In precision manufacturing, video measuring machines (VMMs) have become essential tools for dimensional inspection and quality control, thanks to their non-contact and high-efficiency capabilities. However, with the emergence of new materials and complex surface treatments, early software algorithms have gradually revealed limitations in adaptability. As a technical professional with over twenty years of experience in the optical measurement industry, I have accumulated extensive experience through numerous on-site installations and customer applications, gaining a deep understanding of the interplay between algorithm adaptation and optical optimization.
Early VMM software primarily relied on fixed threshold methods or simple operators such as Sobel and Canny for edge detection. While these methods performed adequately on standard metal and plastic parts, uneven grayscale caused by reflections or complex surface treatments often prevented accurate edge extraction. I recall one electroplated part inspection where surface reflections caused edge detection deviations of nearly 20 micrometers, falling short of the client’s precision requirements. This experience reinforced my understanding that traditional algorithms alone cannot handle the increasing diversity of materials.
As manufacturing incorporates more advanced materials, edge detection faces new challenges. Highly reflective materials, such as chrome-plated or polished stainless steel, can generate mirror reflections and false edges. Transparent or semi-transparent materials, such as glass or acrylic, allow light to pass through, blurring boundaries and making it difficult for traditional algorithms to determine true edges. Additionally, rough surfaces or coated parts exhibit complex grayscale variations, and noise interference can significantly reduce detection accuracy. In one instance, while inspecting a transparent medical catheter, the software misidentified both inner and outer virtual edges, resulting in a measured diameter nearly twice the actual size. Such issues are not uncommon in precision inspection.
The performance of a VMM depends not only on algorithms but also on the tight coupling between optical illumination and software. Contour lighting is suitable for external shape detection but can produce double edges on transparent parts. Coaxial lighting can suppress reflections but may fail to capture details on rough surfaces. Ring lighting can reveal complex structures but uneven illumination may increase algorithmic challenges. During on-site calibration of a metal part, I observed that even with multiple lighting adjustments, the older software frequently misjudged edges. Upgrading to software supporting adaptive thresholds and optimizing the light setup significantly improved edge detection reliability.
In practical cases, a high-reflective automotive needle valve surface once reduced edge detection accuracy to about 60%. By upgrading the algorithm and adding polarized lighting, I achieved an on-site recognition rate of 95% with a precision of ±3 micrometers. In another case involving transparent acrylic parts, I applied multi-image fusion and subpixel fitting techniques to resolve virtual edge interference and achieve stable measurements. These experiences reinforced my view that algorithm optimization and optical solutions must work in tandem.
Based on years of experience, I see the future of VMM algorithm optimization focusing on adaptive thresholds and regional segmentation to improve adaptability in complex scenes; subpixel edge fitting to surpass camera resolution limits; multi-image fusion and intelligent lighting to enhance edge extraction stability; and the use of AI and deep learning to recognize special material features through training, enabling algorithms to learn and optimize autonomously.
The algorithm adaptation challenge in VMMs fundamentally arises from the increasing complexity of materials and processes, which renders traditional edge detection methods insufficient. As a technician who has worked extensively in the field, I have learned that only through continuous algorithm iteration combined with optimized optical illumination can these systems maintain high precision and efficiency in precision manufacturing, electronic components, and medical devices. From my years of hands-on experience, both technical expertise and algorithm updates are equally critical.
Disclaimer: The cases and measurement results mentioned in this article are based on my personal experience in actual projects. Actual measurement accuracy and detection rates may vary depending on part material, surface characteristics, optical setup, and on-site conditions. Users should verify results based on their specific circumstances.