CAREER: Surface Engineering by Predictive Laser Deposition of Multi-Principal Element Alloys

职业：通过多主元合金的预测激光沉积进行表面工程

• 批准号: 1944040
• 负责人: Ganesh Balasubramanian
• 金额: $ 50.94万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944040&HistoricalAwards=false
• 关键词: CAREER; Surface; Engineering; Predictive; Laser

This Faculty Early Career Development Program (CAREER) award supports a transformative, experimentally-validated predictive framework to manufacture multi-principal element alloys (MPEAs). These alloys represent a new class of materials that, unlike conventional alloys such as steels, generally consist of five or more principal elements in significant proportions. Promising structural properties, such as superior mechanical strength and hardness, encourage their use as coatings for additively engineered surfaces. The processing of these materials presents some challenges, however, including uneven mixing and microstructure changes during rapid cooling that contribute to formation of cracks when these materials are deposited as coatings. This research project will build an understanding of the correlation of atomistic properties to system-scale processing parameters through the synergistic use of computational predictions, quantification of uncertainties in the processing conditions and material properties, and experimental characterization. The outcomes of the project will advance the manufacturability of these alloys and their surface coatings, which will have significant impact on many technological areas including propulsion, machinery, transportation, and medical devices. The predictive processing paradigm developed through this project will be widely applicable to an array of materials systems, and can bolster additive manufacturing processes with optimization capabilities. The tightly integrated educational and outreach activities are targeted to encourage students from underrepresented minorities to pursue opportunities in STEM fields, and simultaneously contribute towards gender equality and economic opportunities for impoverished communities. The objective of this CAREER project is to generate new knowledge on how the diffusion of multiple principal elements in an alloy melt under rapid cooling affects the microstructure and properties of their laser deposited clads. To realize this objective, an integrated computational framework will be established that (1) marries together structure and property predictions from molecular dynamics simulations of the alloy melt with processing conditions, (2) provides recommendations for optimizing the manufacturing parameters for producing clads of desired composition and quality, and (3) imparts robustness to the correlations by electron microscopy and X-ray spectroscopy characterizations, and uncertainty quantification of the high-dimensional parameter space of compositions, impurities, and manufacturing environment variables. The direct and multiscale correlation of the simulation predictions to the system scale processing parameters, will facilitate the mapping of the parameters to targeted criteria space via a Pareto front. The interrelationship between the processing conditions (system scale) and the alloy melt dynamics (atomic scale) will enable intelligent parameter selection for the laser cladding and aid in creating surface coatings that are homogenous in composition and crack resistant.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个教师早期职业发展计划（CAREER）奖支持一个变革性的，实验验证的预测框架，以制造多主元素合金（MPEAs）。这些合金代表了一类新的材料，与传统的合金如钢不同，通常由五种或更多种主要元素组成。有前途的结构特性，如上级机械强度和硬度，鼓励它们用作增材工程表面的涂层。然而，这些材料的加工提出了一些挑战，包括在快速冷却期间的不均匀混合和微观结构变化，这有助于在这些材料作为涂层沉积时形成裂纹。该研究项目将通过协同使用计算预测，量化加工条件和材料特性的不确定性以及实验表征，建立对原子特性与系统规模加工参数相关性的理解。该项目的成果将提高这些合金及其表面涂层的可制造性，这将对许多技术领域产生重大影响，包括推进，机械，运输和医疗设备。通过该项目开发的预测处理范式将广泛适用于一系列材料系统，并可以通过优化功能支持增材制造过程。紧密结合的教育和外联活动旨在鼓励代表性不足的少数民族学生在STEM领域寻求机会，同时为贫困社区的性别平等和经济机会做出贡献。这个CAREER项目的目标是产生关于快速冷却下合金熔体中多种主要元素的扩散如何影响其激光沉积覆层的微观结构和性能的新知识。为了实现这一目标，将建立一个集成的计算框架，该框架（1）将来自合金熔体的分子动力学模拟的结构和性能预测与加工条件结合在一起，（2）提供用于优化制造参数的建议，以生产具有所需组成和质量的包层，和（3）通过电子显微镜和X射线光谱表征以及组分，杂质，制造环境变量。模拟预测与系统规模处理参数的直接和多尺度相关性将有助于通过帕累托前沿将参数映射到目标准则空间。加工条件（系统规模）和合金熔体动力学（原子规模）之间的相互关系将使激光熔覆的参数选择变得智能化，并有助于创建成分均匀且抗裂的表面涂层。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Process Parameter Predictive Framework for Laser Cladding of Multi-principal Element Alloys

多主元合金激光熔覆工艺参数预测框架

• DOI: 10.1016/j.addlet.2022.100045
• 发表时间: 2022
• 期刊: Additive Manufacturing Letters
• 作者: Sreeramagiri, Praveen;Balasubramanian, Ganesh
• 通讯作者: Balasubramanian, Ganesh

Effect of Cooling Rate on the Phase Formation of AlCoCrFeNi High-Entropy Alloy

• DOI: 10.1007/s11669-021-00918-5
• 发表时间: 2021-08
• 期刊: Journal of Phase Equilibria and Diffusion
• 影响因子: 1.4
• 作者: Praveen Sreeramagiri;A. Roy;G. Balasubramanian

Machine learning strategies for high-entropy alloys

• DOI: 10.1063/5.0030367
• 发表时间: 2020-12
• 期刊: Journal of Applied Physics
• 影响因子: 3.2
• 作者: J. Rickman;Ganesh Balasubramanian;Christopher J. Marvel;Helen M. Chan;M. Burton

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

使用生成对抗网络模型快速发现高硬度多主元素合金

• DOI: 10.1016/j.actamat.2023.119177
• 发表时间: 2023
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: Roy, Ankit;Hussain, Aqmar;Sharma, Prince;Balasubramanian, Ganesh;Taufique, M.F.N.;Devanathan, Ram;Singh, Prashant;Johnson, Duane D.
• 通讯作者: Johnson, Duane D.

Effect of oxidation on the thermal expansion of a refractory multicomponent alloy

• DOI: 10.1080/09500839.2021.1881641
• 发表时间: 2021-04
• 期刊: Philosophical Magazine Letters
• 影响因子: 1.2
• 作者: Eric Osei-Agyemang;G. Balasubramanian