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Materials informatics approach to understand aluminum alloys.

ABSTRACT: The relations between the mechanical properties, heat treatment, and compositions of elements in aluminum alloys are extracted by a materials informatics technique. In our strategy, a machine learning model is first trained by a prepared database to predict the properties of materials. The dependence of the predicted properties on explanatory variables, that is, the type of heat treatment and element composition, is searched using a Markov chain Monte Carlo method. From the dependencies, a factor to obtain the desired properties is investigated. Using targets of 5000, 6000, and 7000 series aluminum alloys, we extracted relations that are difficult to find via simple correlation analysis. Our method is also used to design an experimental plan to optimize the materials properties while promoting the understanding of target materials.

SUBMITTER: Tamura R

PROVIDER: S-EPMC7476514 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Materials informatics approach to understand aluminum alloys.

Tamura Ryo R Watanabe Makoto M Mamiya Hiroaki H Washio Kota K Yano Masao M Danno Katsunori K Kato Akira A Shoji Tetsuya T

Science and technology of advanced materials 20200729 1

The relations between the mechanical properties, heat treatment, and compositions of elements in aluminum alloys are extracted by a materials informatics technique. In our strategy, a machine learning model is first trained by a prepared database to predict the properties of materials. The dependence of the predicted properties on explanatory variables, that is, the type of heat treatment and element composition, is searched using a Markov chain Monte Carlo method. From the dependencies, a facto ...[more]

PMID: 32939178

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Project description:Complex structures can now be manufactured easily utilizing AM technologies to meet the pre-requisite objectives such as reduced part numbers, greater functionality, and lightweight, among others. Polymers, metals, and ceramics are the few materials that can be used in AM technology, but metallic materials (Magnesium and Aluminum) are attracting more attention from the research and industrial point of view. Understanding the role processing parameters of laser-based additive manufacturing is critical to maximize the usage of material in forming the product geometry. LPBF (Laser powder-based fusion) method is regarded as a potent and effective additive manufacturing technique for creating intricate 3D forms/parts with high levels of precision and reproducibility together with acceptable metallurgical characteristics. While dealing with LBPF, some degree of porosity is acceptable because it is unavoidable; hot ripping and cracking must be avoided, though. The necessary manufacturing of pre-alloyed powder and ductility remains to be the primary concern while dealing with a laser-based additive manufacturing approach. The presence of the Al-Si eutectic phase in AlSi10Mg and AlSi12 alloy attributing to excellent castability and low shrinkage, attaining the most attention in the laser-based approach. Related studies with these alloys along with precipitation hardening and heat treatment processing were discussed. The Pure Mg, Mg-Al alloy, Mg-RE alloy, and Mg-Zn alloy along with the mechanical characteristics, electrochemical durability, and biocompatibility of Mg-based material have been elaborated in the work-study. The review article also summarizes the processing parameters of the additive manufacturing powder-based approach relating to different Mg-based alloys. For future aspects, the optimization of processing parameters, composition of the alloy, and quality of powder material used will significantly improve the ductility of additively manufactured Mg alloy by the LPBF approach. Other than that, the recycling of Mg-alloy powder hasn't been investigated yet. Meanwhile, the post-processing approach, including a homogeneous coating on the porous scaffolds, will mark the suitability in terms of future advancements in Mg and Al-based alloys.

Dataset Information

Materials informatics approach to understand aluminum alloys.

Publications

Materials informatics approach to understand aluminum alloys.

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