Data-Driven and Physics-Assisted Machine Learning Approach for Warpage Classification and Process Parameter Optimization in a 3-D-Printed BeltClip

Download PDF
Open Webpage

Tariku Sinshaw Tamir, Xijin Hua, Jingchao Jiang, Jiewu Leng, Gang Xiong, Zhen Shen, Qiang Liu

Published: 2025, Last Modified: 08 Jan 2026IEEE Trans. Comput. Soc. Syst. 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: 3-D printing, or additive manufacturing (AM), leverages 3-D computer-aided design models and numerical control to produce objects layer-by-layer, playing a key role in Industry 4.0 and Industry 5.0. Despite its potential to revolutionize manufacturing by creating complex structures more efficiently and cost-effectively, 3-D printing still faces quality issues due to a lack of sufficient data, resulting in improper process parameter settings and poor analyzability. This work introduces a data-driven and physics-assisted machine learning (DP-ML) approach for a 3-D-printed BeltClip object, integrating finite element analysis (FEA) and physics-informed machine learning (PIML). The proposed DP-ML framework provides a cost-effective and time-efficient data collection method using Digimat-AM and a warpage classification algorithm. The data collection begins with obtaining the STereoLithography (STL) file of the BeltClip object from Thingiverse and slicing it in Ultimaker©  Cura, considering process parameters such as infill amount, toolpath pattern, layer height, print speed, and extrusion temperature. The resulting G-code file is then input into Digimat-AM for further parameter setting and analysis. In Digimat-AM, glass fiber-filled and unfilled material types are set, undergoing the virtual 3-D printing process, followed by a warpage analysis of the printed BeltClip. The collected 3-D printing data is used to build ML models—deep neural network (DNN), decision tree (DT), support vector machine (SVM), logistic regression (LR), and random forest. The DNN contains three architectures—DNN-1, DNN-2, and DNN-3. Based on the metrics of precision, recall, F1-score, and accuracy, DNN-3 outperforms the others and is chosen for the warpage classification algorithm. The presented DP-ML approach is compared with the state-of-the-art methods and shows a promising capability to predicting warpage, optimizing process parameters, and improving the overall quality and efficiency of a 3-D-printed BeltClip.