Novel monitoring method for belt wear state based on machine vision and image processing under grinding parameter variation

Wang, Nina; Zhang, Guangpeng; Ren, Lijuan; Pang, Wanjing; Li, Yongchang

doi:10.1007/s00170-021-08393-5

Cited by 10 publications

(2 citation statements)

References 25 publications

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“…Wang [91] , Wang [85] 和 Wang [92] 等在砂带磨削过程中基于砂带图像分析和分类提升算法, 对砂带的磨损类型进行识别和分析. Farzaneh [93] 等采用计算机图像处 [94] ,…”

Section: F O R R E V I E W O N L Yunclassified

Research on grinding wheel wear measurement methods: Current status and future perspectives

LI,

BAO,

WANG

et al. 2024

Sci. Sin.-Tech.

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Section: F O R R E V I E W O N L Yunclassified

Research on grinding wheel wear measurement methods: Current status and future perspectives

LI,

BAO,

WANG

et al. 2024

Sci. Sin.-Tech.

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“…Nontheless, Radtke et al, (2022) presented research study referring to High-image resolution imaging is wanted to catalog the form of mobile phenotypes and multicellular ecosystems found in metazoan tissues. Wang et al, (2022) presented the proposed technique that solves the trouble of the dependence and sensitivity of the tracking version at the variant of the grinding parameters within the system of abrasive belt put on tracking, and it improves the adaptability and flexibility of the tracking technique. Liu et al, (2022) findings relating to image processing might also additionally offer beneficial details for organising finite detail standards of the optic nerve head towards understanding excessive intraocular pressure, and sooner or later make contributions to the early analysis of glaucoma.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning and Computer Vision: Machine Learning Analysis and Image Processing of Puromycin Treated Microscopy

Owoeye¹,

Ojo-Omoniyi²

2022

Int.J.Curr.Microbiol.App.Sci

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Digital image processing involves the usage of a functional algorithm to process images with special regions of interest. In most case scenarios, this is termed as an active aspect of digital signal processing; image processing comes with several rewards over analog image processing. Its relevance and application spans Autonomous Vehicles, Biometric fingerprint technologies as well as Face recognition applications. Reliable statistics through feature engineering from the image can be extracted and in turn serve as focus points of deep learning insights. Besides, its application in monitoring Climatic changes, Agricultural crop yields, security measures, industrial manufacturing as well as medical fields exponentially advances each day. Meanwhile, deep learning being a feature of Artificial Intelligence has brought forward several useful models that is being used as transfer base for further model accuracies and baselines. In this study, we make use of a certain Microscopy datasets, sampling one of the images for digital processing, in order to gain useful insights through Cropped Quantizing, Laplace Edge Detection and Gaussian noise with sigma methods respectively. The statistical results of the extracted image features through Support Vector Method (SVM) give accuracy of up to 75%.

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State-of-the-art review of applications of image processing techniques for tool condition monitoring on conventional machining processes

Pimenov,

da Silva,

Ercetin

et al. 2023

Int J Adv Manuf Technol

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In conventional machining, one of the main tasks is to ensure that the required dimensional accuracy and the desired surface quality of a part or product meet the customer needs. The successful accomplishment of these parameters in milling, turning, milling, drilling, grinding and other conventional machining operations directly depends on the current level of tool wear and cutting edge conditions. One of the proven non-contact methods of tool condition monitoring (TCM) is measuring systems based on image processing technologies that allow assessing the current state of the machined surface and the quantitative indicators of tool wear. This review article discusses image processing for tool monitoring in the conventional machining domain. For the first time, a comprehensive review of the application of image processing techniques for tool condition monitoring in conventional machining processes is provided for both direct and indirect measurement methods. Here we consider both applications of image processing in conventional machining processes, for the analysis of the tool cutting edge and for the control of surface images after machining. It also discusses the predominance, limitations and perspectives on the application of imaging systems as a tool for controlling machining processes. The perspectives and trends in the development of image processing in Industry 4.0, namely artificial intelligence, smart manufacturing, the internet of things and big data, were also elaborated and analysed.

show abstract

Novel monitoring method for belt wear state based on machine vision and image processing under grinding parameter variation

Cited by 10 publications

References 25 publications

Research on grinding wheel wear measurement methods: Current status and future perspectives

Research on grinding wheel wear measurement methods: Current status and future perspectives

Deep Learning and Computer Vision: Machine Learning Analysis and Image Processing of Puromycin Treated Microscopy

State-of-the-art review of applications of image processing techniques for tool condition monitoring on conventional machining processes

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