Abstract
The large-scale additive manufacturing system using thermoplastic materials has been widely applied in the aerospace and automotive industries. The improper surface temperature of layers can cause quality issues. Therefore, an accurate prediction of layer deposition time, or layer time, can significantly improve product quality and production efficiency. Due to varying temperature requirements for different experimental designs and differences in temperature cooling curves among various geometries, layer times need to be estimated accounting for multiple conditions such as geometry, material, and ambient temperatures. However, since conducting repetitive experiments using real production process data is expensive and inflexible, temperature data generated from a physics-based FEA model, which can simulate the printing process, needs to be used to find the optimal layer time before printing (offline design). The optimal layer time provided by this method will be highly consistent on each layer for a homogeneous geometry due to the ideal temperature ignoring uncertain environmental influences and sacrificing fidelity. Therefore, during printing based on the optimal layer time provided by offline control, it is necessary to use real-time information captured by the IR camera to further optimize layer time and make corresponding adjustments. We propose this approach as an integrated optimization framework, which is further verified using a real case study.
| Original language | English |
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| Title of host publication | 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023 |
| Publisher | IEEE Computer Society |
| ISBN (Electronic) | 9798350320695 |
| DOIs | |
| State | Published - 2023 |
| Event | 19th IEEE International Conference on Automation Science and Engineering, CASE 2023 - Auckland, New Zealand Duration: Aug 26 2023 → Aug 30 2023 |
Publication series
| Name | IEEE International Conference on Automation Science and Engineering |
|---|---|
| Volume | 2023-August |
| ISSN (Print) | 2161-8070 |
| ISSN (Electronic) | 2161-8089 |
Conference
| Conference | 19th IEEE International Conference on Automation Science and Engineering, CASE 2023 |
|---|---|
| Country/Territory | New Zealand |
| City | Auckland |
| Period | 08/26/23 → 08/30/23 |
Funding
Lu Liu and Feng Ju are with School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, 85281, USA [email protected], [email protected] Eonyeon Jo is with The Bredesen Center, University of Tennessee, Knoxville, TN, 37996, USA. [email protected] Uday Vaidya is with Mechanical, Aerospace and Biomedical Engineering, University of Tennessee Knoxville, 37996 USA. [email protected] Seokpum Kim is with Manufacturing Science Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA. [email protected] Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE 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).
