An approach for surface roughness measurement of helical gears based on image segmentation of region of interest

He, Yan; Zhang, Wei; Li, Yufeng; Wang, Yulin; Wang, Yan; Wang, Shilong

doi:10.1016/j.measurement.2021.109905

Cited by 36 publications

(21 citation statements)

References 31 publications

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“…As image was taken by the vision camera, there was a high probability of poor image clarity. To mitigate the image clarity issue, BLMD-based local adaptive contrast enhancement of image was conducted and function of Region of Interest (ROI) were introduced (Jianbo et al, 2021;Yan et al, 2021). Adaptive thresholding was fitted for the best results for separating out the entire defects from the printed paper background (https://opencv24-python-tutorials.readthedocs.io/en/latest/ py_tutorials/py_tutorials.html; Wendi et al, 2018).…”

Section: Image Processing For Quantitative Benchmarkingmentioning

confidence: 99%

Computer Vision-Based Defect Detection and Severity Classification for Cast Slabs from Sulphur Print Images

Mallick¹,

Palai²,

Kumar³

et al. 2022

IJDSBDA

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Worldwide steel industries are rapidly adopting advance data science, AI, ML kind of technologies for increasing interconnectivity and smart automation of their daily processes. As oil and gas companies are seeking higher quality material from steel manufacturer, dependency of above technologies is growing very fast. Currently, defect detection using computer vision is emerging an important technology which is impressing all the technologist and convincing people to accept it. In conventional steel slab caster, various types of internal defects are generated with low to high severity where Centerline Segregation (CLS) and Internal Crack (IC) evolves most significant type of defects, which are likely undesirable. Since, those defects cannot be avoided due to solidification of liquid steel, dynamic soft reduction technology is universally used for minimizing it. Mannesmann standard images is widely used in steel slab caster area for classifying the slab defects severity by comparing the defects printed in sulphur printing paper with the standard images by visual observation to assure material quality. However, this conventional method is highly error prone due to variation in results for varying judgement by operator to operator. Therefore, a suitable scientific method was required to develop for improving reliability of test result. In this study, a quantitative model for classification of CLS and IC defect severity using advance image analytics technique has been developed and described.

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Section: Image Processing For Quantitative Benchmarkingmentioning

confidence: 99%

Computer Vision-Based Defect Detection and Severity Classification for Cast Slabs from Sulphur Print Images

Mallick¹,

Palai²,

Kumar³

et al. 2022

IJDSBDA

View full text Add to dashboard Cite

show abstract

“…Rifai et al 11 evaluated the surface roughness of workpieces under different machining processes (turning, slot milling, and side milling) using convolutional neural networks. He et al 12 proposed a novel vision method for helical gear roughness inspection by combining the region of interest extraction algorithm with convolutional neural networks. It is worth noting that the inspection methods in the literature [10][11][12] are all traditional deep-learning models.…”

Section: Introductionmentioning

confidence: 99%

Illumination-robust milling surface roughness machine vision inspection based on MAML++ network

Fang

Shu

et al. 2022

Opt. Eng.

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.Existing roughness machine vision inspection methods based on designed indices require the artificial construction of features associated with roughness based on image information. Such methods are less universal but highly demanding for the imaging environment and cannot be directly applied to general industrial environments. The roughness machine vision inspection methods based on traditional deep learning, on the other hand, rely heavily on a large number of training samples, and the model needs to be retrained when the recognition of new categories is required by the inspection task. Therefore, a machine vision inspection method for milling surface roughness grade based on the improved model-agnostic meta-learning (MAML++) network was proposed to address the above problems. This method could not only automatically extract the image features associated with roughness but also recognize the surface roughness grade of milling workpieces by a small number of labeled samples. It reduced the need for sample size and can recognize new categories without retraining the model. The experimental results showed that the method exhibited high accuracy in roughness inspection tasks with different sample distributions and had promising robustness to complex light environments. In addition, in terms of the requirement for recognizing new roughness categories, a low-cost and fast roughness inspection task could be accomplished by acquiring fewer labeled samples. In summary, the proposed model has the potential to be a fast inspection system that can be applied to surface roughness inspection for most industrial applications. It provides the technology for inline inspection of machine vision roughness in general industrial lighting environments.

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“…This model may be used to optimize the cutting process for efficient and economical manufacturing by predicting tool wear and surface roughness in the turning process. CNN algorithms have gained popularity in the assessment of surface roughness in recent years [22][23][24][25]. Because feature extraction is included into the network during the convolution phase, this technique eliminates it.…”

Section: Introductionmentioning

confidence: 99%

“…All offline and online approaches can estimate surface roughness parameters based on evidence from the surface image [27]. The stylus tracing (ST) technique became the most widely known way of evaluating the surface properties of components in recent decades, due to the implementation of tactile profilometers [24,25]. The ST involves measuring the texture of the surface and calculating the R a roughness parameter but in contact with the surface.…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Evaluation in Turning of Nimonic C263 Super Alloy Using 2D DWT Histogram Equalization

Kumar¹,

Thenmozhi²,

Bommi

et al. 2022

Journal of Nanomaterials

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Surface roughness of specimens is an important area of research since it influences the performance of machined parts. Meanwhile, employing a vision system to judge the roughness of the machined surface of specimens via captured images acquired from the specimen is an innovative and extensively used method. In this investigation, a vision system is used to capture the SEM images of the machined surface. The two-dimensional images of the machined surface of the Nimonic263 alloy are used to approximate the profile of the surface of specimens in finish turning. Surface roughness was detected in simulated images of specimens in a variety of machining conditions using the imaging technology. In this research work, the surface texture is extracted using a technique that combines 2D surface images and wavelet transform approach. The 2D wavelet transform has the capability to disintegrate a machined surface image into multiresolution depiction for several surface characteristics and can be utilized for surface evaluation. The difference in the histogram frequency of an illuminated region of interest (ROI) from turned surface images was analyzed to aid in the evaluation of surface roughness with an average prediction error of less than 3.2%.

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An approach for surface roughness measurement of helical gears based on image segmentation of region of interest

Cited by 36 publications

References 31 publications

Computer Vision-Based Defect Detection and Severity Classification for Cast Slabs from Sulphur Print Images

Computer Vision-Based Defect Detection and Severity Classification for Cast Slabs from Sulphur Print Images

Illumination-robust milling surface roughness machine vision inspection based on MAML++ network

Surface Roughness Evaluation in Turning of Nimonic C263 Super Alloy Using 2D DWT Histogram Equalization

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