Deep Convolutional Neural Networks as a Rapid Screening Tool for Complex Additively Manufactured Structures
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Materials, Physical, and Chemical Science Center
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Additively manufactured metamaterials such as lattices offer unique physical properties such as high specific strengths and stiffnesses. However, additively manufactured parts, including lattices, exhibit a higher variability in their mechanical properties than wrought materials, placing more stringent demands on inspection, part quality verification, and product qualification. Previous research on anomaly detection has primarily focused on using in-situ monitoring of the additive manufacturing process or post-process (ex-situ) x-ray computed tomography. In this work, we show that convolutional neural networks (CNN), a machine learning algorithm, can directly predict the energy required to compressively deform gyroid and octet truss metamaterials using only optical images. Using the tiled nature of engineered lattices, the relatively small data set (43 to 48 lattices) can be augmented by systematically subdividing the original image into many smaller sub-images. Furthermore, during testing of the CNN, the prediction from these sub-images can be combined using an ensemble-like technique to predict the deformation work of the entire lattice. This approach provides a fast and inexpensive screening tool for predicting properties of 3D printed lattices. Importantly, this artificial intelligence strategy goes beyond ‘inspection’, since it accurately estimates product performance metrics, not just the existence of defects.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1639094
- Alternate ID(s):
- OSTI ID: 1809810
- Report Number(s):
- SAND--2020-7001J; 687224
- Journal Information:
- Additive Manufacturing, Journal Name: Additive Manufacturing Vol. 35; ISSN 2214-8604
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English