Abstract
Metal additive manufacturing (AM) typically suffers from high degrees of variability in the properties/performance of the fabricated parts, particularly due to the lack of understanding and control over the physical mechanisms that govern microstructure formation during fabrication. This paper directly addresses an important problem in metal AM: the determination of the thermal history of the deposited material. Any attempts to link process to microstructure in AM would need to consider the thermal history of the material. In situ monitoring only provides partial information and simulations may be necessary to have a comprehensive understanding of the thermo-physical conditions to which the deposited material is subjected. We address this in the present work through linking thermal models to experiments via a computationally efficient surrogate modeling approach based on multivariate Gaussian processes (MVGPs). The MVGPs are then used to calibrate the free parameters of the multi-physics models against experiments, sidestepping the use of prohibitively expensive Monte Carlo-based calibration. This framework thus makes it possible to efficiently evaluate the impact of varying process parameter inputs on the characteristics of the melt pool during AM. We demonstrate the framework on the calibration of a thermal model for laser powder bed fusion AM of Ti-6Al-4V against experiments carried out over a wide window in the process parameter space. While this work deals with problems related to AM, its applicability is wider as the proposed framework could potentially be used in many other ICME-based problems where it is essential to link expensive computational materials science models to available experimental data.Graphical Two-stage multi-variate statistical calibration of the finite element thermal model
Highlights
Integrated Computational Materials Engineering (ICME) prescribes a framework for the acceleration in the development and deployment of materials through the establishment and exploitation of processstructure-property-performance (PSPP) relationships
We developed a three-dimensional finite element method (FEM) based thermal model implemented in COMSOL Multiphysics software to study melt pool characteristics, including geometry and thermal profiles, during the fabrication of single tracks printed in a thin layer of powder on top of a solid substrate
It is reported in the literature that the melt pool temperature and geometry are important factors influencing the outcome of the Laser Powder-Bed Fusion (L-PBF) process [65]
Summary
Integrated Computational Materials Engineering (ICME) prescribes a framework for the acceleration in the development and deployment of materials through the establishment and exploitation of processstructure-property-performance (PSPP) relationships. ICME involves utilization of physics-based simulation models that help understand the behavior of complex systems. These models use the system governing equations to compute and predict specific quantities of interest (QoIs). For example for the L-PBF process, different simulation models exist that focus on different physical aspects, including characteristics of the powder-bed, evolution of the melt pool, solidification process, and generation of residual stresses, etc These models introduce various sources of uncertainty [16]. While the focus of the work is on specific physical phenomena associated with L-PBF AM, the overall framework can be readily adapted to address similar problems that involve systematic calibration of complex multipleoutput computational materials models.
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