Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates

Abstract Relational linkages connecting process, structure, and properties are some of the most sought after goals in additive manufacturing (AM). This is desired especially because the microstructural grain morphologies of AM components can be vastly different than their conventionally manufactured...

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Autores principales:	Robert Saunders, Celia Butler, John Michopoulos, Dimitris Lagoudas, Alaa Elwany, Amit Bagchi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/eac4f2eb60f14a5ca1062d3782fce863
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spelling	oai:doaj.org-article:eac4f2eb60f14a5ca1062d3782fce8632021-12-02T15:02:22ZMechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates10.1038/s41524-021-00548-y2057-3960https://doaj.org/article/eac4f2eb60f14a5ca1062d3782fce8632021-06-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00548-yhttps://doaj.org/toc/2057-3960Abstract Relational linkages connecting process, structure, and properties are some of the most sought after goals in additive manufacturing (AM). This is desired especially because the microstructural grain morphologies of AM components can be vastly different than their conventionally manufactured counterparts. Furthermore, data collection at the microscale is costly. Consequently, this work describes and demonstrates a methodology to link microstructure morphology to mechanical properties using functional Gaussian process surrogate models in a directed graphical network capable of achieving near real-time property predictions with single digit error magnitudes when predicting full stress–strain histories of a given microstructure. This methodology is presented and demonstrated using computationally generated microstructures and results from crystal plasticity simulations on those microstructures. The surrogate model uses grain-level microstructural descriptors rather than whole microstructure descriptors so that properties of new, arbitrary microstructures can be predicted. The developed network has the potential to scale to predict mechanical properties of grain structures that would be infeasible to simulate using finite element methods.Robert SaundersCelia ButlerJohn MichopoulosDimitris LagoudasAlaa ElwanyAmit BagchiNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Robert Saunders Celia Butler John Michopoulos Dimitris Lagoudas Alaa Elwany Amit Bagchi Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates
description	Abstract Relational linkages connecting process, structure, and properties are some of the most sought after goals in additive manufacturing (AM). This is desired especially because the microstructural grain morphologies of AM components can be vastly different than their conventionally manufactured counterparts. Furthermore, data collection at the microscale is costly. Consequently, this work describes and demonstrates a methodology to link microstructure morphology to mechanical properties using functional Gaussian process surrogate models in a directed graphical network capable of achieving near real-time property predictions with single digit error magnitudes when predicting full stress–strain histories of a given microstructure. This methodology is presented and demonstrated using computationally generated microstructures and results from crystal plasticity simulations on those microstructures. The surrogate model uses grain-level microstructural descriptors rather than whole microstructure descriptors so that properties of new, arbitrary microstructures can be predicted. The developed network has the potential to scale to predict mechanical properties of grain structures that would be infeasible to simulate using finite element methods.
format	article
author	Robert Saunders Celia Butler John Michopoulos Dimitris Lagoudas Alaa Elwany Amit Bagchi
author_facet	Robert Saunders Celia Butler John Michopoulos Dimitris Lagoudas Alaa Elwany Amit Bagchi
author_sort	Robert Saunders
title	Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates
title_short	Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates
title_full	Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates
title_fullStr	Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates
title_full_unstemmed	Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates
title_sort	mechanical behavior predictions of additively manufactured microstructures using functional gaussian process surrogates
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/eac4f2eb60f14a5ca1062d3782fce863
work_keys_str_mv	AT robertsaunders mechanicalbehaviorpredictionsofadditivelymanufacturedmicrostructuresusingfunctionalgaussianprocesssurrogates AT celiabutler mechanicalbehaviorpredictionsofadditivelymanufacturedmicrostructuresusingfunctionalgaussianprocesssurrogates AT johnmichopoulos mechanicalbehaviorpredictionsofadditivelymanufacturedmicrostructuresusingfunctionalgaussianprocesssurrogates AT dimitrislagoudas mechanicalbehaviorpredictionsofadditivelymanufacturedmicrostructuresusingfunctionalgaussianprocesssurrogates AT alaaelwany mechanicalbehaviorpredictionsofadditivelymanufacturedmicrostructuresusingfunctionalgaussianprocesssurrogates AT amitbagchi mechanicalbehaviorpredictionsofadditivelymanufacturedmicrostructuresusingfunctionalgaussianprocesssurrogates
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Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates

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