ArticleSensors (Basel, Switzerland)2023
A Cost-Effective Method for Automatically Measuring Mechanical Parts Using Monocular Machine Vision.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Two-Dimensional, Vision-Based Measurement for Experimental Characterization of Planar Compliant Mechanisms: A Critical Review and Uncertainty-Aware Framework.Micromachines · 2026Review
- Gap Measurement Method for Railway Switch Machines Based on the Fusion of Deep Vision and Geometric Features.Sensors (Basel, Switzerland) · 2026Article
- Dimensional Accuracy Evaluation of Single-Layer Prints in Direct Ink Writing Based on Machine Vision.Sensors (Basel, Switzerland) · 2025Article
- A Perspective Distortion Correction Method for Planar Imaging Based on Homography Mapping.Sensors (Basel, Switzerland) · 2025Article
- A Method for Measuring Shaft Diameter Based on Light Stripe Image Enhancement.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Automatic measurements via image processing can accelerate measurements and provide comprehensive evaluations of mechanical parts. This paper presents a comprehensive approach to automating evaluations of planar dimensions in mechanical parts, providing significant advancements in terms of cost-effectiveness, accuracy, and repeatability. The methodology employed in this study utilizes a configuration comprising commonly available products in the industrial computer vision market, therefore enabling precise determinations of external contour specifications for mechanical components. Furthermore, it presents a functional prototype for making planar measurements by incorporating an improved subpixel edge-detection method, thus ensuring precise image-based measurements. The article highlights key concepts, describes the measurement procedures, and provides comparisons and traceability tests as a proof of concept for the system. The results show that this vision system did achieve suitable precision, with a mean error of 0.008 mm and a standard deviation of 0.0063 mm, when measuring gauge blocks of varying lengths at different heights. Moreover, when evaluating a circular sample, the system resulted in a maximum deviation of 0.013 mm, compared to an alternative calibrated measurement machine. In conclusion, the prototype validates the methods for planar dimension evaluations, highlighting the potential for enhancing manual measurements, while also maintaining accessibility. The presented system expands the possibilities of machine vision in manufacturing, especially in cases where the cost or agility of current systems is limited.
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Registered trials
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