A universal predictor-based machine learning model for optimal process maps in laser powder bed fusion process
Zhaochen Gu,
Shashank Sharma,
Daniel A. Riley,
Mangesh V. Pantawane,
Sameehan S. Joshi,
Song Fu and
Narendra B. Dahotre (narendra.dahotre@unt.edu)
Additional contact information
Zhaochen Gu: University of North Texas
Shashank Sharma: University of North Texas
Daniel A. Riley: University of North Texas
Mangesh V. Pantawane: University of North Texas
Sameehan S. Joshi: University of North Texas
Song Fu: University of North Texas
Narendra B. Dahotre: University of North Texas
Journal of Intelligent Manufacturing, 2023, vol. 34, issue 8, No 5, 3363 pages
Abstract:
Abstract The primary bottlenecks faced by the laser powder bed fusion (LPBF) process is the identification of optimal process parameters to obtain high density (> 99.8%) and a good surface finish (
Keywords: Laser powder bed fusion; Process parameters; Melting modes; Relative density; Surface roughness; Supervised machine learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-022-02004-0
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