Optimization process of designing and manufacturing of cylinder block system in cast iron industry using metaheuristic computing technique

• Published in: International journal on interactive design and manufacturing Vol. 19; no. 3; pp. 2203 - 2222
• Main Authors: Urvashi, Bansal, Shikha
• Format: Journal Article
• Language: English
• Published: Paris Springer Paris 01.03.2025; Springer Nature B.V
• Subjects: Automotive parts; CAE) and Design; Cast iron; Coal-fired power plants; Computer-Aided Engineering (CAD; Death; Design; Design optimization; Efficiency; Electronics and Microelectronics; Emission standards; Engine blocks; Engineering; Engineering Design; Failure; Genetic algorithms; Heuristic methods; Industrial Design; Instrumentation; Literature reviews; Manufacturing; Markov processes; Mathematical models; Mechanical Engineering; Neural networks; Nuclear power plants; Optimization techniques; Original Article; Parameters; Particle swarm optimization; Performance enhancement; Preventive maintenance; Productivity; Subsystems; Design; Markov approach; Genetic algorithm; Steady-state availability; Particle swarm optimization algorithm; Maintenance; Performance
• ISSN: 1955-2513; 1955-2505
• DOI: 10.1007/s12008-024-02197-z

A cylinder block system is a complicated component within a cast iron manufacturing structure, involving several interaction subsystems throughout the production process. Given the increasing need for efficient automotive components, optimizing the design and manufacturing processes of this system to enhance its performance and production efficiency is imperative. The primary aim of this work is to design and enhance the performance of the cylinder block system utilizing a hybrid metaheuristic approach, specifically the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The algorithms are executed in MATLAB-R2022 software for the computation of numerical results for the performance of the system. The system’s mathematical model is constructed using the Markov birth-death process, and the differential associated with that model is generated from the system’s state transition diagram. The Markov birth-death technique examines the system’s performance and determines which subsystem is more critical and needs greater maintenance. The system’s performance is enhanced through the utilization of the PSO and GA algorithms. The system’s optimism parameters value is determined using these algorithms, varying both population size and generation size; these optimal parameters help the casting production industry to enhance system performance.