Invited review: Machine learning for materials developments in metals additive manufacturing

Johnson, Nathan Scott; Vulimiri, Praveen S.; To, Albert C.; Zhang, X.; Brice, Craig; Kappes, Branden B.; Stebner, Aaron P.

doi:10.1016/j.addma.2020.101641

Cited by 92 publications

(61 citation statements)

References 219 publications

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“…Indeed, such synergy can be beneficial at three different levels. Firstly, ML has established itself as a robust tool for quantitative phase analysis [ 77 , 78 , 79 ], thus facilitating the collection of the large amounts of data needed for the careful validation of enhanced thermo-metallurgical FE models. Second, Bayesian approaches combined with ML have already been proved powerful for parameters identification, and this could be applied to the set of presently unknown material parameters within the TTB concept [ 79 ].…”

Section: Discussionmentioning

confidence: 99%

“…Firstly, ML has established itself as a robust tool for quantitative phase analysis [ 77 , 78 , 79 ], thus facilitating the collection of the large amounts of data needed for the careful validation of enhanced thermo-metallurgical FE models. Second, Bayesian approaches combined with ML have already been proved powerful for parameters identification, and this could be applied to the set of presently unknown material parameters within the TTB concept [ 79 ]. In addition, it has recently been established that a physics-informed feature engineering enables ML with limited data, thus allowing us to model the effects of metallurgical processing variations on the temperature range of a martensitic transformation [ 80 ].…”

Section: Discussionmentioning

confidence: 99%

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A New Concept for Modeling Phase Transformations in Ti6Al4V Alloy Manufactured by Directed Energy Deposition

et al. 2021

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The microstructure directly influences the subsequent mechanical properties of materials. In the manufactured parts, the elaboration processes set the microstructure features such as phase types or the characteristics of defects and grains. In this light, this article aims to understand the evolution of the microstructure during the directed energy deposition (DED) manufacturing process of Ti6Al4V alloy. It sets out a new concept of time-phase transformation-block (TTB). This innovative segmentation of the temperature history in different blocks allows us to correlate the thermal histories computed by a 3D finite element (FE) thermal model and the final microstructure of a multilayered Ti6Al4V alloy obtained from the DED process. As a first step, a review of the state of the art on mechanisms that trigger solid-phase transformations of Ti6Al4V alloy is carried out. This shows the inadequacy of the current kinetic models to predict microstructure evolution during DED as multiple values are reported for transformation start temperatures. Secondly, a 3D finite element (FE) thermal simulation is developed and its results are validated against a Ti6Al4V part representative of repair technique using a DED process. The building strategy promotes the heat accumulation and the part exhibits heterogeneity of hardness and of the nature and the number of phases. Within the generated thermal field history, three points of interest (POI) representative of different microstructures are selected. An in-depth analysis of the thermal curves enables distinguishing solid-phase transformations according to their diffusive or displacive mechanisms. Coupled with the state of the art, this analysis highlights both the variable character of the critical points of transformations, and the different phase transformation mechanisms activated depending on the temperature value and on the heating or cooling rate. The validation of this approach is achieved by means of a thorough qualitative description of the evolution of the microstructure at each of the POI during DED process. The new TTB concept is thus shown to provide a flowchart basis to predict the final microstructure based on FE temperature fields.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A New Concept for Modeling Phase Transformations in Ti6Al4V Alloy Manufactured by Directed Energy Deposition

et al. 2021

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“…3). There are several widely apply and established algorithms used in machine learning methods [1,3,10]. The first group can include: weighted neighbourhood clustering that examples might be decision trees, random forest or k-Nearest neighbour, are applied.…”

Section: Machine Learning Methods Of Analysis Classification and Modmentioning

confidence: 99%

Efficiency and Accuracy of Simulated Microstructure Images Generated by Machine Learning

Gądek-Moszczak¹,

Filipowska²

2021

14th WCCM-ECCOMAS Congress

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Computer image analysis is a well-known method in material science, mechanical engineering and other branches of science and engineering. Application of the machine learning method in image processing delivers promising results, especially in images with a high level of noise and low contrast. Employing automatic classification, predictive models, simulation models deliver a huge benefit in research. The former model of science-based mainly on experiments slowly passing away. It is still an important part of research but, using advanced computer methods save time and allow significantly reduces the cost of studies. It begins new branch of material science and new research challenges, interdisciplinary filed of materials informatics, incorporating scientists exploring, mathematics, informatics and material science.

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“…In addition, the mathematical algorithms forming the backbone of machine learning (a branch of artificial intelligence) and the associated technology are growing at a fast pace since about five years. These methods have been applied to DFT for high-throughput simulations for relatively simple systems (e.g., small cubic minerals), providing huge amount of data [ 175 , 176 , 177 ]. It is highly probable that, in the future, it will be possible to extend machine learning approaches to such complex systems as hard tissues, finding new insights and correlations that could be of extreme help for several fundamental and applied fields, such as biology, medicine, and materials science.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Hydroxylapatite and Related Minerals in Bone and Dental Tissues: Structural, Spectroscopic and Mechanical Properties from a Computational Perspective

2021

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Hard tissues (e.g., bone, enamel, dentin) in vertebrates perform various and different functions, from sustaining the body to haematopoiesis. Such complex and hierarchal tissue is actually a material composite whose static and dynamic properties are controlled by the subtle physical and chemical interplay between its components, collagen (main organic part) and hydroxylapatite-like mineral. The knowledge needed to fully understand the properties of bony and dental tissues and to develop specific applicative biomaterials (e.g., fillers, prosthetics, scaffolds, implants, etc.) resides mostly at the atomic scale. Among the different methods to obtains such detailed information, atomistic computer simulations (in silico) have proven to be both corroborative and predictive tools in this subject. The authors have intensively worked on quantum mechanical simulations of bioapatite and the present work reports a detailed review addressed to the crystal-chemical, physical, spectroscopic, mechanical, and surface properties of the mineral phase of bone and dental tissues. The reviewed studies were conducted at different length and time scales, trying to understand the features of hydroxylapatite and biological apatite models alone and/or in interaction with simplified collagen-like models. The reported review shows the capability of the computational approach in dealing with complex biological physicochemical systems, providing accurate results that increase the overall knowledge of hard tissue science.

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Invited review: Machine learning for materials developments in metals additive manufacturing

Cited by 92 publications

References 219 publications

A New Concept for Modeling Phase Transformations in Ti6Al4V Alloy Manufactured by Directed Energy Deposition

A New Concept for Modeling Phase Transformations in Ti6Al4V Alloy Manufactured by Directed Energy Deposition

Efficiency and Accuracy of Simulated Microstructure Images Generated by Machine Learning

Hydroxylapatite and Related Minerals in Bone and Dental Tissues: Structural, Spectroscopic and Mechanical Properties from a Computational Perspective

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