Machine learning guided alloy design and thermomechanical process optimisation for high performance automotive aluminium alloys

机器学习引导高性能汽车铝合金的合金设计和热机械工艺优化

• 批准号: 2431042
• 金额: --
• 依托单位: Brunel University London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2431042
• 关键词: Machine; learning; guided; alloy; design

Traditional intuition-based and trial-and-error experimentation used in the development of new alloys and/or optimisation of processing conditions are very time-consuming and costly approaches. Materials modelling and process simulations guided by physics-based principles have helped to reduce the time and cost to some extend in the search for optimal solutions in terms of alloy composition and processing parameters for a given metallic material that meets the technical requirements of specific engineering applications. However, the increasing complexity of alloy chemistry and materials processing methodologies can be challenging to current multiphysics-based computational modelling due to the lack of quantitative phenomenological relationships between the composition/processing and properties of alloys. The power of machine learning (ML) is that it can operate on high-dimensional data to make sense of the complexity of multi-element alloy compositions and multi-step processing. The data-driven models generated by machine learning approach have been used extensively in the materials engineering domain to successfully find such complex correlations leading to effective materials design(1-6).

用于开发新合金和/或优化加工条件的传统的基于直觉和试错的实验是非常耗时且昂贵的方法。以物理原理为指导的材料建模和工艺模拟有助于在一定程度上减少为满足特定工程应用的技术要求的给定金属材料的合金成分和加工参数寻找最佳解决方案的时间和成本。然而，由于合金的成分/加工和性能之间缺乏定量的唯象关系，合金化学和材料加工方法日益复杂，可能对当前基于多物理场的计算模型提出挑战。机器学习 (ML) 的强大之处在于它可以对高维数据进行操作，以了解多元素合金成分和多步骤处理的复杂性。由机器学习方法生成的数据驱动模型已广泛应用于材料工程领域，以成功找到此类复杂的相关性，从而实现有效的材料设计(1-6)。