Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances
Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances
复制标题
增材制造工艺改进的不确定性量化：最新进展
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Zhen Hu
中科院分区：
文献类型：
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作者：
S. Mahadevan;Paromita Nath;Zhen Hu
本文回顾了将不确定性量化 (UQ) 方法应用于增材制造 (AM) 的最新技术。基于物理和数据驱动的模型越来越多地得到开发和完善，以支持增材制造的过程优化和控制目标，特别是最大限度地提高增材制造产品的质量并最大限度地减少变异性。然而，在使用这些模型进行决策之前，需要回答的一个基本问题是模型在多大程度上可以被信任，并考虑影响其预测的各种不确定性来源。由于增材制造过程中存在复杂的多物理场、多尺度现象，增材制造中的不确定性量化（UQ）并非微不足道。本文回顾了昆士兰大学方法学的文献，重点关注模型不确定性，讨论了相应的校准、验证和确认活动，并研究了 AM 文献中报告的它们的应用。将当前的昆士兰大学方法论扩展到增材制造需要解决多物理场、多尺度相互作用、数据驱动模型的增加、制造成本高和测量的复杂性等问题。讨论了为实施增材制造验证、校准和确认而需要开展的活动。还回顾了利用昆士兰大学活动结果进行增材制造工艺优化和控制（从而支持质量最大化和变异性最小化）的文献。概述了昆士兰大学和增材制造决策方面的未来研究需求。
This paper reviews the state of the art in applying uncertainty quantification (UQ) methods to additive manufacturing (AM). Physics-based as well as data-driven models are increasingly being developed and refined in order to support process optimization and control objectives in AM, in particular to maximize the quality and minimize the variability of the AM product. However, before using these models for decision-making, a fundamental question that needs to be answered is to what degree the models can be trusted, and consider the various uncertainty sources that affect their prediction. Uncertainty quantification (UQ) in AM is not trivial because of the complex multi-physics, multi-scale phenomena in the AM process. This article reviews the literature on UQ methodologies focusing on model uncertainty, discusses the corresponding activities of calibration, verification and validation, and examines their applications reported in the AM literature. The extension of current UQ methodologies to additive manufacturing needs to address multi-physics, multi-scale interactions, increasing presence of data-driven models, high cost of manufacturing, and complexity of measurements. The activities that need to be undertaken in order to implement verification, calibration, and validation for AM are discussed. Literature on using the results of UQ activities towards AM process optimization and control (thus supporting maximization of quality and minimization of variability) is also reviewed. Future research needs both in terms of UQ and decision-making in AM are outlined.