effects [25]. [PERSON] achieves part contour extraction through wavelet denoising and Canny edge detection. [26]. Compared to traditional manual measurement methods, these machine-vision-based measurement technologies have significant advantages in eliminating human bias and improving measurement speed [17; 22]. The precise measurement of part dimensions is challenging due to various interference factors present in the manufacturing process, including machining marks, metal cutting, and dust. These interference factors decrease the machine vision measurement accuracy in the studies cited above.