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.

可溶性合金是一个新兴领域的特殊金属合金，在特定的操作条件下溶解。这些合金在石油和天然气、生物医学和空间应用中得到了广泛的应用。 该提案旨在使用机器学习（ML）方法来定义用于可溶解工具的新合金系统。 这些工具必须提供强度和理想腐蚀速率的独特组合。 迄今为止，铝合金和镁合金已经用于这种应用。 高熵合金（HEAs）和近化学计量比HEAs（在此均称为HEAs）是新一代材料，其显示出提供这种特性组合的前景。鉴于调整HEAs结构和成分的巨大可能性，该提案旨在开发一种机器学习模型，将公共领域和专利中的结果与这些可溶性合金的预期应用目标相结合。 这些知识将与热力学数据库工具相结合，以确定新的合金成分的HEAs。 实验工作和ML建模将以交互方式进行。然后将使用阿尔伯塔大学开发的混合熔模铸造增材制造方法进行实验验证。 随后将评估机械和腐蚀性能，以验证ML模型。 在获得足够数据的情况下，将建立和测试用于预测HEAs的机械和腐蚀性能的ML模型。该项目的交付成果将是可溶解材料的新材料，基于机器学习的工具和基于特定材料要求的定向材料空间研究方法。