Development of optimization formula for Neural Network-Based Automatic Control System in manufacturing industry
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Abstract
The manufacturing industry faces great challenges in improving the efficiency and effectiveness of automated control systems. This research aims to develop a neural network-based optimization formula that can overcome the limitations of conventional control methods. The method used in this research is gradient descent optimization applied to an objective function with certain constraints. The results show that this optimization method is effective in achieving the optimal value of 𝑥 that is close to the target with high precision, while the control variable 𝑢 remains stable throughout the iterations. The implication of this research is the improvement of the reliability and stability of automatic control systems in the manufacturing industry, which has the potential to significantly increase productivity and operational efficiency. Thus, this research makes an important contribution to the field of control system optimization and opens up opportunities for further development with the integration of more sophisticated optimization techniques.
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How to Cite
Handayani, Y. (2024). Development of optimization formula for Neural Network-Based Automatic Control System in manufacturing industry. Journal of Intelligent Decision Support System (IDSS), 7(4), 302-308. https://doi.org/10.35335/idss.v7i4.271
References
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Spielberg, N. A., Brown, M., Kapania, N. R., Kegelman, J. C., & Gerdes, J. C. (2019). Neural network vehicle models for high-performance automated driving. Science Robotics, 4(28), eaaw1975.
Tortorella, G. L., Giglio, R., & Van Dun, D. H. (2019). Industry 4.0 adoption as a moderator of the impact of lean production practices on operational performance improvement. International Journal of Operations & Production Management, 39(6/7/8), 860–886.
Wang, Y., Chenxie, Y., Tan, J., Wang, C., Wang, Y., & Zhang, Y. (2015). Fuzzy radial basis function neural network PID control system for a quadrotor UAV based on particle swarm optimization. 2015 IEEE International Conference on Information and Automation, 2580–2585.
Alcácer, V., & Cruz-Machado, V. (2019). Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems. In Engineering Science and Technology, an International Journal (Vol. 22, Issue 3, pp. 899–919). Elsevier. https://doi.org/10.1016/j.jestch.2019.01.006
Bag, S., Gupta, S., & Kumar, S. (2021). Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development. International Journal of Production Economics, 231, 107844.
Berry, L. L., & Parasuraman, A. (2004). Marketing services: Competing through quality. Simon and Schuster.
Dastres, R., & Soori, M. (2021). Artificial neural network systems. International Journal of Imaging and Robotics (IJIR), 21(2), 13–25.
ElMaraghy, H., Monostori, L., Schuh, G., & ElMaraghy, W. (2021). Evolution and future of manufacturing systems. CIRP Annals, 70(2), 635–658.
Fettermann, D. C., Cavalcante, C. G. S., Almeida, T. D. de, & Tortorella, G. L. (2018). How does Industry 4.0 contribute to operations management? Journal of Industrial and Production Engineering, 35(4), 255–268.
Ivanov, D., Das, A., & Choi, T.-M. (2018). New flexibility drivers for manufacturing, supply chain and service operations. In International Journal of Production Research (Vol. 56, Issue 10, pp. 3359–3368). Taylor & Francis.
Kang, Z., Catal, C., & Tekinerdogan, B. (2020). Machine learning applications in production lines: A systematic literature review. Computers & Industrial Engineering, 149, 106773.
Liu, J., Gaynor, A. T., Chen, S., Kang, Z., Suresh, K., Takezawa, A., Li, L., Kato, J., Tang, J., & Wang, C. C. L. (2018). Current and future trends in topology optimization for additive manufacturing. Structural and Multidisciplinary Optimization, 57(6), 2457–2483.
Lu, Y., Xu, X., & Wang, L. (2020). Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems, 56, 312–325. https://doi.org/10.1016/j.jmsy.2020.06.010
Manurung, J., Mawengkang, H., & Zamzami, E. (2017). Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment. Journal of Physics: Conference Series, 930(1), 12026. https://doi.org/10.1088/1742-6596/930/1/012026
Merabet, G. H., Essaaidi, M., Haddou, M. Ben, Qolomany, B., Qadir, J., Anan, M., Al-Fuqaha, A., Abid, M. R., & Benhaddou, D. (2021). Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques. Renewable and Sustainable Energy Reviews, 144, 110969.
Müller, J. M., Kiel, D., & Voigt, K.-I. (2018). What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability, 10(1), 247.
Namugenyi, C., Nimmagadda, S. L., & Reiners, T. (2019). Design of a SWOT analysis model and its evaluation in diverse digital business ecosystem contexts. Procedia Computer Science, 159, 1145–1154.
Nimmagadda, V. S. P. (2021). Artificial Intelligence for Real-Time Logistics and Transportation Optimization in Retail Supply Chains: Techniques, Models, and Applications. Journal of Machine Learning for Healthcare Decision Support, 1(1), 88–126.
Parasuraman, R., Mouloua, M., Molloy, R., & Hilburn, B. (2018). Monitoring of automated systems. In Automation and human performance (pp. 91–115). CRC Press.
Pasupuleti, V., Thuraka, B., Kodete, C. S., & Malisetty, S. (2024). Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management. Logistics, 8(3), 73.
Pereira, T., Kennedy, J. V, & Potgieter, J. (2019). A comparison of traditional manufacturing vs additive manufacturing, the best method for the job. Procedia Manufacturing, 30, 11–18.
Qi, X., Chen, G., Li, Y., Cheng, X., & Li, C. (2019). Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives. Engineering, 5(4), 721–729.
Rane, J., Mallick, S. K., Kaya, O., & Rane, N. L. (2024). Automated Machine Learning (AutoML) in industry 4.0, 5.0, and society 5.0: Applications, opportunities, challenges, and future directions. Future Research Opportunities for Artificial Intelligence in Industry 4.0 And, 5, 2.
Ruano, A. (1992). Applications of neural networks to control systems. Applications of Neural Networks to Control Systems, 1–202.
Sjödin, D. R., Parida, V., Leksell, M., & Petrovic, A. (2018). Smart Factory Implementation and Process Innovation: A Preliminary Maturity Model for Leveraging Digitalization in Manufacturing Moving to smart factories presents specific challenges that can be addressed through a structured approach focused on people, processes, and technologies. Research-Technology Management, 61(5), 22–31.
Spielberg, N. A., Brown, M., Kapania, N. R., Kegelman, J. C., & Gerdes, J. C. (2019). Neural network vehicle models for high-performance automated driving. Science Robotics, 4(28), eaaw1975.
Tortorella, G. L., Giglio, R., & Van Dun, D. H. (2019). Industry 4.0 adoption as a moderator of the impact of lean production practices on operational performance improvement. International Journal of Operations & Production Management, 39(6/7/8), 860–886.
Wang, Y., Chenxie, Y., Tan, J., Wang, C., Wang, Y., & Zhang, Y. (2015). Fuzzy radial basis function neural network PID control system for a quadrotor UAV based on particle swarm optimization. 2015 IEEE International Conference on Information and Automation, 2580–2585.
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