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Special Issue "Empowering Materials Processing and Performance from Data and AI"

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".

Deadline for manuscript submissions: closed (15 March 2021).

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A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Francisco Chinesta
E-Mail Website
Guest Editor
ESI Chair, Arts et Metiers Institute of Technology, CNRS, CNAM, PIMM, HESAM Université, F-75013 Paris, France
Interests: model reduction; proper generalized decomposition; kinetic theory of polymers and suspensions; composite materials and processing
Special Issues, Collections and Topics in MDPI journals
Prof. Elías Cueto
E-Mail Website
Guest Editor
Aragon Institute of Engineering Research. Universidad de Zaragoza, Zaragoza, Spain
Interests: model order reduction; scientific machine learning; data-driven computational mechanics; virtual and augmented reality
Prof. Benjamin Klusemann
E-Mail Website
Guest Editor
Institute of Product and Process Innovation, Leuphana University of Lüneburg, Lüneburg, Germany & Department Solid State Joining Processes Materials Mechanics, Institute of Materials Research, Helmholtz-Zentrum Geesthacht, Geestacht, Germany
Interests: scientific machine learning; digital twins; process simulation/ materials modeling; local modification techniques; residual stress engineering; solid state joining processes

Special Issue Information

Dear Colleagues,

This Special Issue, “Empowering Materials Processing and Performances from Data and AI”, will address advances in materials engineering, with special emphasis on the bridging from raw materials, processing and the induced properties and performances. Third millennium engineering is addressing new challenges in materials sciences and engineering. The present topical issue aims at addressing four key challenges using data and artificial intelligence:

(i) processing data, for enhancing existing physic-based models or creating data-driven models from scratch when the former (physics-based) models are absent or too poor for making valuable predictions;
(ii) proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying, data and data-driven models;
(iii) enabling data to be smarter (in the same way that data allow enriching physics-based models, those models allow transforming big-data into smart-data);
(iv) inverting usual material engineering with all the just referred techniques, to discover materials and their processing for optimal properties/performances.

Original papers are solicited on all types of approaches and materials, scales and applications. Of particular interest are recent developments in the use of data and AI in the four axes mentioned before.

Prof. Francisco Chinesta
Prof. Elias Cueto
Prof. Benjamin Klusemann
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Materials properties
  • Materials processing
  • Materials performances
  • Physics-based modeling
  • Data-driven modeling
  • Materials optimization
  • Materials discovering

Published Papers (9 papers)

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Editorial

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Editorial
Empowering Materials Processing and Performance from Data and AI
Materials 2021, 14(16), 4409; https://doi.org/10.3390/ma14164409 - 06 Aug 2021
Viewed by 335
Abstract
Third millennium engineering is addressing new challenges in materials sciences and engineering [...] Full article
(This article belongs to the Special Issue Empowering Materials Processing and Performance from Data and AI)

Research

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Article
A Stochastic FE2 Data-Driven Method for Nonlinear Multiscale Modeling
Materials 2021, 14(11), 2875; https://doi.org/10.3390/ma14112875 - 27 May 2021
Cited by 1 | Viewed by 831
Abstract
A stochastic data-driven multilevel finite-element (FE2) method is introduced for random nonlinear multiscale calculations. A hybrid neural-network–interpolation (NN–I) scheme is proposed to construct a surrogate model of the macroscopic nonlinear constitutive law from representative-volume-element calculations, whose results are used as input [...] Read more.
A stochastic data-driven multilevel finite-element (FE2) method is introduced for random nonlinear multiscale calculations. A hybrid neural-network–interpolation (NN–I) scheme is proposed to construct a surrogate model of the macroscopic nonlinear constitutive law from representative-volume-element calculations, whose results are used as input data. Then, a FE2 method replacing the nonlinear multiscale calculations by the NN–I is developed. The NN–I scheme improved the accuracy of the neural-network surrogate model when insufficient data were available. Due to the achieved reduction in computational time, which was several orders of magnitude less than that to direct FE2, the use of such a machine-learning method is demonstrated for performing Monte Carlo simulations in nonlinear heterogeneous structures and propagating uncertainties in this context, and the identification of probabilistic models at the macroscale on some quantities of interest. Applications to nonlinear electric conduction in graphene–polymer composites are presented. Full article
(This article belongs to the Special Issue Empowering Materials Processing and Performance from Data and AI)
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Article
Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions
Materials 2021, 14(8), 1883; https://doi.org/10.3390/ma14081883 - 10 Apr 2021
Cited by 2 | Viewed by 886
Abstract
Within the fields of materials mechanics, the consideration of physical laws in machine learning predictions besides the use of data can enable low prediction errors and robustness as opposed to predictions only based on data. On the one hand, exclusive utilization of fundamental [...] Read more.
Within the fields of materials mechanics, the consideration of physical laws in machine learning predictions besides the use of data can enable low prediction errors and robustness as opposed to predictions only based on data. On the one hand, exclusive utilization of fundamental physical relationships might show significant deviations in their predictions compared to reality, due to simplifications and assumptions. On the other hand, using only data and neglecting well-established physical laws can create the need for unreasonably large data sets that are required to exhibit low bias and are usually expensive to collect. However, fundamental but simplified physics in combination with a corrective model that compensates for possible deviations, e.g., to experimental data, can lead to physics-based predictions with low prediction errors, also despite scarce data. In this article, it is demonstrated that a hybrid model approach consisting of a physics-based model that is corrected via an artificial neural network represents an efficient prediction tool as opposed to a purely data-driven model. In particular, a semi-analytical model serves as an efficient low-fidelity model with noticeable prediction errors outside its calibration domain. An artificial neural network is used to correct the semi-analytical solution towards a desired reference solution provided by high-fidelity finite element simulations, while the efficiency of the semi-analytical model is maintained and the applicability range enhanced. We utilize residual stresses that are induced by laser shock peening as a use-case example. In addition, it is shown that non-unique relationships between model inputs and outputs lead to high prediction errors and the identification of salient input features via dimensionality analysis is highly beneficial to achieve low prediction errors. In a generalization task, predictions are also outside the process parameter space of the training region while remaining in the trained range of corrections. The corrective model predictions show substantially smaller errors than purely data-driven model predictions, which illustrates one of the benefits of the hybrid modelling approach. Ultimately, when the amount of samples in the data set is reduced, the generalization of the physics-related corrective model outperforms the purely data-driven model, which also demonstrates efficient applicability of the proposed hybrid modelling approach to problems where data is scarce. Full article
(This article belongs to the Special Issue Empowering Materials Processing and Performance from Data and AI)
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Article
A Strategy for Dimensionality Reduction and Data Analysis Applied to Microstructure–Property Relationships of Nanoporous Metals
Materials 2021, 14(8), 1822; https://doi.org/10.3390/ma14081822 - 07 Apr 2021
Cited by 2 | Viewed by 778
Abstract
Nanoporous metals, with their complex microstructure, represent an ideal candidate for the development of methods that combine physics, data, and machine learning. The preparation of nanporous metals via dealloying allows for tuning of the microstructure and macroscopic mechanical properties within a large design [...] Read more.
Nanoporous metals, with their complex microstructure, represent an ideal candidate for the development of methods that combine physics, data, and machine learning. The preparation of nanporous metals via dealloying allows for tuning of the microstructure and macroscopic mechanical properties within a large design space, dependent on the chosen dealloying conditions. Specifically, it is possible to define the solid fraction, ligament size, and connectivity density within a large range. These microstructural parameters have a large impact on the macroscopic mechanical behavior. This makes this class of materials an ideal science case for the development of strategies for dimensionality reduction, supporting the analysis and visualization of the underlying structure–property relationships. Efficient finite element beam modeling techniques were used to generate ~200 data sets for macroscopic compression and nanoindentation of open pore nanofoams. A strategy consisting of dimensional analysis, principal component analysis, and machine learning allowed for data mining of the microstructure–property relationships. It turned out that the scaling law of the work hardening rate has the same exponent as the Young’s modulus. Simple linear relationships are derived for the normalized work hardening rate and hardness. The hardness to yield stress ratio is not limited to 1, as commonly assumed for foams, but spreads over a large range of values from 0.5 to 3. Full article
(This article belongs to the Special Issue Empowering Materials Processing and Performance from Data and AI)
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Article
Evaluation of Ti–Mn Alloys for Additive Manufacturing Using High-Throughput Experimental Assays and Gaussian Process Regression
Materials 2020, 13(20), 4641; https://doi.org/10.3390/ma13204641 - 17 Oct 2020
Cited by 3 | Viewed by 1250
Abstract
Compositionally graded cylinders of Ti–Mn alloys were produced using the Laser Engineered Net Shaping (LENS™) technique, with Mn content varying from 0 to 12 wt.% along the cylinder axis. The cylinders were subjected to different post-build heat treatments to produce a large sample [...] Read more.
Compositionally graded cylinders of Ti–Mn alloys were produced using the Laser Engineered Net Shaping (LENS™) technique, with Mn content varying from 0 to 12 wt.% along the cylinder axis. The cylinders were subjected to different post-build heat treatments to produce a large sample library of α–β microstructures. The microstructures in the sample library were studied using back-scattered electron (BSE) imaging in a scanning electron microscope (SEM), and their mechanical properties were evaluated using spherical indentation stress–strain protocols. These protocols revealed that the microstructures exhibited features with averaged chord lengths in the range of 0.17–1.78 μm, and beta content in the range of 20–83 vol.%. The estimated values of the Young’s moduli and tensile yield strengths from spherical indentation were found to vary in the ranges of 97–130 GPa and 828–1864 MPa, respectively. The combined use of the LENS technique along with the spherical indentation protocols was found to facilitate the rapid exploration of material and process spaces. Analyses of the correlations between the process conditions, several key microstructural features, and the measured material properties were performed via Gaussian process regression (GPR). These data-driven statistical models provided valuable insights into the underlying correlations between these variables. Full article
(This article belongs to the Special Issue Empowering Materials Processing and Performance from Data and AI)
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Article
A Methodology for the Statistical Calibration of Complex Constitutive Material Models: Application to Temperature-Dependent Elasto-Visco-Plastic Materials
Materials 2020, 13(19), 4402; https://doi.org/10.3390/ma13194402 - 02 Oct 2020
Cited by 2 | Viewed by 672
Abstract
The calibration of any sophisticated model, and in particular a constitutive relation, is a complex problem that has a direct impact in the cost of generating experimental data and the accuracy of its prediction capacity. In this work, we address this common situation [...] Read more.
The calibration of any sophisticated model, and in particular a constitutive relation, is a complex problem that has a direct impact in the cost of generating experimental data and the accuracy of its prediction capacity. In this work, we address this common situation using a two-stage procedure. In order to evaluate the sensitivity of the model to its parameters, the first step in our approach consists of formulating a meta-model and employing it to identify the most relevant parameters. In the second step, a Bayesian calibration is performed on the most influential parameters of the model in order to obtain an optimal mean value and its associated uncertainty. We claim that this strategy is very efficient for a wide range of applications and can guide the design of experiments, thus reducing test campaigns and computational costs. Moreover, the use of Gaussian processes together with Bayesian calibration effectively combines the information coming from experiments and numerical simulations. The framework described is applied to the calibration of three widely employed material constitutive relations for metals under high strain rates and temperatures, namely, the Johnson–Cook, Zerilli–Armstrong, and Arrhenius models. Full article
(This article belongs to the Special Issue Empowering Materials Processing and Performance from Data and AI)
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Article
Nonlinear Regression Operating on Microstructures Described from Topological Data Analysis for the Real-Time Prediction of Effective Properties
Materials 2020, 13(10), 2335; https://doi.org/10.3390/ma13102335 - 19 May 2020
Cited by 5 | Viewed by 803
Abstract
Real-time decision making needs evaluating quantities of interest (QoI) in almost real time. When these QoI are related to models based on physics, the use of Model Order Reduction techniques allows speeding-up calculations, enabling fast and accurate evaluations. To accommodate real-time constraints, a [...] Read more.
Real-time decision making needs evaluating quantities of interest (QoI) in almost real time. When these QoI are related to models based on physics, the use of Model Order Reduction techniques allows speeding-up calculations, enabling fast and accurate evaluations. To accommodate real-time constraints, a valuable route consists of computing parametric solutions—the so-called computational vademecums—that constructed off-line, can be inspected on-line. However, when dealing with shapes and topologies (complex or rich microstructures) their parametric description constitutes a major difficulty. In this paper, we propose using Topological Data Analysis for describing those rich topologies and morphologies in a concise way, and then using the associated topological descriptions for generating accurate supervised classification and nonlinear regression, enabling an almost real-time evaluation of QoI and the associated decision making. Full article
(This article belongs to the Special Issue Empowering Materials Processing and Performance from Data and AI)
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Article
A Data-Driven Learning Method for Constitutive Modeling: Application to Vascular Hyperelastic Soft Tissues
Materials 2020, 13(10), 2319; https://doi.org/10.3390/ma13102319 - 18 May 2020
Cited by 4 | Viewed by 1034
Abstract
We address the problem of machine learning of constitutive laws when large experimental deviations are present. This is particularly important in soft living tissue modeling, for instance, where large patient-dependent data is found. We focus on two aspects that complicate the problem, namely, [...] Read more.
We address the problem of machine learning of constitutive laws when large experimental deviations are present. This is particularly important in soft living tissue modeling, for instance, where large patient-dependent data is found. We focus on two aspects that complicate the problem, namely, the presence of an important dispersion in the experimental results and the need for a rigorous compliance to thermodynamic settings. To address these difficulties, we propose to use, respectively, Topological Data Analysis techniques and a regression over the so-called General Equation for the Nonequilibrium Reversible-Irreversible Coupling (GENERIC) formalism (M. Grmela and H. Ch. Oettinger, Dynamics and thermodynamics of complex fluids. I. Development of a general formalism. Phys. Rev. E 56, 6620, 1997). This allows us, on one hand, to unveil the true “shape” of the data and, on the other, to guarantee the fulfillment of basic principles such as the conservation of energy and the production of entropy as a consequence of viscous dissipation. Examples are provided over pseudo-experimental and experimental data that demonstrate the feasibility of the proposed approach. Full article
(This article belongs to the Special Issue Empowering Materials Processing and Performance from Data and AI)
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Article
Data-Oriented Constitutive Modeling of Plasticity in Metals
Materials 2020, 13(7), 1600; https://doi.org/10.3390/ma13071600 - 01 Apr 2020
Cited by 2 | Viewed by 1298
Abstract
Constitutive models for plastic deformation of metals are typically based on flow rules determining the transition from elastic to plastic response of a material as function of the applied mechanical load. These flow rules are commonly formulated as a yield function, based on [...] Read more.
Constitutive models for plastic deformation of metals are typically based on flow rules determining the transition from elastic to plastic response of a material as function of the applied mechanical load. These flow rules are commonly formulated as a yield function, based on the equivalent stress and the yield strength of the material, and its derivatives. In this work, a novel mathematical formulation is developed that allows the efficient use of machine learning algorithms describing the elastic-plastic deformation of a solid under arbitrary mechanical loads and that can replace the standard yield functions with more flexible algorithms. By exploiting basic physical principles of elastic-plastic deformation, the dimensionality of the problem is reduced without loss of generality. The data-oriented approach inherently offers a great flexibility to handle different kinds of material anisotropy without the need for explicitly calculating a large number of model parameters. The applicability of this formulation in finite element analysis is demonstrated, and the results are compared to formulations based on Hill-like anisotropic plasticity as reference model. In future applications, the machine learning algorithm can be trained by hybrid experimental and numerical data, as for example obtained from fundamental micromechanical simulations based on crystal plasticity models. In this way, data-oriented constitutive modeling will also provide a new way to homogenize numerical results in a scale-bridging approach. Full article
(This article belongs to the Special Issue Empowering Materials Processing and Performance from Data and AI)
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