Physics-Driven Data Collection in 3-D Printing: Traversing the Realm of Social Manufacturing

Abstract

Additive manufacturing (AM), also called 3-D printing, is a supporting technology in social manufacturing that has gained significant attention recently. As the AM industry grows, collecting and analyzing data are essential to ensure product quality, process efficiency, and cost-effectiveness. However, obtaining experimental data is challenging owing to cost and time constraints. Therefore, cost-effective and time-efficient strategies for collecting AM data are urgently required. This study proposes a novel data-collection approach that integrates the concept of finite element analysis (FEA) and physics-informed machine learning (PIML). We begin by discussing the importance of data collection in AM and the associated challenges. We then present various types of data that can be collected in AM, including the 3-D models and end-to-end data. End-to-end data comprise experimental data (i.e., sensors and images) and simulation data. Moreover, we present a case study that demonstrates the generation of simulation data and provides a detailed analysis of warpage. The STereoLithography (STL) file format of the BeltClip object from the Thingiverse possesses slicing through the Ultimaker© Cura software. The resulting G-code file is input to the Digimat-AM platform for virtual simulation of the BeltClip printing process. Digimat-AM, as a FEA simulation tool, then generates observational sample data. These data function as a roadmap for understanding the application of physical information for learning, which constitutes the observational bias aspect of PIML. The observational data obtained from the Digimat-AM is suggested for building a machine-learning model. Finally, we conclude with a discussion of inductive and learning biases in the prediction, control, and optimization aspects of AM.