Machine-learning based thermal-geometric predictive modeling of laser powder bed fusion additive manufacturing

Yong Ren, Qian Wang, Pan Michaleris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a machine-learning based predictive modeling approach on how process input parameters affect melt-pool volumes for a Laser Powder Bed Fusion (LPBF) additive manufacturing (AM) process. A physics-informed approach is adopted to define input features for the machine-learning models, and a two-level architecture is defined for the model training and validation. Specifically, a so-called initial (pre-deposition) temperature at the deposition point is identified as one key variable in characterizing thermal history for predicting melt-pool sizes. At the lower-level of the two-level modeling architecture, a hybrid model consisting of an analytical computation and a Gaussian process is developed to predict the pre-deposition temperature using process input parameters. Then at the higher-level of the modeling architecture, eight machine learning algorithms (including machine-learning based regression models and a two-layer neural network) are evaluated in predicting melt-pool volumes using the pre-deposition temperature and process parameters. For this proof-of-concept study, simulation data generated from the Autodesk Netfabb Local Simulation are used for model training and validation. The study shows that the proposed two-level machine learning model achieves high prediction performance and its prediction accuracy improves significantly compared to the one-level machine learning without using pre-deposition temperature as an input feature.

Original languageEnglish (US)
Title of host publicationAdvanced Driver Assistance and Autonomous Technologies; Advances in Control Design Methods; Advances in Robotics; Automotive Systems; Design, Modeling, Analysis, and Control of Assistive and Rehabilitation Devices; Diagnostics and Detection; Dynamics and Control of Human-Robot Systems; Energy Optimization for Intelligent Vehicle Systems; Estimation and Identification; Manufacturing
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791859148
DOIs
StatePublished - Jan 1 2019
EventASME 2019 Dynamic Systems and Control Conference, DSCC 2019 - Park City, United States
Duration: Oct 8 2019Oct 11 2019

Publication series

NameASME 2019 Dynamic Systems and Control Conference, DSCC 2019
Volume1

Conference

ConferenceASME 2019 Dynamic Systems and Control Conference, DSCC 2019
CountryUnited States
CityPark City
Period10/8/1910/11/19

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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