An impulse response formulation for small-sample learning and control of additive manufacturing quality

Abstract

Machine learning for additive manufacturing (ML4AM) has emerged as a viable strategy in recent years to enhance 3D printing performance. However, the amount of data required for model training and the lack of ability to infer AM process insights can be serious barriers for black-box learning methods. Due to the nature of low-volume fabrication of infinite product variety in AM, ML4AM also faces “small data, big tasks” challenges to learn heterogeneous point cloud data and control the quality of new designs. To address these challenges, this work establishes an impulse response formulation of layer-wise AM processes to relate design inputs with the deformed final products. To enable prescriptive learning from a small sample of printed parts with different 3D shapes, we develop a fabrication-aware input–output representation, where each product is constructed by a large amount of basic shap primitives. The impulse response model depicts how the 2D shape primitives (circular sectors, line segments, and corner segments) in each layer are stacked up to become final 3D shape primitives. A geometric quality of a new design can therefore be predicted through the construction of learned shape primitives. Essentially, the small-sample learning of printed products is transformed into a large-sample learning of printed shape primitives under the impulse response formulation of AM. This fabrication-aware formulation builds the foundation for applying well-established control theory to the intelligent quality control in AM. It not only provides theoretical underpinning and justification of our previous work, but also enable new opportunities in ML4AM. As an example, it leads to transfer function characterization of AM processes to uncover process insights. It also provides block-diagram representation of AM processes to design and optimize the control of AM quality.

Keywords:

• Fabrication-aware representation and learning
• impulse response modeling
• transfer function
• block-diagram
• intelligent quality control

Additional information

Funding

This work is supported by National Science Foundation with Grant# NSF CMMI-1901514. We appreciate all collaborators in our previous publications.

Notes on contributors

Qiang Huang

Dr. Qiang Huang is currently a professor at the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles. His research focuses on AI and Machine Learning for Manufacturing, in particular, Machine Learning for Additive Manufacturing (ML4AM). He was the holder of Gordon S. Marshall Early Career Chair in Engineering at USC from 2012 to 2016. He received IISE Fellow Award, ASME Fellow Award, NSF CAREER award, and 2021 IEEE CASE Best Conference Paper Award, 2013 IEEE Transactions on Automation Science and Engineering Best Paper Award, among others. He holds five patents on ML4AM. He is a Department Editor for IISE Transactions and an Associate Editor for ASME Transactions, Journal of Manufacturing Science and Engineering.