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

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.