A tensor‐based hierarchical process monitoring approach for anomaly detection in additive manufacturing

Abstract

Additive manufacturing (AM) is a technology that enables the creation of complex shapes with advanced structural and functional properties. It has transformed the traditional manufacturing operations into a more flexible and efficient process, reshaping the whole value chain and allowing new levels of product customization. AM is a layer‐by‐layer manufacturing process, in which materials are deposited in each layer to create the object of interest. Due to the layer‐wise nature of the process, anomalies and defects might occur within each layer, across several layers or throughout the whole sample. An accurate and responsive detection strategy that enables the detection of various types of anomalies is essential for ensuring the quality and integrity of the manufactured product. In this paper, a hierarchical in situ process monitoring approach, namely, a three level monitoring strategy, is proposed to detect local, layer‐wise, and sample‐wise anomalies using thermal videos acquired during the manufacturing process. The proposed approach integrates hierarchical low‐rank tensor decomposition methods with statistical monitoring techniques to effectively detect anomalies at different levels, namely, the within‐layer level, the layer level, and the sample level. Simulations are used to evaluate the performance of the method and compare with existing benchmarks. The proposed approach is also applied to thermal videos acquired during the laser powder bed fusion (L‐PBF) process to illustrate its effectiveness in practice.

Multi‐objective Bayesian modeling and optimization of 3D printing process via experimental data‐driven method

“…Therefore, Cho et al 37 highlighted the importance of performing batch computations in practical problems and proposed the parallel 𝐸𝐼 𝐻 (𝑞𝐸𝐼 𝐻 ) algorithm to improve optimization efficiency. When obtaining 𝑓(𝐱) = (𝑓 (1) (𝐱), … , 𝑓 (𝑚) 15) and (16).…”

“…One approach involves obtaining reliable data and optimizing process parameters through high-fidelity simulations, 13 but it is time-consuming. Another approach is to optimize process parameters by conducting experiments and acquiring on-site data, [14][15][16] but the cost of on-site experiments is often prohibitively high. Therefore, there is an urgent need for an efficient artificial intelligence method for data analysis to find reasonable process parameters.…”

The ENBIS‐21 quality and reliability engineering international special issue

“…The issue is opened by the contribution of Yang et al 1 . in the Statistical Process Monitoring (SPM) of data in the form of tensors.…”