طراحی سیستم دوقلوی دیجیتال ماشین تزریق پلاستیک برای بهبود زمان چرخه تولید (مورد مطالعه: گروه صنعتی انتخاب)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی صنایع و آینده پژوهی, دانشکده فنی و مهندسی, دانشگاه اصفهان, اصفهان, ایران

2 دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه صنعتی اصفهان، اصفهان، ایران

چکیده

تحولات فناوری در صنعت ۴.۰ به‌ویژه با به‌کارگیری فناوری‌هایی مانند دوقلوی دیجیتال، اینترنت اشیاء و هوش مصنوعی باعث تغییرات چشم‌گیری در فرآیندهای تولید شده است. این پژوهش به بررسی دوقلوی دیجیتال به‌عنوان یکی از فناوری‌های کلیدی این انقلاب صنعتی در کارخانه‌ای وابسته به گروه صنعتی انتخاب می‌پردازد. این سیستم شامل سه زیرسیستم اصلی اجمع‌آوری داده‌های لحظه‌ای، شبیه‌سازی با استفاده از نرم‌افزار MoldFlow، و هوش مصنوعی مبتنی بر شبکه عصبی در محیط پایتون است. همچنین یک رابط کاربری برای تعامل با سیستم طراحی شده است. شبیه‌ساز دوقلوی دیجیتال می‌تواند با تنظیم بلادرنگ پارامترهایی مانند فشار، سرعت تزریق، دمای قالب و آب خنک‌کننده، زمان چرخه تولید را کاهش داده و در عین حال کیفیت محصول را حفظ کند. نتایج نشان داد که اجرای این سیستم به کاهش ۱۳.۳ درصدی در زمان چرخه تولید و افزایش نرخ تولید منجر شده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Designing a Digital Twin System for Plastic Injection Molding Machine to Improve Production Cycle Time (Case Study: Entekhab Industrial Group)

نویسندگان [English]

  • Mohamad Javad Aghalar 1
  • Kamran Kianfar 1
  • Alireza Goli 1
  • Mohammad Abdeyazdan 2
1 Department of Industrial Engineering and Futures Studies, Faculty of Engineering, University of Isfahan, Isfahan, Iran
2 Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
چکیده [English]

Technological advancements in Industry 4.0, particularly through the integration of technologies such as digital twins, the Internet of Things (IoT), and artificial intelligence (AI), have led to significant changes in manufacturing processes. This study investigates the implementation of a digital twin system as one of the key technologies of this industrial revolution in a factory affiliated with the Entekhab Industrial Group. The proposed system consists of three main subsystems: real-time data acquisition, simulation using MoldFlow software, and AI based on a neural network developed in the Python environment. Additionally, a user interface was designed for system interaction. The digital twin simulator is capable of reducing production cycle time while maintaining product quality by real-time adjustment of parameters such as pressure, injection speed, mold temperature, and coolant water temperature. The results showed that implementing this system led to a 13.3% reduction in cycle time and an increase in production rate.

کلیدواژه‌ها [English]

  • Digital Twin
  • Plastic Injection Simulation
  • Neural Network
  • Production Cycle Time
  • Entekhab Industrial Group

مراجع

Behera, R., & Das, K. (2017). A Survey on Machine Learning: Concept, Algorithms and Applications. International Journal of Innovative Research in Computer and Communication Engineering, 2.
Bhandal, R., McRiton, R., Kavanagh, R. E., & Brown, A. (2022). The application of digital twin technology in operations and supply chain management: a bibliometric review. Supply Chain Management-an International Journal, 27(2), 182-206. https://doi.org/10.1108/scm-01-2021-0053
Bibow, P., Dalibor, M., Hopmann, C., Mainz, B., Rumpe, B., Schmalzing, D., Schmitz, M., & Wortmann, A. (2020, 2020//). Model-Driven Development of a Digital Twin for Injection Molding. Advanced Information Systems Engineering, Cham.
Brunthaler, J., Grabski, P., Sturm, V., Lubowski, W., & Efrosinin, D. (2022). On the Problem of State Recognition in Injection Molding Based on Accelerometer Data Sets. SENSORS, 22(16), Article 6165. https://doi.org/10.3390/s22166165
Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, 49, 86-97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004
Erol, T., Mendi, A. F., & Doğan, D. (2020, 22-24 Oct. 2020). The Digital Twin Revolution in Healthcare. 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT),
Huang, H., Ji, T., & Xu, X. (2024). An adaptable Digital Twin model for manufacturing. Manufacturing Letters, 41, 1163-1169. https://doi.org/https://doi.org/10.1016/j.mfglet.2024.09.142
Hürkamp, A., Gellrich, S., Ossowski, T., Beuscher, J., Thiede, S., Herrmann, C., & Dröder, K. (2020). Combining Simulation and Machine Learning as Digital Twin for the Manufacturing of Overmolded Thermoplastic Composites. Journal of Manufacturing and Materials Processing, 4(3).
Ivanov, D., Dolgui, A., Das, A., & Sokolov, B. (2019). Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility. In D. Ivanov, A. Dolgui, & B. Sokolov (Eds.), HANDBOOK OF RIPPLE EFFECTS IN THE SUPPLY CHAIN (Vol. 276, pp. 309-332). https://doi.org/10.1007/978-3-030-14302-2_15
Kitayama, S., Ishizuki, R., Takano, M., Kubo, Y., & Aiba, S. (2019). Optimization of mold temperature profile and process parameters for weld line reduction and short cycle time in rapid heat cycle molding. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 103(5-8), 1735-1744. https://doi.org/10.1007/s00170-019-03685-3
Koch, V., Kuge, S., Geissbauer, R., & Schrauf, S. (2014). Industry 4.0: Opportunities and challenges of the industrial internet. Strategy & PwC, 5-50.
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016-1022. https://doi.org/https://doi.org/10.1016/j.ifacol.2018.08.474
Kuo, C. C., & Xu, W. C. (2018). Effects of different cooling channels on the cooling efficiency in the wax injection molding process. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 98(1-4), 887-895. https://doi.org/10.1007/s00170-018-2345-7
Lacueva-Pérez, F. J., Hermawati, S., Amoraga, P., Salillas-Martínez, R., Hoyo-Alonso, R. d., & Lawson, G. (2022). SHION (Smart tHermoplastic InjectiON): An Interactive Digital Twin Supporting Real-Time Shopfloor Operations. IEEE Internet Computing, 26(3), 23-32. https://doi.org/10.1109/MIC.2020.3047349
Lee, D., & Lee, S. (2021). Digital Twin for Supply Chain Coordination in Modular Construction. APPLIED SCIENCES-BASEL, 11(13), Article 5909. https://doi.org/10.3390/app11135909
Li, X., Cao, J. R., Liu, Z. G., & Luo, X. G. (2020). Sustainable Business Model Based on Digital Twin Platform Network: The Inspiration from Haier's Case Study in China. SUSTAINABILITY, 12(3), Article 936. https://doi.org/10.3390/su12030936
Liao, Y., Deschamps, F., Loures, E. d. F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609-3629. https://doi.org/10.1080/00207543.2017.1308576
Liau, Y. Y., Lee, H., & Ryu, K. (2018). Digital Twin concept for smart injection molding. IOP Conference Series: Materials Science and Engineering, 324, 012077. https://doi.org/10.1088/1757-899X/324/1/012077
Lin, X., Chen, W., Zhou, Z., Li, J., Zhao, Y., & Zhang, X. (2025). A five-dimensional digital twin framework driven by large language models-enhanced RL for CNC systems. Robotics and Computer-Integrated Manufacturing, 95, 103009. https://doi.org/https://doi.org/10.1016/j.rcim.2025.103009
Mandolla, C., Petruzzelli, A. M., Percoco, G., & Urbinati, A. (2019). Building a digital twin for additive manufacturing through the exploitation of blockchain: A case analysis of the aircraft industry. Computers in Industry, 109, 134-152. https://doi.org/10.1016/j.compind.2019.04.011
Marrone, S., Papa, C., & Sansone, C. (2021). Effects of hidden layer sizing on CNN fine-tuning. Future Generation Computer Systems, 118, 48-55. https://doi.org/https://doi.org/10.1016/j.future.2020.12.020
Martowibowo, S. Y., & Kaswadi, A. (2017). Optimization and Simulation of Plastic Injection Process using Genetic Algorithm and Moldflow. CHINESE JOURNAL OF MECHANICAL ENGINEERING, 30(2), 398-406. https://doi.org/10.1007/s10033-017-0081-9
Mazzei, D., Baldi, G., Fantoni, G., Montelisciani, G., Pitasi, A., Ricci, L., & Rizzello, L. (2020). A Blockchain Tokenizer for Industrial IOT trustless applications. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 105, 432-445. https://doi.org/10.1016/j.future.2019.12.020
Mianehrow, H., & Abbasian, A. (2017). Energy monitoring of plastic injection molding process running with hydraulic injection molding machines. JOURNAL OF CLEANER PRODUCTION, 148, 804-810. https://doi.org/10.1016/j.jclepro.2017.02.053
Modoni, G. E., Stampone, B., & Trotta, G. (2022). Application of the Digital Twin for in process monitoring of the micro injection moulding process quality. Computers in Industry, 135, 103568. https://doi.org/https://doi.org/10.1016/j.compind.2021.103568
Passos, D., & Mishra, P. (2022). A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks. Chemometrics and Intelligent Laboratory Systems, 223, 104520. https://doi.org/https://doi.org/10.1016/j.chemolab.2022.104520
Rehmer, A., Klute, M., Heim, H.-P., & Kroll, A. (2024). Chapter 4 - A Digital Twin for part quality prediction and control in plastic injection molding. In P. Mercorelli, W. Zhang, H. Nemati, & Y. Zhang (Eds.), Modeling, Identification, and Control for Cyber- Physical Systems Towards Industry 4.0 (pp. 79-109). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-32-395207-1.00014-7
Sapounas, I., Vosniakos, G. C., & Papazetis, G. (2020). A simulation-based robust methodology for operator guidance on injection moulding machine settings. INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM, 14(2), 519-533. https://doi.org/10.1007/s12008-020-00646-z
Singh, G., Pradhan, M. K., & Verma, A. (2018). Multi Response optimization of injection moulding Process parameters to reduce cycle time and warpage MATERIALS TODAY-PROCEEDINGS, 
Sleiti, A. K., Kapat, J. S., & Vesely, L. (2022). Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems. Energy Reports, 8, 3704-3726. https://doi.org/https://doi.org/10.1016/j.egyr.2022.02.305
Sun, X., Zhang, F., Niu, X., & Wang, J. (2024). A digital twin commissioning method for machine tools based on scenario simulation. Journal of Manufacturing Systems, 77, 697-707. https://doi.org/https://doi.org/10.1016/j.jmsy.2024.10.017
Wang, L., Deng, T. H., Shen, Z. J. M., Hu, H., & Qi, Y. Z. (2022). Digital twin-driven smart supply chain. Frontiers of Engineering Management, 9(1), 56-70. https://doi.org/10.1007/s42524-021-0186-9
Wang, Z., Feng, W., Ye, J., Yang, J., & Liu, C. (2021). A Study on Intelligent Manufacturing Industrial Internet for Injection Molding Industry Based on Digital Twin. Complexity, 2021, 8838914. https://doi.org/10.1155/2021/8838914
White, G., Zink, A., Codecá, L., & Clarke, S. (2021). A digital twin smart city for citizen feedback. Cities, 110, 103064. https://doi.org/https://doi.org/10.1016/j.cities.2020.103064
Zhang, K., Zhou, H.-Y., Baptista-Hon, D. T., Gao, Y., Liu, X., Oermann, E., Xu, S., Jin, S., Zhang, J., Sun, Z., Yin, Y., Razmi, R. M., Loupy, A., Beck, S., Qu, J., & Wu, J. (2024). Concepts and applications of digital twins in healthcare and medicine. Patterns, 5(8), 101028. https://doi.org/https://doi.org/10.1016/j.patter.2024.101028
Yavari, M., Marvi, M., & Akbari, A. H. (2020). Semi-permutation-based genetic algorithm for order acceptance and scheduling in two-stage assembly problem. Neural Computing and Applications, 32, 2989-3003. https://doi.org/10.1007/s00521-019-04027-w
ارسال نظر در مورد این مقاله
CAPTCHA Image
مدیریت مهندسی و رایانش نرم

مقالات آماده انتشار، پذیرفته شده
انتشار آنلاین از تاریخ 29 مهر 1404

فایل ها

هم رسانی

ارجاع به این مقاله

آمار