CAREER: A Systematic Data-Analytics Approach to the Design of Interface-Rich Materials

职业：用于设计富界面材料的系统数据分析方法

• 批准号: 1552716
• 负责人: Jennifer Carter
• 金额: $ 50万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552716&HistoricalAwards=false
• 关键词: CAREER; Systematic; Data; Analytics; Approach

Interface-rich materials are pervasive in all engineered systems, including turbine discs for energy harvesting, and bonded layered composites in medical imaging equipment. The mechanisms that control performance of interface-rich materials manifest at the mesoscale, a length scale between the nanometer and macroscopic system (millimeter to meters) scales. Therefore, efficient design of these materials to achieve the performance metrics necessary for an engineered system (i.e., thermal resistance for x-ray generation) requires a quantitative understanding of the manufacturing processes and their relationship to the mechanisms at this "in-between" length scale. This Faculty Early Career Development (CAREER) award supports fundamental research needed integrate the theory of mesostructure performance, and statistical-based analysis of "big data." The design paradigm will be applied specifically to a forged nickel-based superalloy, and has the potential for broad impact on the rapid insertion of new materials and manufacturing processes to reduce cost and time-to-market for engineered systems which interface-rich materials.The research goal is to test the hypothesis that statistical data analytics can lead to a versatile design paradigm for the manufacturing of interface-rich materials for particular performance requirements. The predictive capabilities of these data-derived models will be assessed for Inconel-706 a forged Ni-based superalloy, with the performance metrics of high temperature strength and low cycle fatigue life. This alloy is significant for the manufacturing of efficient energy harvesting applications. The data for this research includes manufacturing, mesostructure and performance measures from legacy data obtained over the past 10 years by Alcoa Forging Research Group. The research team will test this hypothesis by cultivating an open, robust, data infrastructure that goes beyond a simple electronic "filing cabinet" and allows for seamless access to the data by analysis tools and allows for the analysis results to also be stored with all the associated metadata. Exploratory data analysis will guide the team in determining what additional datasets are needed to increase the statistical validity of the data-derived models. This research program supports broader efforts of the Material Science community through both the Materials Genome Initiative and Integrated Computational Materials Engineering by producing a robust, open-source data framework, which is generalizable to manufacturing routes of other interface-rich materials.

界面丰富的材料普遍存在于所有工程系统中，包括用于能量收集的涡轮机盘，以及医学成像设备中的粘合层状复合材料。控制富界面材料性能的机制表现在中尺度，即纳米和宏观系统（毫米到米）尺度之间的长度尺度。因此，有效设计这些材料以实现工程系统所需的性能指标（即，用于X射线产生的热阻）需要定量地理解制造过程以及它们与该“中间”长度尺度下的机制的关系。这个教师早期职业发展（CAREER）奖支持基础研究需要整合介观结构性能的理论，并基于“大数据”的分析。“设计范例将专门应用于锻造镍基高温合金，并有可能对新材料的快速插入和制造工艺产生广泛的影响，以降低界面丰富材料的工程系统的成本和上市时间。研究目标是测试统计数据分析可以导致界面制造的通用设计范式的假设，丰富的材料，满足特定的性能要求。这些数据导出的模型的预测能力将被评估为Inconel-706锻造镍基高温合金，高温强度和低周疲劳寿命的性能指标。这种合金对于制造高效的能量收集应用具有重要意义。这项研究的数据包括制造，细观结构和性能指标，这些数据来自Alcoa Forging Research Group在过去10年中获得的遗留数据。研究小组将通过建立一个开放、强大的数据基础设施来验证这一假设，该基础设施不仅限于简单的电子“文件柜”，还允许通过分析工具无缝访问数据，并允许分析结果与所有相关元数据一起存储。探索性数据分析将指导团队确定需要哪些额外的数据集来提高数据衍生模型的统计有效性。该研究计划通过材料基因组计划和集成计算材料工程支持材料科学界更广泛的努力，通过产生一个强大的开源数据框架，该框架可推广到其他界面丰富的材料的制造路线。

项目成果

A Data-Driven Framework to Select a Cost-Efficient Subset of Parameters to Qualify Sourced Materials

• DOI: 10.1007/s40192-022-00266-3
• 发表时间: 2022-07
• 期刊: Integrating Materials and Manufacturing Innovation
• 影响因子: 3.3
• 作者: N. M. Senanayake;Jennifer L. W. Carter;C. Bowman;D. Ellis;J. Stuckner

Characterization of nanoscale precipitates in superalloy 718 using high resolution SEM imaging

• DOI: 10.1016/j.matchar.2018.12.018
• 发表时间: 2019-02-01
• 期刊: MATERIALS CHARACTERIZATION
• 影响因子: 4.7
• 作者: Smith, T. M.;Senanayake, N. M.;Carter, J.
• 通讯作者: Carter, J.