A multi-resolution framework for automated in-plane alignment and error quantification in additive manufacturing

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Practical implications: 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. Originality/value: 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.