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.

The stability and reliability of the brake system are critically affected by the concentricity error of automotive brake piston components. Traditional contact-based concentricity measurement methods are inefficient. To address the issue of low detection efficiency, a non-contact concentricity measurement method based on the combination of machine vision and image processing technology is proposed in this paper. In this method, an industrial camera is utilized to capture images of the measured part's end face from the top of the spring. Edge contours are extracted through image preprocessing algorithms, the outer circle center is calculated, and the inner circle center is fitted. Finally, the concentricity error is calculated using the coordinates of the two circle centers. Experimental results show that, compared to a coordinate measuring machine(CMM), this method has a maximum error of only 0.0393mm and an average measurement time of just 3.9s. It significantly improves measurement efficiency and meets the industry's demand for automated inspection. The experiments verified the feasibility and effectiveness of this method in practical engineering applications, providing reliable technical support for the online inspection of automotive brake piston components. Additionally, this method can be applied to the concentricity measurement of other complex stepped shaft parts.