SYSTEM-OBJECT MODELING AS A TOOL FOR VERIFICATION AND OPTIMIZATION OF THE QUALITY CONTROL SYSTEM IN A PRODUCTION LINE (CASE STUDY OF «DENTALCAST 50 TYPE 4» PRODUCT)
Relevance. In the context of industrial digital transformation and increasing demands for economic efficiency and product quality, methods enabling in-depth analysis and optimization of production systems are becoming particularly significant. This is especially true for high-tech industries, such as the manufacturing of dental materials, where stringent quality control at every stage is critically important. The application of advanced modeling methodologies to address these challenges represents a highly relevant research direction.
Problem. The key problem investigated in this work is the suboptimal architecture of the quality control system in production processes, specifically its terminal, rather than distributed, location within the technological chain. This leads to systemic deficiencies: the irrational use of resources for processing initially defective raw materials and the passage of defects arising during processing into finished products, resulting in significant economic losses and reduced overall production efficiency.
Methods. To address this problem, the study employs the methodology of system-object modeling, which allows the production process to be represented as an integrated system of interconnected elements. Based on this methodology, a simulation model for the production of the dental material «DentalCast 50 Type 4» was developed, serving as the primary tool for verifying the problem and analyzing optimization opportunities.
Results. The computer simulation clearly demonstrated the inefficiency of the existing system with terminal control. Based on the analysis of the results, a comprehensive optimization measure was proposed and modeled – the implementation of a distributed control system throughout the entire technological chain. The simulation confirmed that this approach minimizes operational costs through early defect detection and significantly enhances the overall system efficiency while ensuring consistently high product quality.
Conclusions. The developed approach demonstrates the high practical value of the system-object modeling methodology for solving current production optimization tasks. The obtained results indicate that the transition from terminal to distributed quality control fundamentally improves the production process efficiency. The findings and proposals of the research possess universality and can be successfully applied not only in dental materials science but also in other industrial sectors for optimizing complex technological processes.
Buzov P.A., Zhikharev A.G., Malkush E.V., Kuznetsov A.V. System-Object Modeling as a Tool for Verification and Optimization of the Quality Control System in a Production Line (Case Study of «DentalCast 50 Type 4» Product) // Research result. Information technologies. – T.10, №4, 2025. – P. 79-89. DOI: 10.18413/2518-1092-2025-10-4-0-7
While nobody left any comments to this publication.
You can be first.
1. Adamiya K.G. Development of Methods and Tools for Operational Correction of Production Schedules in a Machining Shop in Small-Batch Production: Candidate of Technical Sciences' Dissertation: 05.13.07. Moscow, 1997. 112 p.
2. Grekul V.I. Design of Information Systems: A Study Guide / V.I. Grekul, G.N. Denishchenko, N.L. Korovkina. 2nd ed., corrected. – Moscow: Internet University of Information Technologies: Binom. Knowledge Lab, 2008 (Ulyanovsk: Ulyanovsk House of Printing). 299 p.
3. Grundig K.G. Design of Industrial Enterprises: Principles, Methods, Practice / K.G. Grundig; [translated from German by A. Starkov]. Moscow: Alpina Business Books: Technopolis, 2007. – 339 p. (Series "Production Management").
4. Ivanova V.S. Introduction to Interdisciplinary Nanomaterials Science / V.S. Ivanova. Moscow: Science-Press, 2005 (Chekhov State Unitary Enterprise. Polygraphic Combination). 205 p.
5. Muzipov Kh.N. Automated Design of Control Tools and Systems [Text]: a tutorial for students of higher education institutions studying in the field of training 220400 – Control in Technical Systems in the Urals Federal District / Kh.N. Muzipov, O.N. Kuzyakov; Ministry of Education and Science of the Russian Federation, Federal State Budgetary Educational Institution of Higher Professional Education "Tyumen State Oil and Gas University". Tyumen: Tyumen State Oil and Gas University, 2011. 167 p.
6. Organization and Planning of Mechanical Engineering Production: [Text]: Production Management: Textbook for University Students Majoring in Mechanical Engineering and Instrument-Making Specialties / [Yu.V. Skvortsov et al.]; ed. Yu.V. Skvortsov, L.A. Nekrasov. Moscow: Vysshaya shkola, 2005. 469 p.
7. Quality Management in the Product Life Cycle: Textbook for Students of All Mechanical Engineering Specialties / V.V. Bespalov, R.Sh. Mansurov, B.V. Ustinov, E.S. Leshchenko; Ministry of Science and Higher Education of the Russian Federation, Federal State Budgetary Educational Institution of Higher Education Nizhny Novgorod State Technical University named after R.E. Alekseev. Nizhny Novgorod: R.E. Alekseev Nizhny Novgorod State Technical University, 2023. 165 p.
8. Shepel V.M. Formation of corporate mentality space – the reputational problem of management // Reputationology. Vol. 11, No. 1–2 (47-48). 2018. 54 p.
9. Kent P. Mathematics in the University Education of Engineers. A Report to the Ove Arup Foundation / P. Kent. R. Noss. – London: London Knowledge, 2003. (дата обращения: 25.10.2025). URL: http://www.lkl.ac. uk/research/ REMIT/Kent-Noss-report- Engineering-Maths.pdf
10. Law A.M. Simulation Modeling and Analysis. McGraw-Hill. 1991. – 759 р.
11. McKenna A.F, Carberry A.R. / Characterizing the Role of Modeling in Innovation // International Jornal of engineering education. 2012, Vol. 28-2, P. 263-269.