Measurement and Analysis of Tool Wear Using Vision System

Mehta, S.A.; Singh, Rajput Anurag; Mohata, Yugalkishore; Kiran, M.B.

doi:10.1109/iea.2019.8715209

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…These noises arise in the process of obtaining images for further image processing. The analysis of technical diagnostics systems shown, that the reason of the noises arising is the internal sensors of devices noise and nonuniform illumination of objects of research [18,19]. In this case there is a multiplicative noise on the images, which is removed by the homomorphic filtering method [2].…”

Section: Hybrid Texture Identification Methodsmentioning

confidence: 99%

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Hybrid Texture Identification Method

Volkova

Krylov²

2021

HAIT

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The importance of the modeling mode in systems of computer visual pattern recognition is shown. The purpose of the mode is to determine the types of textures that are present on the images processed in intelligent diagnostic systems. Images processed in technical diagnostic systems contain texture regions, which can be represented by different types of textures - spectral, statistical and spectral-statistical. Texture identification methods, such as, statistical, spectral, expert, multifractal, which are used to identify and analyze texture images, have been analyzed. To determine texture regions on images that are of a combined spectral-statistical nature, a hybrid texture identification method has been developed which makes it possible to take into account the local characteristics of the texture based on multifractal indicators characterizing the non-stationarity and impulsite of the data and the sign of the spectral texture. The stages of the developed hybrid texture identification method are: preprocessing; formation of the primary features vector; formation of the secondary features vector. The formation of the primary features vector is performed for the selected rectangular fragment of the image, in which the multifractal features and the spectral texture feature are calculated. To reduce the feature space at the stage of formation of the secondary identification vector, the principal component method was used. An experimental study of the developed hybrid texture identification method textures on model images of spectral, statistical, spectralstatistical textures has been carried out. The results of the study showed that the developed method made it possible to increase the probability of correct determination of the region of the combined spectral-statistical texture. The developed identification method was tested on images from Brodatz album of textures and images of wear zones of cutting tools, which are processed in intelligent systems of technical diagnostics. The probability of correctly identifying areas of spectral-statistical texture in the images of wear zones of cutting tools averaged 0.9, which is sufficient for the needs of practice

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Section: Hybrid Texture Identification Methodsmentioning

confidence: 99%

“…Spectral methods of texture identification use a Fourier spectrum, which takes into account the directionality of texture elements [6][7][8]. Spectral methods make it possible to move from the analysis of textures in the spatial domain to the analysis of textures in the frequency domain [6].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Hybrid Texture Identification Method

Volkova

Krylov²

2021

HAIT

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“…The monitoring and improvement of overall business processes is highlighted by Gillis et al (2020, February) as a key benefit, and this is supported by Johnson (2019b) who notes "the biggest ROI kicks in, not from tracking assets or measuring the environment, but from optimizing processes in light of that information" (p.5). Machine learning and artificial intelligence technology are being used in many industries (Dave et al, 2020;Mehta et al, 2019;Patel et al, 2020) for tracking the quality of the manufactured product, and thereby product consistency and timely delivery to the end-customer. Product manufacturing lines produce huge amounts of data, and IoT technologies facilitate data analysis to support significant process improvements.…”

Section: Iot Project Benefitsmentioning

confidence: 99%

The Internet of Things in the Corporate Environment

Kiran

Wynn

2022

Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies

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The Internet of Things (IoT) is formed by a set of physical objects with embedded sensors, connected using a network so that they can collect and exchange data. Though the concept looks simple, its deployment in industry has enormous potential to bring major business benefits and radical change. This chapter examines IoT technology and how it is being used in the corporate environment. Based on a review of existing literature and case examples, the various definitions and elements of IoT are discussed, followed by an assessment of how IoT is being used and what benefits are being delivered. Some key emergent themes are then examined – security aspects, the significance of 5G networks, and the need for an IoT strategy and project implementation guidelines. The chapter concludes by outlining possible areas for future research and suggests a step-change in the mega-infrastructure connecting IoT devices is imminent.

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“…Most predominantly various sensor types are selected depending on the output parameters in the machine monitoring process during machining. Basically, sensors of obtaining acoustic emission signals [33], force sensors [34], acceleration sensors [35], current sensors [36], optical sensors [37], etc., are required to full fill the assessment of the condition of the machining process. Each sensor having designated effect of information accessing capability, literature from the past shows that the use of these sensors effectively shows reliable results in identifying the flaws in the machining process.…”

Section: Types Of Sensors and Implementationmentioning

confidence: 99%

A review on multi sensor data fusion technique in CNC machining of tailor-made nanocomposites

et al. 2020

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In this study, a multiple sensor data fusion system is anticipated as essential for monitoring of cutting operations, by identifying suitable sensor locations to obtain feedback signals periodically. The sensor signal derives the failure during machining owing to complex cutting tool geometry even in machining of composites. Nano metal matrix composites (NMMC) being extremely upright in mechanical characteristics, consequently machining of these hybrid nano metal matrix composites reinforced with difficult-to-cut nano particles leads to reduced tool life thereby causing rapid flank wear. Therefore, it is a challenge to identify the wear features caused during machining of tailor made NMMC's, reducing wastage and preventing machine malfunction. This paper presents a comprehensive review on the machining strategies in extreme output conditions which rely on input parameters of speed, feed and depth of cut influencing tool life during CNC machining. This can be achieved only with multiple sensor data fusion technique during CNC machining.

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Measurement and Analysis of Tool Wear Using Vision System

Cited by 9 publications

References 8 publications

Hybrid Texture Identification Method

Hybrid Texture Identification Method

The Internet of Things in the Corporate Environment

A review on multi sensor data fusion technique in CNC machining of tailor-made nanocomposites

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