Abstract The temperature history of an additively manufactured part plays a critical role in determining process–structure–property relationships in fusion-based additive manufacturing (AM) processes. Therefore, fast thermal simulation methods are needed for a variety of AM tasks, from temperature history prediction for part design and process planning to in situ temperature monitoring and control during manufacturing. However, conventional numerical simulation methods fall short in satisfying the strict requirements of time efficiency in these applications due to the large space and time scales of the required multiscale simulation. While data-driven surrogate models are of interest for their rapid computation capabilities, the performance of these models relies on the size and quality of the training data, which is often prohibitively expensive to create. Physics-informed neural networks (PINNs) mitigate the need for large datasets by imposing physical principles during the training process. This work investigates the use of a PINN to predict the time-varying temperature distribution in a part during manufacturing with laser powder bed fusion (L-PBF). Notably, the use of the PINN in this study enables the model to be trained solely on randomly synthesized data. These training data are both inexpensive to obtain, and the presence of stochasticity in the dataset improves the generalizability of the trained model. Results show that the PINN model achieves higher accuracy than a comparable artificial neural network trained on labeled data. Further, the PINN model trained in this work maintains high accuracy in predicting temperature for laser path scanning strategies unseen in the training data.
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This content will become publicly available on May 1, 2024
A Bayesian Approach to Modeling Unit Manufacturing Process Environmental Impacts using Limited Data with Case Studies on Laser Powder Bed Fusion Cumulative Energy Demand
Informed decision-making for sustainable manufacturing requires accurate manufacturing process environmental impact models with uncertainty
quantification (UQ). For emerging manufacturing technologies, there is often insufficient process data available to derive accurate data-driven
models. This paper explores an alternative mechanistic modeling approach using easy-to-access data from a given machine to perform Bayesian
inference and reduce the uncertainty of model parameters. First, we derive mechanistic models of the cumulative energy demand (CED) for
making aluminum (AlSi10) and nylon (PA12) parts using laser powder bed fusion (L-PBF). Initial parametric uncertainty is assigned to the model
inputs informed by literature reviews and interviews with industry experts. Second, we identify the most critical sources of uncertainty using
variance-based global sensitivity analyses; therefore, reducing the dimension of the problem. For metal and polymer L-PBF, critical uncertainty
is related to the adiabatic efficiency of the process (a measure of the efficiency with which the laser energy is used to fuse the powder) and the
recoating time per layer between laser scans. Data pertinent to both of these parameters include the part geometry (height and volume) and total
build time. Between three and eight data points on part geometry and build time were collected on two different L-PBF machines and Bayesian
inference was performed to reduce the uncertainty of the adiabatic efficiency and recoating time per layer on each machine. This approach was
validated by subsequently taking direct parameter measurements on these machines during operation. The delivered electricity uncertainty is
reduced by 40-70% after performing inference, highlighting the potential to construct accurate energy and environmental impact models of
manufacturing processes using small easy-to-access datasets without interfering with the operations of the manufacturing facility.
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- Award ID(s):
- 2040013
- NSF-PAR ID:
- 10492267
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Procedia CIRP
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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