A multi-resolution framework for automated in-plane alignment and error quantification in additive manufacturing. Issue 7 (7th July 2020)

• Record Type: Journal Article
• Authors: Jin, Yu; Liao, Haitao; Pierson, Harry A.
• Is Part Of: Rapid prototyping journal. Volume 26:Issue 7(2020)
• Page Start: 1289
• Page End: 1303
• Publication Date: 2020-07-07
• Subjects: Multi-resolution -- Alignment -- Error quantification -- Additive manufacturing; Engineering design -- Periodicals; 620.004205
• DOI: 10.1108/RPJ-07-2019-0183
• Languages: English
• ISSNs: 1355-2546

Abstract

Abstract : 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.