An algorithm for physics informed scan path optimization in additive manufacturing

B. Stump

doi:10.1016/j.commatsci.2022.111566

An algorithm for physics informed scan path optimization in additive manufacturing

B. Stump

Computational Coupled Physics

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

Site specific microstructure control is a critical research area within the field of additive manufacturing due to its potential to revolutionize part performance. One way to achieve site specific microstructure control is through control of the solidification conditions via the construction of intricate scan paths; however, the search space for such a problem is large. Previous attempts only considered the solidification conditions at the top surface while also requiring either lots of manual-fine tuning or large amounts of computational resources. This paper introduces a general method for scan path optimization which considers the solidification conditions in the bulk of the material without an increase in computational expense. This method consists of three core components: 1. A heat transfer model for simulating the temperature field at a given time. 2. A surrogate model which takes scan pattern information and temperature data and predicts the solidification conditions of the bulk as well as the meltpool depths for a spot melt. 3. A decision algorithm to decide which spot melt should be printed next based on the outputs of the surrogate model.Each of these components can be changed without changing the overall method. Within this paper, this method is applied in the creation of an algorithm containing a semi-analytic heat transfer model to simulate the temperature field, a fully convolutional neural network (FCNN) as the surrogate model, and a greedy decision algorithm. The resulting algorithm produced complex scan patterns which gave strong results for simulated microstructure control.

Original language	English
Article number	111566
Journal	Computational Materials Science
Volume	212
DOIs	https://doi.org/10.1016/j.commatsci.2022.111566
State	Published - Sep 2022

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. Research was co-sponsored the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office and the Office of Electricity Delivery and Energy Reliability (OE) – Transformer Resilience and Advanced Components (TRAC) Program, The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( ). This research was performed at the Department of Energy's Manufacturing Demonstration Facility located at Oak Ridge National Laboratory. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. Research was co-sponsored the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office and the Office of Electricity Delivery and Energy Reliability (OE) – Transformer Resilience and Advanced Components (TRAC) Program, The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (<http://energy.gov/downloads/doe-public-access-plan > ). The author would like to thank Alex Plotkowski and Gerald Knapp and for reviewing this work and providing valuable feedback. The raw/processed data required to reproduce these findings cannot be shared at this time due to technical or time limitations.

Funders	Funder number
Alex Plotkowski and Gerald Knapp
DOE Public Access Plan
United States Government
U.S. Department of Energy
Advanced Manufacturing Office
Office of Electricity Delivery and Energy Reliability
Office of Energy Efficiency and Renewable Energy
Oak Ridge National Laboratory	DE-AC05-00OR22725

Keywords

Additive manufacturing
Machine learning
Microstructure control
Numerical modeling
Scan path optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.commatsci.2022.111566

Cite this

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abstract = "Site specific microstructure control is a critical research area within the field of additive manufacturing due to its potential to revolutionize part performance. One way to achieve site specific microstructure control is through control of the solidification conditions via the construction of intricate scan paths; however, the search space for such a problem is large. Previous attempts only considered the solidification conditions at the top surface while also requiring either lots of manual-fine tuning or large amounts of computational resources. This paper introduces a general method for scan path optimization which considers the solidification conditions in the bulk of the material without an increase in computational expense. This method consists of three core components: 1. A heat transfer model for simulating the temperature field at a given time. 2. A surrogate model which takes scan pattern information and temperature data and predicts the solidification conditions of the bulk as well as the meltpool depths for a spot melt. 3. A decision algorithm to decide which spot melt should be printed next based on the outputs of the surrogate model.Each of these components can be changed without changing the overall method. Within this paper, this method is applied in the creation of an algorithm containing a semi-analytic heat transfer model to simulate the temperature field, a fully convolutional neural network (FCNN) as the surrogate model, and a greedy decision algorithm. The resulting algorithm produced complex scan patterns which gave strong results for simulated microstructure control.",

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An algorithm for physics informed scan path optimization in additive manufacturing

Abstract

Funding

Keywords

UN SDGs

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