Additive manufacturing (AM) has shown its capability in producing complex geometries. Due to the additive nature, the in situ layer-wise inspection of geometric accuracy is essential to making AM reach its full potential. This paper aims to propose a novel automated in-plane alignment and error quantification framework to distinguish the fabrication, measurement and alignment errors in AM.,In this work, a multi-resolution framework based on wavelet decomposition is proposed to automatically align two-dimensional point clouds via a polar coordinate representation and then to differentiate errors from different sources based on a randomized complete block design approach. In addition, a two-stage optimization model is proposed to find the best configuration of the multi-resolution framework.,The proposed framework can not only distinguish errors attributed to different sources but also evaluate the performance and consistency of alignment results under different levels of details.,A sample part with different featured layers, including a simple free-form layer, a defective layer and a layer with internal features, is used to illustrate the effectiveness and efficiency of the proposed framework. The proposed alignment method outperforms the widely used iterative closest point algorithm.,This work fills a research gap of state-of-the-art studies by automatically quantifying different types of error inherent in manufacturing, measuring and part alignment.

中文翻译：

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用于增材制造中自动平面对准和误差量化的多分辨率框架

增材制造 (AM) 已显示出其生产复杂几何形状的能力。由于可加性，几何精度的原位分层检查对于使 AM 发挥其全部潜力至关重要。本文旨在提出一种新的自动平面对准和误差量化框架，以区分增材制造中的制造、测量和对准误差。在这项工作中，提出了一种基于小波分解的多分辨率框架来自动对准二维点云通过极坐标表示，然后基于随机完整块设计方法区分来自不同来源的错误。此外，提出了一个两阶段优化模型来寻找多分辨率框架的最佳配置。所提出的框架不仅可以区分归因于不同来源的错误，还可以评估不同细节层次下对齐结果的性能和一致性。,具有不同特征层的样本部分，包括简单的自由形式层、缺陷层和具有内部特征的层，用于说明所提出框架的有效性和效率。所提出的对齐方法优于广泛使用的迭代最近点算法。这项工作通过自动量化制造、测量和零件对齐中固有的不同类型的错误，填补了最新研究的研究空白。包括一个简单的自由形式层、一个缺陷层和一个具有内部特征的层，用于说明所提出框架的有效性和效率。所提出的对齐方法优于广泛使用的迭代最近点算法。这项工作通过自动量化制造、测量和零件对齐中固有的不同类型的错误，填补了最新研究的研究空白。包括一个简单的自由形式层、一个缺陷层和一个具有内部特征的层，用于说明所提出框架的有效性和效率。所提出的对齐方法优于广泛使用的迭代最近点算法。这项工作通过自动量化制造、测量和零件对齐中固有的不同类型的错误，填补了最新研究的研究空白。