Quality control is the key for the widespread adoption of metal additive manufacturing (AM). However, online quality estimation is challenging because high-frequency stream data derived from in situ metrology have to be processed in a timely manner to figure out the complicated interactions among material, machine, and part. To tackle such issue, this article proposes an intelligent AM metrology (IAMM) architecture to decouple and evaluate quality variations caused by material properties, machine issues, and process parameters when building an AM part. The IAMM architecture can also estimate the online layer-to-layer quality (e.g., roughness and density of an AM part) by applying the robust parameters derived from the uniform design (UD) method via the enhanced automatic virtual metrology technology in a parallel computing environment, as soon as the microfeatures of melt-pools and the macrofeatures of each layer are extracted. In addition, the associated indices and features in the IAMM architecture can also be used to evaluate the defects caused by machine issues in the given process parameters. The results of case studies validate the applicability of the IAMM architecture and show that the proposed estimation models are prospective for future closed-loop control in an AM process. Note to Practitioners —The proposed intelligent AM metrology (IAMM) architecture can be implemented modularly in a metal additive manufacturing (AM) machine that possesses the capability of estimating the coating and printing qualities of the AM process by extracting features from the optical data of the melt pools and chamber layer by layer. The estimated indices and qualities can be derived online using parallel computation to timely diagnose the machine issues and control the process of the next layer. Hence, when an AM machine is equipped with the IAMM architecture, it can efficiently manufacture a 3-D part with reduced defects in real time.

中文翻译：

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从池到层的在线AM质量评估架构

质量控制是金属增材制造（AM）广泛采用的关键。但是，在线质量估算具有挑战性，因为从原位必须及时处理计量，以找出材料，机器和零件之间的复杂相互作用。为解决此类问题，本文提出了一种智能AM计量（IAMM）架构，以在构建AM零件时解耦和评估由材料特性，机器问题和工艺参数引起的质量变化。IAMM架构还可以通过在并行计算中通过增强的自动虚拟计量技术应用从统一设计（UD）方法派生的鲁棒参数，来估计在线层到层质量（例如AM零件的粗糙度和密度）在环境中，只要提取熔池的微观特征和每一层的宏观特征即可。此外，IAMM体系结构中的相关索引和功能还可用于评估给定过程参数中由机器问题引起的缺陷。案例研究的结果验证了IAMM体系结构的适用性，并表明所提出的估计模型对于AM过程中的未来闭环控制具有前景。执业者注意 -可以在金属增材制造（AM）机器中以模块化方式实施建议的智能AM计量（IAMM）体系结构，该机器具有通过从熔池和熔池的光学数据中提取特征来估计AM工艺的涂层和印刷质量的能力。室逐层。估计的指标和质量可以使用并行计算在线得出，以便及时诊断机器问题并控制下一层的过程。因此，当AM机配备IAMM架构时，它可以实时有效地制造缺陷减少的3-D零件。