Simulation-assisted multi-process integrated optimization for greentelligent aluminum casting

Abstract

Aluminum casting is one of the most important, yet energy-intensive, lightweight-enabling technologies. To achieve carbon peak and carbon neutrality goals, it is critical to reduce energy consumption without sacrificing productivity. Production optimization of aluminum casting for green and intelligent—i.e., greentelligent—operation is an effective way to accomplish this objective, but this is not sufficient for separately optimizing different processes. Instead, simultaneously optimizing multiple processes is a more advantageous approach, which, however, has not yet been performed. This paper proposes a simulation-assisted approach for the integrated optimization of three primary processes in aluminum casting (i.e., melting, transferring, and holding) to fill this gap. A comprehensive manufacturing cost optimization model that considers the energy, material loss, manpower, and pauses in production for the three processes was first built. Simulation was the key to the formation of the optimization model. Then, a dynamic solution mode with parallel computing and four algorithms (pattern search, genetic algorithm, particle swarm optimization, and simulated annealing) were selected to solve the optimization model. The proposed approach was applied to two die-casting factories to verify its optimization performance. The integrated optimized parameters reduced the comprehensive cost by approximately 8.4% and 16.4% for cases 1 and 2, respectively, for which energy reduction was the primary contributor to the comprehensive cost reduction. The proposed approach was found to be promising for energy conservation and related carbon emissions reduction in the aluminum casting industry.