Development of Automated Quality Control and Management Systems at Industrial Enterprises

Kurdyukova, Natalia O.; Menshikova, Margarita A.; Dzhamaldinova, Marina D.

doi:10.1109/itqmis51053.2020.9322970

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…Now, the majority of products, services and processes can be controlled in an automated way across the value chain. Intelligent machines, smart factories and operators armed with augmented reality technologies can communicate and collaborate to identify and remove the root causes of production defects, take immediate measures to prevent defects and avoid production and product failures (Sader et al , 2019; Kurdyukova et al , 2020; Lim, 2020).…”

Section: Results Of the Literature Reviewmentioning

confidence: 99%

Quality 4.0: literature review analysis, definition and impacts of the digital transformation process on quality

Dias

Carvalho

Sampaio

2021

IJQRM

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PurposeThe changes brought forth by the Digital Transformation have an impact on the way we think, assess and manage Quality. While the concept of Quality 4.0 has resulted from these changes, there is still limited understanding and unclear definitions in this new era of Quality Management.Design/methodology/approachThere is a clear opportunity to review and analyze the state-of-the-art of Quality 4.0, its main research efforts, topics and directions. To do so, a bibliometric analysis and descriptive/mapping literature review were performed.FindingsThere is a clear focus on the implications of technology for Quality, but other relevant topics include business management and strategy models and systems, as well as human or soft factors. Results reveal an increasing interest in Quality 4.0 as well as a link between topics.Originality/valueTogether with the review, analysis and digest of the literature, we offer our own contribution to a commonly accepted definition Quality 4.0.

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Section: Results Of the Literature Reviewmentioning

confidence: 99%

Quality 4.0: literature review analysis, definition and impacts of the digital transformation process on quality

Dias

Carvalho

Sampaio

2021

IJQRM

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“…U skladu sa tim, predložena su tri nova principa upravljanja kvalitetom [11]:  Prilagodljivost,  Inovativnost i digitalizacija, i  Razvoj programa i alata za upravljanje kvalitetom zasnovanog na digitalnim tehnologijama. Savremene tehnologije omogućuju da se otkrije potencijalni uzrok kvara, i otkloni pre nego što prouzrokuje dodatne troškove [12,13,14]. Lociranje kvara i obustava rada mašine, kako bi se izbegli proizvodi sa greškom, sada je moguće u bilo kojoj fazi proizvodnje [15].…”

Section: Osnove čEtvrte I Pete Industrijske Revolucijeunclassified

The Quality 5.0 model in the function of production personalization and user satisfaction

Tadić-Stanić¹

2023

Tehnika

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Every industrial revolution represents a sudden burst, which sets off an avalanche of changes and novelties. The Fourth and Fifth revolutions were marked by the development and use of innovative technology, which permanently changed the way business systems function. Within Industry 4.0, the positive impact of technology on quality performance was discovered, and Industry 5.0 focused that impact on creating personalized products and increasing customer satisfaction. The importance of this work is reflected in the conceptual model of Quality 5.0, which theoretically and illustrated presents the most important features in the service of satisfaction of interested parties.

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“…Our case refers to the automation of quality control in the photovoltaic (PV) industry. While automated quality control is common in many industries [27][28][29], the PV industry is lacking behind because of complex production environments. Consequently, the PV industry often still relies on manual human controls of PV wafers which is both tedious and costly and therefore offers grand potentials for improvements via automated inspections based on DL models [14,30].…”

Section: Case Descriptionmentioning

confidence: 99%

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

Harl¹,

Herchenbach²,

Kruschel³

et al. 2022

Preprint

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In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for general image recognition tasks, application in industry is often precluded for three reasons: 1) large pre-trained DNNs are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre-training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal issues for companies. As a remedy, we study neural networks for CV that we train from scratch. For this purpose, we use a real-world case from a solar wafer manufacturer. We find that our neural networks achieve similar performances as pre-trained DNNs, even though they consist of far fewer parameters and do not rely on third-party datasets.

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Development of Automated Quality Control and Management Systems at Industrial Enterprises

Cited by 6 publications

References 1 publication

Quality 4.0: literature review analysis, definition and impacts of the digital transformation process on quality

Quality 4.0: literature review analysis, definition and impacts of the digital transformation process on quality

The Quality 5.0 model in the function of production personalization and user satisfaction

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

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