Insights from the 3rd World Congress on Integrated Computational Materials Engineering

Howe, David H.; Goodlet, Brent; Weaver, Jordan S.; Σπανός, Γ.

doi:10.1007/s11837-016-1867-4

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…, ψ q ∈ R q + . Notice that the model introduced in Equation (1) involves a summation of three random processes defined in Equations (7), (12) and (13). Here, we approximate the multivariate T process (the surrogate model) with a Gaussian Process, so that the summation in the RHS of Equation (1) becomes another Gaussian Process due to the property of addition of statistically independent Gaussian random variables [63].…”

Section: Multivariate Calibration Modelmentioning

confidence: 99%

“…These models use the system governing equations to compute and predict specific quantities of interest (QoIs). As a well-established fact, all of these simulation models are imperfect and thus their predictions will differ key need for computational materials models [11,12], there is a literature gap in this area [13]. Chernatynskiy et al [3] present a review of the few existing works on UQ of multi-scale simulation models.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Calibration and Experimental Validation of a 3D Finite Element Thermal Model for Laser Powder Bed Fusion Metal Additive Manufacturing

Mahmoudi

Tapia

Karayagiz

et al. 2018

Integr Mater Manuf Innov

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Metal additive manufacturing (AM) typically suffers from high degree 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 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.

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Section: Multivariate Calibration Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multivariate Calibration and Experimental Validation of a 3D Finite Element Thermal Model for Laser Powder Bed Fusion Metal Additive Manufacturing

Mahmoudi

Tapia

Karayagiz

et al. 2018

Integr Mater Manuf Innov

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“…For example, until recently there were still major scientific and technological challenges associated with establishing (computational/experimental) linkages across the process-structure-property/performance paradigm [1] that underpins much of the research activities of the materials science community. Despite the many remaining challenges, this barrier has reduced considerably over the last decade through research programs and initiatives such as ICME [10,11] and MGI [12][13][14]. The design research community also has made great strides in areas such as design representation, design informatics, and design automation.…”

Section: A Workhop On Interdisciplinary Frontiers Ofmentioning

confidence: 99%

Interdisciplinary Research on Designing Engineering Material Systems: Results From a National Science Foundation Workshop

Arróyave

Shields

Chang

et al. 2018

Journal of Mechanical Design

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We present the results from a workshop on interdisciplinary research on design of engineering material systems, sponsored by the National Science Foundation. The workshop was prompted by the need to foster a culture of interdisciplinary collaboration between the engineering design and materials communities. The workshop addressed the following: (i) conceptual barriers between materials and engineering design research communities; (ii) research questions that the interdisciplinary field of materials design should focus on; (iii) processes and metrics to be used to validate research activities and outcomes on materials design; and (iv) strategies to sustain and grow the interdisciplinary field. This contribution presents a summary of the state of the field-elicited through extensive guided discussions between representatives of both communities-and a snapshot of research activities that have emerged since the workshop. Based on the increasing level of sophistication of interdisciplinary research programs on design of materials it is apparent that the field is growing and has great potential to play a key role in a vibrant interdisciplinary materials innovation ecosystem. Sustaining such efforts will contribute significantly to the advancement of technologies that will impact many industries and will enhance society-wide health, security, and economic well-being.

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“…Likelihood function: We arrange the full set of data as d ¼ ½Y > ; Z > > and calculate the likelihood function based on the GP assumptions, which yields pðdjh; b; /Þ $ N Nþn ðHb; RÞ (11) where Posterior distribution: Given the above, the posterior distribution is obtained using Bayes' rule…”

Section: Surrogatementioning

confidence: 99%

“…One field in which the application of UQ is highly desired is computational materials science in general [10] and the emerging field of Integrated Computational Materials Engineering (ICME) in particular [11][12][13]. ICME is a new approach within the broad field of materials science and engineering that aims to integrate computational materials models to enable the optimization of the materials, manufacturing processes, and component design long before components are fabricated [14].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Calibration and Uncertainty Quantification for a Physics-Based Precipitation Model of Nickel–Titanium Shape-Memory Alloys

Tapia

Johnson

Franco

et al. 2017

Journal of Manufacturing Science and Engineering

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Uncertainty quantification (UQ) is an emerging field that focuses on characterizing, quantifying, and potentially reducing, the uncertainties associated with computer simulation models used in a wide range of applications. Although it has been successfully applied to computer simulation models in areas such as structural engineering, climate forecasting, and medical sciences, this powerful research area is still lagging behind in materials simulation models. These are broadly defined as physics-based predictive models developed to predict material behavior, i.e., processing-microstructure-property relations and have recently received considerable interest with the advent of emerging concepts such as Integrated Computational Materials Engineering (ICME). The need of effective tools for quantifying the uncertainties associated with materials simulation models has been identified as a high priority research area in most recent roadmapping efforts in the field. In this paper, we present one of the first efforts in conducting systematic UQ of a physics-based materials simulation model used for predicting the evolution of precipitates in advanced nickel–titanium shape-memory alloys (SMAs) subject to heat treatment. Specifically, a Bayesian calibration approach is used to conduct calibration of the precipitation model using a synthesis of experimental and computer simulation data. We focus on constructing a Gaussian process-based surrogate modeling approach for achieving this task, and then benchmark the predictive accuracy of the calibrated model with that of the model calibrated using traditional Markov chain Monte Carlo (MCMC) methods.

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Insights from the 3rd World Congress on Integrated Computational Materials Engineering

Cited by 6 publications

References 4 publications

Multivariate Calibration and Experimental Validation of a 3D Finite Element Thermal Model for Laser Powder Bed Fusion Metal Additive Manufacturing

Multivariate Calibration and Experimental Validation of a 3D Finite Element Thermal Model for Laser Powder Bed Fusion Metal Additive Manufacturing

Interdisciplinary Research on Designing Engineering Material Systems: Results From a National Science Foundation Workshop

Bayesian Calibration and Uncertainty Quantification for a Physics-Based Precipitation Model of Nickel–Titanium Shape-Memory Alloys

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