{"title":"Adaptable multi-objective optimization framework: application to metal additive manufacturing","authors":"","doi":"10.1007/s00170-024-13489-9","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>This work presents a novel adaptable framework for multi-objective optimization (MOO) in metal additive manufacturing (AM). The framework offers significant advantages by departing from the traditional design of experiments (DoE) and embracing surrogate-based optimization techniques for enhanced efficiency. It accommodates a wide range of process variables such as laser power, scan speed, hatch distance, and optimization objectives like porosity and surface roughness (SR), leveraging Bayesian optimization for continuous improvement. High-fidelity surrogate models are ensured through the implementation of space-filling design and Gaussian process regression. Sensitivity analysis (SA) is employed to quantify the influence of input parameters, while an evolutionary algorithm drives the MOO process. The efficacy of the framework is demonstrated by applying it to optimize SR and porosity in a case study, achieving a significant reduction in SR and porosity levels using data from existing literature. The Gaussian process model achieves a commendable cross-validation <em>R</em>2 score of 0.79, indicating a strong correlation between the predicted and actual values with minimal relative mean errors. Furthermore, the SA highlights the dominant role of hatch spacing in SR prediction and the balanced contribution of laser speed and power on porosity control. This adaptable framework offers significant potential to surpass existing optimization approaches by enabling a more comprehensive optimization, contributing to notable advancements in AM technology.</p>","PeriodicalId":50345,"journal":{"name":"International Journal of Advanced Manufacturing Technology","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Advanced Manufacturing Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00170-024-13489-9","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
This work presents a novel adaptable framework for multi-objective optimization (MOO) in metal additive manufacturing (AM). The framework offers significant advantages by departing from the traditional design of experiments (DoE) and embracing surrogate-based optimization techniques for enhanced efficiency. It accommodates a wide range of process variables such as laser power, scan speed, hatch distance, and optimization objectives like porosity and surface roughness (SR), leveraging Bayesian optimization for continuous improvement. High-fidelity surrogate models are ensured through the implementation of space-filling design and Gaussian process regression. Sensitivity analysis (SA) is employed to quantify the influence of input parameters, while an evolutionary algorithm drives the MOO process. The efficacy of the framework is demonstrated by applying it to optimize SR and porosity in a case study, achieving a significant reduction in SR and porosity levels using data from existing literature. The Gaussian process model achieves a commendable cross-validation R2 score of 0.79, indicating a strong correlation between the predicted and actual values with minimal relative mean errors. Furthermore, the SA highlights the dominant role of hatch spacing in SR prediction and the balanced contribution of laser speed and power on porosity control. This adaptable framework offers significant potential to surpass existing optimization approaches by enabling a more comprehensive optimization, contributing to notable advancements in AM technology.
摘要 这项工作为金属增材制造（AM）中的多目标优化（MOO）提出了一个新颖的适应性框架。该框架摒弃了传统的实验设计（DoE），采用了基于代理的优化技术以提高效率，因而具有显著优势。它能适应激光功率、扫描速度、舱口距离等多种工艺变量，以及孔隙率和表面粗糙度（SR）等优化目标，并利用贝叶斯优化技术实现持续改进。通过实施空间填充设计和高斯过程回归，确保了高保真代用模型。灵敏度分析（SA）用于量化输入参数的影响，而进化算法则驱动 MOO 过程。通过在案例研究中应用该框架优化 SR 和孔隙度，利用现有文献中的数据显著降低了 SR 和孔隙度水平，从而证明了该框架的功效。高斯过程模型的交叉验证 R2 值为 0.79，值得称赞，这表明预测值和实际值之间具有很强的相关性，相对平均误差极小。此外，SA 突出显示了舱口间距在 SR 预测中的主导作用，以及激光速度和功率对孔隙率控制的均衡贡献。这种适应性强的框架通过实现更全面的优化，为超越现有优化方法提供了巨大潜力，从而推动了 AM 技术的显著进步。
期刊介绍：
The International Journal of Advanced Manufacturing Technology bridges the gap between pure research journals and the more practical publications on advanced manufacturing and systems. It therefore provides an outstanding forum for papers covering applications-based research topics relevant to manufacturing processes, machines and process integration.
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