The effects of material property assumptions on predicted meltpool shape for laser powder bed fusion based additive manufacturing

Teng, Chong; Ashby, Kathryn; Phan, Nam; Pal, Devendra; Stucker, Brent

doi:10.1088/0957-0233/27/8/085602

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…It has been reported [36,37] that the material properties can affect meltpool features. Therefore, to have a comprehensive model with higher accuracy, a material-based model including thermophysical properties is intended [38].…”

Section: Developing a Model To Calculate The Meltpool Depthmentioning

confidence: 99%

A comprehensive study on meltpool depth in laser-based powder bed fusion of Inconel 718

Khorasani

Ghasemi

Leary³

et al. 2022

Int J Adv Manuf Technol

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One problematic task in the laser-based powder bed fusion (LB-PBF) process is the estimation of meltpool depth, which is a function of the process parameters and thermophysical properties of the materials. In this research, the effective factors that drive the meltpool depth such as optical penetration depth, angle of incidence, the ratio of laser power to scan speed, surface properties and plasma formation are discussed. The model is useful to estimate the meltpool depth for various manufacturing conditions. A proposed methodology is based on the simulation of a set of process parameters to obtain the variation of meltpool depth and temperature, followed by validation with reference to experimental test data. Numerical simulation of the LB-PBF process was performed using the computational scientific tool “Flow3D Version 11.2” to obtain the meltpool features. The simulation data was then developed into a predictive analytical model for meltpool depth and temperature based on the thermophysical powder properties and associated parameters. The novelty and contribution of this research are characterising the fundamental governing factors on meltpool depth and developing an analytical model based on process parameters and powder properties. The predictor model helps to accurately estimate the meltpool depth which is important and has to be sufficient to effectively fuse the powder to the build plate or the previously solidified layers ensuring proper bonding quality. Results showed that the developed analytical model has a high accuracy to predict the meltpool depth. The model is useful to rapidly estimate the optimal process window before setting up the manufacturing tasks and can therefore save on lead-time and cost. This methodology is generally applied to Inconel 718 processing and is generalisable for any powder of interest. The discussions identified how the effective physical factors govern the induced heat versus meltpool depth which can affect the bonding and the quality of LB-PBF components.

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Section: Developing a Model To Calculate The Meltpool Depthmentioning

confidence: 99%

A comprehensive study on meltpool depth in laser-based powder bed fusion of Inconel 718

Khorasani

Ghasemi

Leary³

et al. 2022

Int J Adv Manuf Technol

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“…In 2012, the National Institute of Standards and Technology (NIST) held a workshop entitled 'measurement science roadmap for metal-based additive manufacturing' to understand and address the hurdles faced by the hard matter (metal) community from the perspective of measurement science [2]. Some sample research includes traceable thermal [4], meltpool shape [5] and dimensional metrology, which however remain mainly in fundamental measurement theory without real-time exploration yet. In situ metrology modules available from AM machine manufacturers and measurement specialists include equipment such as pyrometer and camera [6], as well as technologies such as coherent imaging and x-ray tomography [7].…”

Section: Real-time Interferometric Monitoring and Measuring Of Photop...mentioning

confidence: 99%

Real-time interferometric monitoring and measuring of photopolymerization based stereolithographic additive manufacturing process: sensor model and algorithm

Zhao

Rosen

2016

Meas. Sci. Technol.

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As additive manufacturing is poised for growth and innovations, it faces barriers of lack of in-process metrology and control to advance into wider industry applications. The exposure controlled projection lithography (ECPL) is a layerless mask-projection stereolithographic additive manufacturing process, in which parts are fabricated from photopolymers on a stationary transparent substrate. To improve the process accuracy with closed-loop control for ECPL, this paper develops an interferometric curing monitoring and measuring (ICM&M) method which addresses the sensor modeling and algorithms issues. A physical sensor model for ICM&M is derived based on interference optics utilizing the concept of instantaneous frequency. The associated calibration procedure is outlined for ICM&M measurement accuracy. To solve the sensor model, particularly in real time, an online evolutionary parameter estimation algorithm is developed adopting moving horizon exponentially weighted Fourier curve fitting and numerical integration. As a preliminary validation, simulated real-time measurement by offline analysis of a video of interferograms acquired in the ECPL process is presented. The agreement between the cured height estimated by ICM&M and that measured by microscope indicates that the measurement principle is promising as real-time metrology for global measurement and control of the ECPL process.

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“…Xiong et al [17] studied online measurement of bead geometry in gas metal arc welding based AM using passive vision. Teng et al [18] studied the effects of material property assumptions on predicted meltpool shape for laser powder bed fusion based additive manufacturing. Paul et al developed a three-dimensional thermomechanical finite element (FE) model to [19] calculate the thermal deformation in AM parts based on slice thickness, part orientation, scanning speed, and material properties.…”

Section: Introductionmentioning

confidence: 99%

Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network

Wang

Zhang

et al. 2020

Applied Sciences

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This paper presents a Thermal Deformation defect prediction method for layered printing using Convolutional Generative Adversarial Network (CGAN). Firstly, the original manifold mesh is converted into layered image in Printing Coordinate System (PCS). The trajectory inside layered image with various infill patterns are generated for making comparisons. Inspired by monocular vision and even binocular vision, the mathematical model of thermal defect prediction via infrared thermogram is built via virtual printing of Digital Twins to preset the initial parameters of Artificial Neural Network (ANN). Particularly, the depth convolution is used to extract multi-scale features of layered image. By using transfer learning techniques to identify small sample data, the CGAN is employed to build the nonlinear implicit relations between thermal deformation and multi-scale features. The binocular stereo vision laser scanner is used to determine the actual thermal deformation of the target printed objects. The shape deformation dissimilarity can be succinctly calculated by evaluating the surface profile error via mesh registration between the original source and target mesh model. The proposed method is verified by physical experiments. The experiment proved that the proposed method can deal with the thermal deformation with more optimal parameters, which contributes to performance forward design of irregular complex parts regarding diversified customized requirements.

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The effects of material property assumptions on predicted meltpool shape for laser powder bed fusion based additive manufacturing

Cited by 6 publications

References 28 publications

A comprehensive study on meltpool depth in laser-based powder bed fusion of Inconel 718

A comprehensive study on meltpool depth in laser-based powder bed fusion of Inconel 718

Real-time interferometric monitoring and measuring of photopolymerization based stereolithographic additive manufacturing process: sensor model and algorithm

Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network

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