Summary of Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization For Time Series Process Optimization, by Vispi Karkaria et al.
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization
by Vispi Karkaria, Anthony Goeckner, Rujing Zha, Jie Chen, Jianjing Zhang, Qi Zhu, Jian Cao, Robert X. Gao, Wei Chen
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed digital twin (DT) framework for real-time predictive control of laser-directed-energy deposition (DED) aims to address challenges in additive manufacturing (AM). By integrating real-time monitoring, physics-based modeling, and control, the framework predicts temperatures in DED parts using Long Short-Term Memory (LSTM)-based machine learning with Bayesian Inference. This enables dynamic optimization of processes, identifying optimal laser power profiles for desired mechanical properties. The framework’s components are outlined, promoting its integration into a comprehensive system for AM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new digital twin framework is being developed to improve additive manufacturing using laser-directed-energy deposition (DED). Right now, DED has some big problems like not always making things with the same good quality. This new framework will use computers to predict what’s happening during the process and make adjustments in real-time to get better results. It uses a special kind of artificial intelligence called machine learning to do this. The goal is to make it easier to create complex shapes and materials with DED. |
Keywords
* Artificial intelligence * Bayesian inference * Lstm * Machine learning * Optimization
