DEVELOPMENT OF A SYSTEM FOR OPERATIONAL CONTROL AND QUALITY ASSURANCE OF PRODUCTS PRODUCED BY ADDITIVE MANUFACTURING (DMLS) USING COMPUTER VISION AND MACHINE LEARNING
Keywords:
Quality management, mechanical engineering, digitalization, Industry 4.0, ISO 9001, IATF 16949, MES, QMS, SPC, Internet of Things (IoT), Big Data, Machine learning (ML), Artificial intelligence (AI), Digital twin, Predictive qualityAbstract
In the context of increasing product complexity and stricter market requirements, quality management in mechanical engineering is transforming from a predominantly reactive management to an integrated digital ecosystem. The article analyzes the evolution of digital tools in this area: from the automation of processes regulated by international quality standards (ISO 9001, IATF 16949) to the use of advanced intelligent systems based on artificial intelligence and machine learning. The stages of digitalization are considered: from the implementation of basic information systems (ERP, MES, QMS, SPC) for data collection and automation to real-time data integration via the Internet of Things and the concept of a digital twin. Particular attention is paid to AI/ML applications for predictive quality, automated visual inspection and defect root cause analysis. The main technical, economic and organizational problems associated with digital transformation are identified. Development prospects, including fully autonomous systems and the use of blockchain technologies, as well as associated risks are outlined. The conclusion is made about the critical importance of digital tools for achieving a high level of quality, transition to proactive management and increasing competitiveness, the synergy of the analytical capabilities of AI and human engineering intuition is emphasized.
References
Санин С. Н., Пелипенко Н. А. Настройка мобильного станкоробота на обработку крупногабаритной кольцевой детали / Санин С. Н., Пелипенко Н. А. // СТИН. — 2022. — № 3. — С. 2-4.
Oakland J. S. Total Quality Management and Operational Excellence. – [London]: Routledge, 2014. – [Х] с.
Artificial Intelligence and Digital Twin for Smart Manufacturing Quality Control / L. Wang, R. Li // International Journal of Production Research. – 2021. – Vol. 59, no. 12. – P. 3601–3618.
Deming W. E. Out of the Crisis. – Cambridge, MA: MIT Press, 2000. – [Х] с.
ISO 9001:2015. Quality management systems – Requirements. – [Geneva]: International Organization for Standardization, 2015. – [Х] с.
IATF 16949:2016. Quality management system requirements for automotive production and relevant service parts organizations. – [Southfield, MI]: International Automotive Task Force, 2016. – [Х] с.
The Machine That Changed the World / J. P. Womack, D. T. Jones, D. Roos. – New York: Harper Perennial, 1990. – [Х] с.
MES/MOM Functionality & Standards White Paper / MESA International. – Tempe, AZ: MESA International, 2019. – [Х] с.
Montgomery D. C. Introduction to Statistical Quality Control. – Hoboken, NJ: John Wiley & Sons, 2012. – [Х] с.
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems / J. Lee, B. Bagheri, H. A. Kao // Manufacturing Letters. – 2015. – Vol. 3. – P. 18–23.
The Digital Twin Paradigm for Future NASA Missions / E. Glaessgen, D. Stargel. – Moffett Field, CA: NASA Ames Research Center, 2012. – [Х] с.
Deep Learning for Industrial Visual Inspection: A Survey / Y. Luo, X. Li // IEEE Transactions on Industrial Informatics. – 2020. – Vol. 16, no. 2. – P. 779–792.
