Control of interlayer temperature through laser power optimization in powder bed fusion

Yong Ren, Corey Dickman, Matthew Pantano, Edward Reutzel, Qian Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Existing studies have demonstrated that for laser powder bed fusion (L-PBF) processes, applying the default parameters given by the manufacturers to build certain geometries (e.g., those including an overhang structure) could lead to overheating during the build process. Therefore, there is a strong need to optimize process parameters over the geometry to reduce overheating. Prior studies have also shown that interlayer temperatures have a significant thermal effect on the resulting melt-pool size and morphology, suggesting the need of controlling part-level temperatures to improve the build quality. Motivated by these needs, this paper presents an experimental demonstration of a model-based optimal control of laser power on each grouped layer (consisting of multiple physical layers) to reduce interlayer temperatures below a target threshold. The optimal laser-power profiles are derived from solving a convex program, which provides a theoretical guarantee for optimality, and they are implemented on an EOS M280 L-PBF system as a feed-forward control. Results from this study show that the thermal control is capable of reducing interlayer temperatures when needed. Moreover, the thermal control helps reduce the variability of melt-pool size along the build direction, leading to more than 20% reduction in melt-pool mean half-width at a representative layer (layer 876) compared to the uncontrolled case. Furthermore, the microhardness of the area of interest (near layer 876) under the thermal control has 6% increase in the percentage-of-test-points above 297 HV and 32% increase in the percentage-of-test-points above 318 HV compared to the uncontrolled case, indicating improved hardness under the optimal control of laser power.

Original languageEnglish (US)
Pages (from-to)189-203
Number of pages15
JournalJournal of Manufacturing Processes
Volume135
DOIs
StatePublished - Feb 15 2025

All Science Journal Classification (ASJC) codes

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

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