A Method for Measurement of Workpiece Form Deviations Based on Machine Vision

Due to the presence of numerous surface defects, the inadequate contrast between defective and non-defective regions, and the resemblance between noise and subtle defects, edge detection poses a significant challenge in dimensional error detection, leading to increased dimensional measurement inaccuracies. These issues serve as major bottlenecks in the domain of automatic detection of high-precision metal parts. To address these challenges, this research proposes a combined approach involving the utilization of the YOLOv6 deep learning network in conjunction with metal lock body parts for the rapid and accurate detection of surface flaws in metal workpieces. Additionally, an enhanced Canny–Devernay sub-pixel edge detection algorithm is employed to determine the size of the lock core bead hole. The methodology is as follows: The data set for surface defect detection is acquired using the labeling software lableImg and subsequently utilized for training the YOLOv6 model to obtain the model weights. For size measurement, the region of interest (ROI) corresponding to the lock cylinder bead hole is first extracted. Subsequently, Gaussian filtering is applied to the ROI, followed by a sub-pixel edge detection using the improved Canny–Devernay algorithm. Finally, the edges are fitted using the least squares method to determine the radius of the fitted circle. The measured value is obtained through size conversion. Experimental detection involves employing the YOLOv6 method to identify surface defects in the lock body workpiece, resulting in an achieved mean Average Precision (mAP) value of 0.911. Furthermore, the size of the lock core bead hole is measured using an upgraded technique based on the Canny–Devernay sub-pixel edge detection, yielding an average inaccuracy of less than 0.03 mm. The findings of this research showcase the successful development of a practical method for applying machine vision in the realm of the automatic detection of metal parts. This achievement is accomplished through the exploration of identification methods and size-measuring techniques for common defects found in metal parts. Consequently, the study establishes a valuable framework for effectively utilizing machine vision in the field of metal parts inspection and defect detection.

Real-Time Defect Detection for Metal Components: A Fusion of Enhanced Canny–Devernay and YOLOv6 Algorithms

Wang

Liu³

et al. 2023

Validation of a Manual Methodology for Measuring Roundness and Cylindricity Tolerances

García-Martínez,

García-González,

Manjabacas

et al. 2023

This paper describes a practice-based methodology applied to roundness and cylindricity concepts. Traditionally, technicians encounter difficulties in understanding this topic, especially when they analyze the tolerances involved due to the complexity of their graphic interpretation. Additionally, it is mandatory for industrial engineers to acquire competencies that allow them to validate form and make decisions in this field. With the goal of enhancing the handling of measurement techniques involved in this topic and analyzing the results obtained, a methodology has been designed to address the roundness and cylindricity tolerance evaluation from different perspectives. Firstly, based on a conceptual analysis, an analytical procedure is introduced. Secondly, an engaging manual practice is implemented by using simple measuring instruments that allow the user to be involved in the development of the procedure applied. The conditions that tried to optimize the manual procedure to get good results were analyzed. Moreover, the results obtained under this perspective allow us to ensure that better skills can be acquired regarding the typical method based on the coordinate measuring machines. This experience has been validated based on the practice with ongoing users under a training period.

Research on Coaxiality Measurement Method for Automobile Brake Piston Components Based on Machine Vision

Li,

Ge,

Shi

et al. 2024

Aiming at addressing the problem of the online detection of automobile brake piston components, a non-contact measurement method based on the combination of machine vision and image processing technology is proposed. Firstly, an industrial camera is used to capture an image, and a series of image preprocessing algorithms is used to extract a clear contour of a test piece with a unit pixel width. Secondly, based on the structural characteristics of automobile brake piston components, the region of interest is extracted, and the test piece is segmented into spring region and cylinder region. Then, based on mathematical morphology techniques, the edges of the image are optimized. We extract geometric feature points by comparing the heights of adjacent pixel points on both sides of the pixel points, so as to calculate the variation of the spring axis relative to the reference axis (centerline of the cylinder). Then, we extract the maximum variation from all images, and calculate the coaxiality error value using this maximum variation. Finally, we validate the feasibility of the proposed method and the stability of extracting geometric feature points through experiments. The experiments demonstrate the feasibility of the method in engineering practice, with the stability in extracting geometric feature points reaching 99.25%. Additionally, this method offers a new approach and perspective for coaxiality measurement of stepped shaft parts.