Heat Transfer and Temperature Characteristics of a Working Digital Camera

Zhou, Shichao; Zhu, Haibin; Ma, Qingsong; Ma, Shiping

doi:10.3390/s20092561

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…By analyzing the test data of pivoting center shift of flexible joint, it can be seen that the higher frequency of original data acquisition will lead to the excessive detection noise, and the proper reduction of the frequency will not distort the curve of the detection results (Tao et al, 2020 ; Zhang, 2020 ; Moru and Borro, 2021 ). Analysis shows that the high precision detection of pivoting center shift of flexible joint can be achieved when the image acquisition frequency reaches 21 Hz (Pohanka et al, 2018 ; Damirchi et al, 2019 ; Marina et al, 2020 ; Zhou et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Research on Intelligent Identification of Pivoting Center and Smooth Processing of Test Data for Flying Flexible Joint

Wen

Tan

2021

Front. Neurorobot.

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In this paper, a method of intelligent identification and data smooth processing of flying flexible joint pivoting center based on machine vision is proposed. The intelligent identification is realized by the following process: first of all the geometric center of the two markers attached to the flying body is located on a straight line at a certain angle to the center-line of the measured pivoting body, secondly then continuous image sampling is carried out by industrial camera when the marker swings with the pivoting body, and image data is transmitted through a data interface to an industrial computer, Finally the image processing module de-noises the image, removes the background and locates the markers to obtain the plane coordinates of the markers in the coordinate system of the test system. The data smooth of obtained coordinates is carried outby Matlab software including the following steps: the coordinates of the mark points detected based on machine vision are optimized to obtain the smooth curve by fitting of the parabola and arc. Then the coordinates of the points on the curve are used to optimize the coordinates of the marked points from measurement. The optimized coordinate values are substituted into the calculation module of pivoting center, so the average pivoting center of the sampling interval of two images is calculated according to the mathematical model to approach the instantaneous pivoting center during the motion of the pivoting body. The result processing module displays and records the curve of pivoting center shift directly and effectively. Finally, it is validated by simulation and experiments that the precision of pivoting center measured by such measuring system is ~0.5%.

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Section: Methodsmentioning

confidence: 99%

Research on Intelligent Identification of Pivoting Center and Smooth Processing of Test Data for Flying Flexible Joint

Wen

Tan

2021

Front. Neurorobot.

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“…However, in measurement technologies based on vision and structured light, the imaging quality of the camera determines the measurement accuracy. In recent years, attention has been drawn to deviations in camera imaging caused by temperature [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Improving Measurement Accuracy of Deep Hole Measurement Instruments through Perspective Transformation

Zhao,

Du,

2024

Sensors

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Deep hole measurement is a crucial step in both deep hole machining and deep hole maintenance. Single-camera vision presents promising prospects in deep hole measurement due to its simple structure and low-cost advantages. However, the measurement error caused by the heating of the imaging sensor makes it difficult to achieve the ideal measurement accuracy. To compensate for measurement errors induced by imaging sensor heating, this study proposes an error compensation method for laser and vision-based deep hole measurement instruments. This method predicts the pixel displacement of the entire field of view using the pixel displacement of fixed targets within the camera’s field of view and compensates for measurement errors through a perspective transformation. Theoretical analysis indicates that the perspective projection matrix changes due to the heating of the imaging sensor, which causes the thermally induced measurement error of the camera. By analyzing the displacement of the fixed target point, it is possible to monitor changes in the perspective projection matrix and thus compensate for camera measurement errors. In compensation experiments, using target displacement effectively predicts pixel drift in the pixel coordinate system. After compensation, the pixel error was suppressed from 1.99 pixels to 0.393 pixels. Repetitive measurement tests of the deep hole measurement instrument validate the practicality and reliability of compensating for thermal-induced errors using perspective transformation.

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Full-field microscale strain measurements of a nitinol medical device using digital image correlation

Aycock

Weaver

Paranjape³

et al. 2021

Journal of the Mechanical Behavior of Biomedical Materials

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Computational modeling and simulation are commonly used during the development of cardiovascular implants to predict peak strains and strain amplitudes and to estimate the associated durability and fatigue life of these devices. However, simulation validation has historically relied on comparison with surrogate quantities like force and displacement due to barriers to direct strain measurement-most notably, the small spatial scale of these devices. We demonstrate the use of microscale two-dimensional digital image correlation (2D-DIC) to directly characterize full-field surface strains on a nitinol device coupon under emulated physiological loading. Experiments are performed using a digital optical microscope and a custom, temperature-controlled load frame. Following applicable recommendations from the International DIC Society, hardware and environmental heating studies, noise floor analyses, and inand out-of-plane rigid body translation studies are first performed to characterize the microscale DIC setup. Uniaxial tension experiments are also performed using a polymeric test specimen up to nominal stains of 5%. Sub-millimeter fields of view and sub-micron displacement accuracies (9 nm mean error) are achieved, and systematic (mean) and random (standard deviation) errors in strain are each estimated to be approximately 1,000 µ. The system is then demonstrated by acquiring measurements at the root of a 300 µm-wide nitinol device strut undergoing fixed-free cantilever bending motion. Lüderslike transformation bands are observed originating from the tensile side of the strut that spread toward the neutral axis at an angle of approximately 55°. Optical microscale 2D-DIC setups like that demonstrated herein will be useful in future studies for characterizing cardiovascular implant micromechanics, validating computational models, and guiding the development of next-generation material models for simulating superelastic nitinol.

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Heat Transfer and Temperature Characteristics of a Working Digital Camera

Cited by 6 publications

References 31 publications

Research on Intelligent Identification of Pivoting Center and Smooth Processing of Test Data for Flying Flexible Joint

Research on Intelligent Identification of Pivoting Center and Smooth Processing of Test Data for Flying Flexible Joint

Improving Measurement Accuracy of Deep Hole Measurement Instruments through Perspective Transformation

Full-field microscale strain measurements of a nitinol medical device using digital image correlation

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