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

Abstract

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>Additive manufacturing (AM) has shown its capability in producing complex geometries. Due to the additive nature, the <jats:italic>in situ</jats:italic> 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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Practical implications</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>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.</jats:p> </jats:sec>

Predictive modeling for online in-plane shape deviation inspection and compensation of additive manufacturing

“…; ng, respectively. After aligning the two shape profiles by applying the multiresolution alignment framework (Jin et al, 2020a(Jin et al, , 2020b, the in-plane deviation of layer l can be obtained by:…”

“…The key idea of the multiresolution alignment and inspection is for hierarchically interpreting the shape profile with different levels of detail. In Jin et al (2020aJin et al ( , 2020b, the multiresolution analysis is achieved by wavelet decomposition (Mallat, 1989) to decompose the original shape profiles into different levels of coefficient profiles, which are further applied in a three-step alignment process as illustrated in Figure 5.…”

“…. ; mg. To accommodate multiresolution alignment and inspection process for the shape with concave features, it is suggested to conduct the process with a feature-based inspection scheme as proposed in Jin et al (2020aJin et al ( , 2020b. Then the change point detection, as well as the corresponding predictive modeling and compensation, will be conducted on each feature.…”

“…In this work, the predictive modeling and compensation will be performed on the (x-y plane) shape deviation, which is referred to as the metric for evaluating geometric accuracy. As proposed in one of the recent works (Jin et al, 2020a(Jin et al, , 2020b, the in-plane…”

“…In this paper, the proposed Seq2Seq model with an attention mechanism is implemented to predict the in-plane deviation of the subsequent layers to be printed and the occluded portion of the layer under inspection based on the deviation of previous layers. Based on the predicted deviations, a change point detection method (Jin et al, 2020a(Jin et al, , 2020b) is applied to decide if a compensation strategy should be performed or not. The proposed online compensated strategy is generated by changing the G-code directly to allow the machine to perform the fabrications by following the modified toolpaths.…”