Optimizing Manufacturing Processes with Programming-Driven Simulation and Control


Authors : Farag M. Meragh Hossen; Ali F. Ali Fadiel; Magdi E.M. EL-Garosh

Volume/Issue : Volume 9 - 2024, Issue 1 - January

Google Scholar : http://tinyurl.com/yk9vbk45

Scribd : http://tinyurl.com/mrxfnv35

DOI : https://doi.org/10.5281/zenodo.10613238

Abstract : In the ever-evolving landscape of manufacturing, the need for efficient and adaptive processes is paramount. This research delves into the realm of manufacturing process optimization, employing a novel approach that integrates programming-driven simulation and control strategies. The study begins with exploring the current state of manufacturing optimization and identifying gaps and challenges. A comprehensive methodology outlines the simulation framework, programming techniques, and control strategies implemented in the manufacturing system under investigation. The mathematical models used for simulation are detailed, accompanied by a discussion of assumptions and simplifications. The simulation results are then presented, showcasing the proposed approach's performance compared to baseline methods. The paper further describes the implementation of control strategies, providing insights into the coding structure, design considerations, and the seamless integration with the simulation framework. The results obtained from the control measures are analyzed, offering a comprehensive understanding of their impact on the manufacturing process. The discussion section interprets the findings, highlighting their implications for the field of manufacturing optimization. Comparative analyses with existing studies underscore the uniqueness and effectiveness of the proposed approach. Limitations and challenges encountered during the research are transparently discussed, paving the way for future investigations. The conclusion succinctly summarizes the key contributions of this research and outlines recommendations for further exploration in this interdisciplinary domain. This paper advances the understanding of manufacturing process optimization. It provides a practical framework integrating programming-driven simulation and control, offering a promising avenue for enhancing efficiency and adaptability in contemporary manufacturing environments.

Keywords : Machine Learning, Manufacturing Optimization. Mathematical Modeling, Control Strategies, Manufacturing Simulation, Process Automation, Computational Optimization, Industry 4.0, Smart Manufacturing.

In the ever-evolving landscape of manufacturing, the need for efficient and adaptive processes is paramount. This research delves into the realm of manufacturing process optimization, employing a novel approach that integrates programming-driven simulation and control strategies. The study begins with exploring the current state of manufacturing optimization and identifying gaps and challenges. A comprehensive methodology outlines the simulation framework, programming techniques, and control strategies implemented in the manufacturing system under investigation. The mathematical models used for simulation are detailed, accompanied by a discussion of assumptions and simplifications. The simulation results are then presented, showcasing the proposed approach's performance compared to baseline methods. The paper further describes the implementation of control strategies, providing insights into the coding structure, design considerations, and the seamless integration with the simulation framework. The results obtained from the control measures are analyzed, offering a comprehensive understanding of their impact on the manufacturing process. The discussion section interprets the findings, highlighting their implications for the field of manufacturing optimization. Comparative analyses with existing studies underscore the uniqueness and effectiveness of the proposed approach. Limitations and challenges encountered during the research are transparently discussed, paving the way for future investigations. The conclusion succinctly summarizes the key contributions of this research and outlines recommendations for further exploration in this interdisciplinary domain. This paper advances the understanding of manufacturing process optimization. It provides a practical framework integrating programming-driven simulation and control, offering a promising avenue for enhancing efficiency and adaptability in contemporary manufacturing environments.

Keywords : Machine Learning, Manufacturing Optimization. Mathematical Modeling, Control Strategies, Manufacturing Simulation, Process Automation, Computational Optimization, Industry 4.0, Smart Manufacturing.

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