Predicting mechanical properties from co-axial melt pool monitoring signals in laser powder bed fusion

Raj, Anant; Owen, C. J.; Stegman, Benjamin; Abdel-Khalik, Hany S.; Zhang, Xinghang; Sutherland, John W.

doi:10.1016/j.jmapro.2023.04.083

Cited by 5 publications

(1 citation statement)

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“…The objective is to visualize certain aspects of the printing process that influence mechanical properties and to supplement the temperature sensing data. Feasibility proven by Raj et al [16] by using a CMOS camera and photodiode mounted coaxially with the laser to quantify the area and intensity of the molten pool, which resulted in reasonably accurate predictions of all ductility-related attributes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of mechanical properties of LPBF built part based on process monitoring and Gaussian process regression

Yuan,

Peng,

et al. 2024

Meas. Sci. Technol.

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As a highly promising technology in additive manufacturing, the laser powder bed fusion (LPBF) has only limited application due to its low reproducibility. In this study, the image information of the 316L specimen after laser scanning and powder paving of each layer was acquired by a CMOS industrial camera. The important features were selected,extrated and quantificated by analyzing the tensile test results. Finally,combined with the laser power, the quantified features were as input of a Gaussian process regression model based on optimisation algorithm of grid search to predict the tensile strength of 316L specimen. The results show that the quantized image features have a significant improvement on the regression effect, and the coefficient of determination(R²) is improved from 63% to 90.57% compared to using only the laser power as input.

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Section: Introductionmentioning

confidence: 99%