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Optimizing process parameters in cold spray additive manufacturing: A data-driven, simulation-based multi-objective approach
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DOI：10.1016/j.simpat.2025.103235.png)
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Abstract
En 中文
Cold spray additive manufacturing (CSAM) is an emerging solid-state deposition technique that utilizes high-velocity gas to propel powdered materials onto a substrate. Analysis of objective functions for process parameter optimization in CSAM requires data that is usually obtained from costly experiments or numerical simulations. Integrating simulations or experiments directly into conventional optimization algorithms can lead to significantly high computational costs. Additionally, these optimization problems typically involve multiple conflicting objectives that should be taken into account simultaneously. In this work, we develop a data-driven, simulation-based multi-objective optimization framework (SMOF) to optimize CSAM process parameters online. The smoothed particle hydrodynamics (SPH) method is used to perform CSAM simulations. A new optimal grid mutation-based infill criterion (OIC) is proposed to enhance the surrogate-assisted search in SMOF. Subsequently, numerical simulations are replaced by an ensemble of surrogates with high prediction robustness. We assess the effectiveness of the proposed OIC on two benchmark test problems and further optimize multiple powder impact problems. The optimization results demonstrate that the present SMOF can identify desired process parameter combinations for the CSAM process. Based on the proposed SMOF, refined multi-objective process parameter windows are established for the first time to analyze the evolution of CSAM process parameters.
Keywords:
Cold spray additive manufacturing
Meshfree simulation
Surrogate model
Multi-objective optimization
Process parameter
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