A Cost-Effective Method for Automatically Measuring Mechanical Parts Using Monocular Machine Vision

Barca, Luiz Fernando; Pimenta, Tales C.

doi:10.3390/s23135994

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…The diameter-measurement formula was derived from this calibration process. Nogueira et al [ 19 ] presented a comprehensive method for the automated evaluation of planar dimensions of mechanical parts. This method significantly advanced cost-effectiveness, accuracy, and repeatability.…”

Section: Introductionmentioning

confidence: 99%

A Method for Measuring Shaft Diameter Based on Light Stripe Image Enhancement

Li,

Xu,

Liu

et al. 2024

Sensors

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When the workpiece surface exhibits strong reflectivity, it becomes challenging to obtain accurate key measurements using non-contact, visual measurement techniques due to poor image quality. In this paper, we propose a high-precision measurement method shaft diameter based on an enhanced quality stripe image. By capturing two stripe images with different exposure times, we leverage their different characteristics. The results extracted from the low-exposure image are used to perform grayscale correction on the high-exposure image, improving the distribution of stripe grayscale and resulting in more accurate extraction results for the center points. The incorporation of different measurement positions and angles further enhanced measurement precision and robustness. Additionally, ellipse fitting is employed to derive shaft diameter. This method was applied to the profiles of different cross-sections and angles within the same shaft segment. To reduce the shape error of the shaft measurement, the average of these measurements was taken as the estimate of the average diameter for the shaft segment. In the experiments, the average shaft diameters determined by averaging elliptical estimations were compared with shaft diameters obtained using a coordinate measuring machine (CMM) the maximum error and the minimum error were respectively 18 μm and 7 μm; the average error was 11 μm; and the root mean squared error of the multiple measurement results was 10.98 μm. The measurement accuracy achieved is six times higher than that obtained from the unprocessed stripe images.

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Section: Introductionmentioning

confidence: 99%

A Method for Measuring Shaft Diameter Based on Light Stripe Image Enhancement

Li,

Xu,

Liu

et al. 2024

Sensors

View full text Add to dashboard Cite

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Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision

Li,

Liao,

Zhang

et al. 2024

Applied Sciences

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In-process measurement of machining precision is of great importance to advanced manufacturing, which is an essential technology to realize compensation machining. In terms of cost-effectiveness and repeatability of computer vision, it has become a trend to replace traditional manual measurement with computer vision measurement. In this paper, an in-process measurement method is proposed to improve precision and reduce the costs of machining precision. Firstly, a universal features model framework of machining parts is established to analyze the CAD model and give standard information on the machining features. Secondly, a window generator is proposed to adaptively crop the image of the machining part according to the size of features. Then, the automatic detection of the edges of machining features is performed based on regions of interest (ROIs) from the cropped image. Finally, the measurement of machining precision is realized through a Hough transform on the detected edges. To verify the effectiveness of the proposed method, a series of in-process measurement experiments were carried out on machined parts with various features and sheet metal parts, such as dimensional accuracy measurement tests, straightness measurement tests, and roundness measurement tests under the same part conditions. The best measurement accuracy of this method for dimensional accuracy, straightness, and roundness were 99%, 97%, and 96%, respectively. In comparison, precision measurement experiments were conducted under the same conditions using the Canny edge detection algorithm, the sub-pixel edge detection algorithm, and the Otsu–Canny edge detection algorithm. Experimental results show that the feature-model-based in-process measurement of machining precision using computer vision demonstrates superiority and effectiveness among various measurement methods.

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A Cost-Effective Method for Automatically Measuring Mechanical Parts Using Monocular Machine Vision

Cited by 2 publications

References 31 publications

A Method for Measuring Shaft Diameter Based on Light Stripe Image Enhancement

A Method for Measuring Shaft Diameter Based on Light Stripe Image Enhancement

Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision

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