Abstract Current Additive Manufacturing (AM) technologies are typically limited by the minimum feature sizes of the parts they can produce. This issue is addressed by the microscale selective laser sintering system (µ-SLS), which is capable of building parts with single micrometer resolutions. Despite the resolution of the system, the minimum feature sizes producible using the µ-SLS tool are limited by unwanted heat dissipation through the particle bed during the sintering process. To address this unwanted heat flow, a particle scale thermal model is needed to characterize the thermal conductivity of the nanoparticle bed during sintering and facilitate the prediction of heat affected zones (HAZ). This would allow for the optimization of process parameters and a reduction in error for the final part. This paper presents a method for the determination of the effective thermal conductivity of copper nanoparticle beds in a µ-SLS system using finite element simulations performed in ANSYS. A Phase Field Model (PFM) is used to track the geometric evolution of the particle groups within the particle bed during sintering. CAD models are extracted from the PFM output data at various timesteps, and steady state thermal simulations are performed on each particle group. The full simulation developed in this work is scalable to particle groups with variable sizes and geometric arrangements. The particle thermal model results from this work are used to calculate the thermal conductivity of the copper nanoparticles as a function of the density of the particle group.
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Data-Efficient Surrogate Model for Rapid Prediction of Temperature Evolution in a Microscale Selective Laser Sintering System
Current metal additive manufacturing (AM) systems suffer from limitations on the minimum feature sizes they can produce during part formation. The microscale selective laser sintering (μ-SLS) system addresses this drawback by enabling the production of parts with minimum feature resolutions of the order of a single micrometer. However, the production of microscale parts is challenging due to unwanted heat conduction within the nanoparticle powder bed. As a result, finite element (FE) thermal models have been developed to predict the evolution of temperature within the particle bed during laser sintering. These thermal models are not only computationally expensive but also must be integrated into an iterative model-based control framework to optimize the digital mask used to control the distribution of laser power. These limitations necessitate the development of a machine learning (ML) surrogate model to quickly and accurately predict the temperature evolution within the μ-SLS particle bed using minimal training data. The regression model presented in this work uses an “Element-by-Element” approach, where models are trained on individual finite elements to learn the relationship between thermal conditions experienced by each element at a given time-step and the element's temperature at the next time-step. An existing bed-scale FE thermal model of the μ-SLS system is used to generate element-by-element tabular training data for the ML model. A data-efficient artificial neural network (NN) is then trained to predict the temperature evolution of a 2D powder-bed over a 2 s sintering window with high accuracy.
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- Award ID(s):
- 2141044
- PAR ID:
- 10512228
- Publisher / Repository:
- ASME
- Date Published:
- Journal Name:
- Journal of Micro- and Nano-Manufacturing
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2166-0468
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4064106
