EAGER/Cybermanufacturing: Cyber-Enabled Additive Manufacturing of Advanced Materials

增材制造（AM）是一种从数字模型打印三维固体物体的过程，它可以产生几乎任何几何复杂性，而对成本的影响很小。然而，目前大多数增材制造技术尚未开始发挥其全部潜力。这个探索性研究（EAGER）项目将探索推进增材制造技术的可行性，重点关注与传统制造工艺不同的能力，例如制造具有通常在自然界中无法观察到的特性的材料的能力。这些能力将带来突破性的制造技术，例如目前无法制造出更坚固、更轻的结构非晶金属，以及修复比原始状态更坚固的零件。因此，该项目将为制造先进材料提供一种变革性的方法。制造材料的强度、硬度和疲劳性能也将使极轻的结构设计成为可能，从而彻底改变市场上的许多产品。因此，这项研究的结果将有利于美国制造业。该项目将展示一种新型网络增材制造（CyberAM）系统的可行性，以改变材料制造能力。使用CyberAM系统，结构非晶金属将使用金属AM系统通过良好控制的高冷却过程制造。该项目还将推进网络制造的知识，包括1)一种有效地将过程模型信息提取到统一的CyberAM表示中的方法，用于实时过程监控和控制；2)简化模型计算和数据生成以支持网络制造的策略；3)利用传感器反馈不断改进过程模型和数据库的学习和自适应体系结构；4)运用所学知识进行CyberAM演示，制作新型材料。如果成功，研究人员将能够将已开发的知识扩展到互联网规模，以支持多个AM站制造以前无法制造的先进材料。

Additive manufacturing (AM), a process of printing a 3-dimensional solid object from a digital model, makes it possible to produce virtually any geometric complexity with very little impact on cost. However, most of the current AM technologies have not yet begun to achieve their full potential. This EArly-concept Grant for Exploratory Research (EAGER) project will explore the feasibility of advancing AM technologies, focusing on capacities that differ from conventional manufacturing processes, such as the ability to create materials with properties not generally observed in nature. These capabilities will lead to breakthrough manufacturing technologies, such as structural amorphous metals with much stronger and lighter products that cannot be currently made, and repairing parts stronger than their original condition. Thus the project will provide a transformative method to fabricate advanced materials. The strength, hardness, and fatigue properties of fabricated materials will also enable extremely lightweight structure design, thus revolutionize many products in the market. Therefore, results from this research will benefit the U.S. manufacturing industry.This project will demonstrate the feasibility of a novel Cyber-enabled Additive Manufacturing (CyberAM) system to transform material fabrication capability. Using the CyberAM system, structural amorphous metals will be fabricated using a metal AM system through well-controlled high cooling process. This project will also advance the knowledge of cybermanufacturing, including 1) a methodology to effectively extract process model information into a unified CyberAM representation for real time process monitoring and control; 2) a strategy to simplify model computation and data generation to support cyber manufacturing; 3) a learning and adaptation architecture to continuously improve process model and database using sensor feedback; and 4) CyberAM demonstration with the learned knowledge to make novel materials. If successful, the researchers will be able to extend the developed knowledge to internet scale to support multiple AM stations to fabricate advanced materials that cannot be made before.