Heat source model calibration for thermal analysis of laser powder-bed fusion

2020 ◽  
Vol 106 (7-8) ◽  
pp. 3367-3379 ◽  
Author(s):  
Shahriar Imani Shahabad ◽  
Zhidong Zhang ◽  
Ali Keshavarzkermani ◽  
Usman Ali ◽  
Yahya Mahmoodkhani ◽  
...  
Keyword(s):  
Thermal Analysis ◽  
Heat Source ◽  
Model Calibration ◽  
Source Model ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Heat Source Model ◽  
Powder Bed

scholarly journals Novel Calibration Strategy for Validation of Finite Element Thermal Analysis of Selective Laser Melting Process Using Bayesian Optimization

Materials ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 4948
Author(s):  
Masahiro Kusano ◽  
Houichi Kitano ◽  
Makoto Watanabe
Keyword(s):  
Thermal Analysis ◽  
Finite Element ◽  
Heat Source ◽  
Selective Laser Melting ◽  
High Speed ◽  
Thermal Analyses ◽  
Source Model ◽  
Bayesian Optimization ◽  
Laser Melting ◽  
Heat Source Model

Selective laser melting (SLM) produces a near-net-shaped product by scanning a concentrated high-power laser beam over a thin layer of metal powder to melt and solidify it. During the SLM process, the material temperature cyclically and sharply rises and falls. Thermal analyses using the finite element method help to understand such a complex thermal history to affect the microstructure, material properties, and performance. This paper proposes a novel calibration strategy for the heat source model to validate the thermal analysis. First, in-situ temperature measurement by high-speed thermography was conducted for the absorptivity calibration. Then, the accurate simulation error was defined by processing the cross-sectional bead shape images by the experimental observations and simulations. In order to minimize the error, the optimal shape parameters of the heat source model were efficiently found by using Bayesian optimization. Bayesian optimization allowed us to find the optimal parameters with an error of less than 4% within 50 iterations of the thermal simulations. It demonstrated that our novel calibration strategy with Bayesian optimization can be effective to improve the accuracy of predicting the temperature field during the SLM process and to save the computational costs for the heat source model optimization.

scholarly journals On the Relevance of Volumetric Energy Density in the Investigation of Inconel 718 Laser Powder Bed Fusion

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 538 ◽  
Author(s):  
Fabrizia Caiazzo ◽  
Vittorio Alfieri ◽  
Giuseppe Casalino
Keyword(s):  
Surface Roughness ◽  
Energy Density ◽  
Process Parameters ◽  
Vickers Hardness ◽  
Powder Bed Fusion ◽  
Processing Conditions ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Fractional Density ◽  
Experimental Variation

Laser powder bed fusion (LPBF) can fabricate products with tailored mechanical and surface properties. In fact, surface texture, roughness, pore size, the resulting fractional density, and microhardness highly depend on the processing conditions, which are very difficult to deal with. Therefore, this paper aims at investigating the relevance of the volumetric energy density (VED) that is a concise index of some governing factors with a potential operational use. This paper proves the fact that the observed experimental variation in the surface roughness, number and size of pores, the fractional density, and Vickers hardness can be explained in terms of VED that can help the investigator in dealing with several process parameters at once.

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