Digital Twin-Driven Optimization of Production Projects in the Plastics Manufacturing Industry: Efficiency Enhancement and Economic Benefit Assessment
DOI:
https://doi.org/10.62051/s1fpxz31Keywords:
Digital Twin, Process Simulation, Smart Manufacturing, Injection Molding, Predictive MaintenanceAbstract
Traditional production models in the plastics manufacturing industry face challenges such as lagging process control and frequent quality fluctuations. This paper proposes an optimization solution based on digital twin technology, establishing a technical framework encompassing IoT data collection, high-fidelity modeling, process simulation, and big data analytics to achieve precise control over production processes. By reconstructing and optimizing workflows, a closed-loop management mechanism has been established that bridges physical mapping with accumulated expertise, effectively addressing efficiency and quality issues inherent in conventional manufacturing. The research findings provide a systematic solution for the digital transformation of the plastics manufacturing industry, offering significant guidance for advancing high-quality development within the sector.
Downloads
References
[1] Gu Y, Wu W, Huang L, et al. Digital and Smart Assembly of Laser Target Designation Based on Digital Twin [J]. Lecture Notes in Mechanical Engineering, 2025: 347-356.
[2] Erdogan N, Bhinder M, Murphy J, et al. Digital twin in design and control optimization of marine renewable energy and offshore wind energy systems [J]. Digital Twin Technology for the Energy Sector, 2025: 143-160.
[3] Liu Z. Intelligent monitoring and control of farmland based on edge-cloud collaboration and digital twin for digital energy management: investment benefit analysis [J]. Renewables: Wind, Water, and Solar, 2025, 12 (1).
[4] Fathollahi A, Andresen B. Power Quality Analysis and Improvement of Power-to-X Plants Using Digital Twins: A Practical Application in Denmark [J]. IEEE Transactions on Energy Conversion, 2025: 1-12.
[5] Latifah A, Supangkat S H, Leksono E, et al. Multi-Objective Optimization for Balancing Thermal Comfort and Energy Efficiency Using Genetic Algorithms within a Digital Twin Model [J]. Journal Européen des Systèmes Automatisés, 2025, 58 (5).
[6] Reddy G D, Medikondu N R, Kumar T V, et al. Optimizing smart manufacturing system: a digital twin approach utilizing teaching–learning-based optimization [J]. Cogent Engineering, 2024, 11 (1): Article: 2415670.
[7] Tesanovic M, Bardel T, Karl R, et al. Towards a digital twin: Digitization and model-based optimization of the innovative high-gradient magnetic separator [J]. Current Research in Biotechnology, 2025, 10 (000).
[8] Rocío Mingorance, Pereira D C, Uribetxebarria J, et al. A methodology leveraging digital twins to enhance the operational strategy of manufacturing plants in unexpected scenarios [J].Results in Engineering, 2025, 27 (000).
[9] Mbasso W F, Harrison A, Dagal I, et al. Sensor-Driven PCA–DEA framework for efficiency optimization in manual material handling: application to industrial purification systems with ergonomic redesign validation [J]. Production Engineering, 2026, 20 (1).
[10] Jiang K, Cao Y, Wang Z, et al. Reinforcement Learning for Digital Twin Empowered Ride-Sharing System Optimization [J]. IEEE Network, 2025 (2): 39.
[11] Chen J, Shao Z, Zhu H, et al. Sustainable interior design: A new approach to intelligent design and automated manufacturing based on Grasshopper [J]. Computers & Industrial Engineering, 2023, 183: 109509.
[12] Chen J, Zheng X, Shao Z, et al. Creative interior design matching the indoor structure generated through diffusion model with an improved control network [J]. Frontiers of Architectural Research, 2024.
[13] Chen R. The application of data mining in data analysis [C]. Proceedings of the International Conference on Mathematics, Modeling, and Computer Science (MMCS2022), 2023, 12625: 473-478. SPIE.
[14] Qi R. Interpretable Slow-Moving Inventory Forecasting: A Hybrid Neural Network Approach with Interactive Visualization [J]. 2025.
[15] Zhou Y, Cui C, Rafailov R, et al. Aligning modalities in vision large language models via preference fine-tuning [J]. arXiv preprint arXiv:2402.11411, 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Transactions on Economics, Business and Management Research
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
