High entropy alloy design for dissolvable tools using machine learning

使用机器学习的可溶解工具的高熵合金设计

• 批准号: 570911-2021
• 负责人: Henein, HaniH
• 金额: $ 3.64万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746821
• 关键词: entropy; alloy; design; dissolvable; tools

Dissolvable Alloys is an emerging area of special metallic alloys that dissolve under specific operating conditions. These alloys have found wide spread use in oil and gas, biomedical, and space applications. This proposal aims to use Machine Learning (ML) approaches to define new alloy systems for application of dissolvable tools. These tools must provide a unique combination of strength and desirable corrosion rates. To date, aluminum alloys and magnesium alloys have been used for such applications. High Entropy alloys (HEAs) and near stoichiometric HEAs (all referred to here as HEAs) are a new generation of materials that show promise to provide such a combination of properties. Given the enormous possibilities in tunning the structures and compositions of HEAs, this proposal aims to develop a machine learning model that will combine the results in the public domain and patents with the desired application goals for these dissolvable alloys. Such knowledge will be combined with thermodynamic database tools to identify new alloy compositions for HEAs. Experimental work and ML modelling will be interactively carried out. An experimental validation will then be undertaken using a hybrid investment casting additive manufacturing approach developed at the University of Alberta. Mechanical and corrosion properties will subsequently be evaluated to validate the ML model. With sufficient data acquired, ML models for predicting the mechanical and corrosion properties of HEAs will be set up and tested. The deliverables of the project will be new materials for dissolvable materials, machine learning based tools and methods for directed materials space research based on specific material requirements.

可溶解的合金是在特定工作条件下溶解的特殊金属合金的新兴区域。这些合金发现在石油和天然气，生物医学和空间应用中广泛使用。 该建议旨在使用机器学习（ML）方法来定义新的合金系统以应用可溶解的工具。 这些工具必须提供强度和理想的腐蚀速率的独特组合。 迄今为止，铝合金和镁合金已用于此类应用。 高熵合金（HEAS）和附近的化学计量heas（这里都称为heas）是新一代的材料，有望提供这种特性组合。鉴于调整HEAS的结构和组成的巨大可能性，该建议旨在开发一种机器学习模型，将结果与公共领域的结果和专利结合在一起，并与这些可溶解的合金的所需应用程序目标相结合。 这些知识将与热力学数据库工具结合使用，以识别HEAS的新合金组成。 实验工作和ML建模将进行交互。然后，将使用在艾伯塔大学开发的混合投资铸造添加剂制造方法进行实验验证。 随后将评估机械和腐蚀特性以验证ML模型。 有了足够的数据，将建立和测试用于预测HEAS的机械和腐蚀特性的ML模型。该项目的可交付成果将是可溶解材料，基于机器学习的工具以及基于特定材料要求的定向材料空间研究的方法。