A finite difference method for fast prediction and control of part-scale temperature evolution in laser powder bed fusion

Yong Ren, Qian Wang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Part-scale thermal modeling is a crucial building block in the multi-scale thermo-mechanical analysis for laser powder bed fusion process, and it plays a pivotal role in enabling computationally efficient thermal simulation of parts of large size. This paper presents a novel finite difference model that can provide fast prediction of part-scale temperature evolution to enable model-based part-scale thermal control. The effectiveness of the proposed modeling method is illustrated through a case study of a square-canonical geometry of Inconel 718, where several heat transfer parameters of the model are identified by matching the model computation with the in-situ measurements of interlayer temperature obtained from the build process on an EOS M280 system. The root-mean-square error between the model computed interlayer temperature and mean values of the measured temperature is less than 25 °C, suggesting that the model captures the major underlying physics for interlayer temperature prediction once the model parameters are identified. The proposed modeling efforts demonstrate that the heat transfer between part components and powder bed is essential to characterize part-scale temperature evolution. Based on the proposed part-scale thermal model, a numerical study on optimal control of interlayer temperature through layer-by-layer control of laser power is also presented. Results from this study set a foundation for future experimental investigation of model-based part-scale thermal control to reduce overheating by which to improve build quality for powder bed fusion systems.

Original languageEnglish (US)
Pages (from-to)299-314
Number of pages16
JournalJournal of Manufacturing Processes
StatePublished - May 5 2023

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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