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Meeting Materials Science & Technology 2020
Symposium Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
Presentation Title 3D Printing and Machine Learning
Author(s) Anthony D. Rollett, Srujana Yarasi, Christopher Kantzos, Elizabeth A Holm
On-Site Speaker (Planned) Anthony D. Rollett
Abstract Scope 3D printing, aka additive manufacturing, has grown strongly in recent years. For metals, fusion-based powder bed technologies dominate at present because of their ability to make (near) net shape, complex parts. Machine learning (ML) is having a pervasive impact on the field because of the complexity of the processes that are used and the largely empirical development that has occurred. Applications will be given of using ML to classify powders, to identify spreading defects, to relate powder morphologies to their flow characteristics, to classify pore types, to recognize off-normal microstructures, to identify defect formation from acoustic signals, to predict stress hot spots on rough surfaces and to find relationships between surface roughness and fatigue life. Although modern ML methods are making vital contributions particularly to feature extraction from images, traditional data analytics are also useful and it is essential for the materials scientist to apply the full spectrum of methods.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

3D Printing and Machine Learning
Cycle Life Prediction of Lithium Ion Batteries Based on Data Driven Methods
Expert-guided Learning for Data-constrained Materials Science Problems
Fast and Generalizable Detailed Router Using Attention-based Reinforcement Learning
Introductory Comments: Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
Neural Network Potential for Lattice Dynamics Calculations and Thermal Conductivity Prediction
Parametric Analysis to Quantify Process Input Influence on the Printed Densities of Binder Jetted Alumina Ceramics
SimuLearn: Machine Learning-empowered Fast and Accurate Simulator to Support 4D Printing Design
Uncertainty Quantification and Active Learning of Neural Network Models for Predicting ZrO2 Crystal Energy

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