«Equipment and technologies for oil and gas complex»

Quality management future: artificial intelligence as a key tool for process optimization

UDC: 006.3:005.591.6
DOI: -

Authors:

¹ National University of Oil and Gas "Gubkin University", Moscow, Russia

Keywords: quality management, quality control, artificial intelligence, production processes, "quality gates", video monitoring, machine control, optimization, rationalization, efficiency, oil and gas equipment

Annotation:

The scientific article considers current trends of quality management of oil and gas equipment manufacturing, focusing on the role of using artificial intelligence (AI). The authors of the article analyze in detail some various quality management processes, including the “quality gate” stages, and highlight the complexities and likely errors associated with human errors and high costs of manual inspections. The article discusses the potential benefits of implementing AI to optimize these processes, focusing on productivity increase, early detection of problems and time and cost reduction. Specific examples of AI application, such as video monitoring and machine quality control, are reviewed, and the results of foreign studies highlighting the effectiveness of AI in reducing the time of various inspections and errors in production are presented. The final part of the article highlights the role of AI-driven software in optimizing process cycles and improving the efficiency of quality management.

Bibliography:

1. Panteleev A.S., Kodash D.V. Postanovka zadachi po intellektualizatsii podzemnykh khranilishch gaza // Oborudovanie i tekhnologii dlya neftegazovogo kompleksa. – 2020. – № 2(116). – S. 79–83. – DOI: 10.33285/1999-6934-2020-2(116)-79-83
2. Panteleev A.S., Shmatin A.K. Problemy tsifrovizatsii i robotizatsii v sovremennykh rossiyskikh realiyakh // Avtomatizirovannoe proektirovanie v mashinostroenii. – 2022. – № 13. – S. 155–158. – DOI: 10.26160/2309-8864-2022-13-155-158
3. Salimova T.A., Vatolkina N.Sh., Makolov V.I. Vektory razvitiya SMK pri perekhode k industrii 4.0 // Standarty i kachestvo. – 2018. – № 8. – S. 44–48.
4. Levchenko E.V. Vliyanie tsifrovizatsii na razvitie sistemy menedzhmenta kachestva // Vestn. SGSEU. – 2018. – № 4(73). – S. 9–14.
5. A Review of Machine Learning Methods Applicable to Quality Issues / C. Hoseini, M.A. Badar, A.M. Shahhosseini, C.J. Kluse // Proceedings of the 11th Annual Int. Conf. on Industrial Engineering and Operations Management, Singapore, March 7. – 2021. – P. 1225–1240. – DOI: 10.46254/AN11.20210250
6. Total Quality Management Through Defect Detection in Manufacturing Processes Using Machine Learning Algorithms / A.S. Kahya, S. Şişmanoğlu, Z. Erçin, H.C. Akdağ // Proceedings of the Int. Symposium for Production Research 2019, Aug. 25–30, Vienna, Austria. – Cham: Springer, 2020. – P. 508–516. – DOI: 10.1007/978-3-030-31343-2_44
7. Models and Methods for Quality Management Based on Artificial Intelligence Applications / N. Yussupova, G. Kovács, M. Boyko, D. Bogdanova // Acta Polytechnica Hungarica. – 2016. – Vol. 13, No. 3. – P. 45–60. – DOI: 10.12700/APH.13.3.2016.3.3
8. Desyat' primerov togo, kak II uluchshaet proizvodstvennye protsessy v 2020 g. – URL: https://vc.ru/ml/145748-10-primerov-togo-kak-iiuluchshaet-proizvodstvennye-processy-v-2020-godu