可以创造更好的人工智能陶瓷元件?

Amanda Krause, Ph.D.

有一切从墙面砖和人工髋关节玻璃器皿,巡航导弹和热瓦为NASA的航天飞机使用了陶瓷。应该把它吃也不奇怪,因为好处,包括低导电性,高的熔点和化学品优异的抗长的列表中进行理想地适用于所有这些应用的陶瓷材料。

而在先进陶瓷的创新已经取得进展,在过去几十年中,许多传统的缺点仍然存在。例如,重量更轻,耐高低温,今天制作的高科技陶瓷理想的飞机涡轮仍来自于一项艰巨而昂贵的制造工艺的价格。此外,随着陶瓷固有的强度和硬度来的热量,蒸汽的极端负载条件和力下这同样是这些飞机经常涡轮机运行状态下的全面敏感性要失败的。

Preventing that failure is at the heart of the latest research for UF Department of Materials Science & Engineering (MSE) Assistant Professor Amanda Krause, Ph.D., and her team consisting of UF MSE colleague Michael Tonks, Ph.D., Joel Harley, Ph.D. from UF’s Department of Electrical and Computer Engineering and Michael Kesler, Ph.D. from Oak Ridge National Laboratory. Backed by a $1.26 million award from the U.S. Department of Energy’s (DOE) Basic Energy Sciences (BES) program, Dr. Krause feels that artificial intelligence (AI), specifically machine learning, may hold the key to predicting why one particular grain expands abnormally while the ones next to it quickly succumb.

“能源部感兴趣的是在寻找材料在极端环境中,如温度或磁场和电场的行为。我们两个项目涉及高温,磁场,博士说:”。克劳斯。

理想情况下,微观粒子组成的任何材料的结构是均匀的尺寸相对整个给定的样本 - 这通常是在常温下的情况。当问题涉及到材料:如氧化铝,加热至极端温度或之下的过度应力的一些其它手段放置。时所发生的,这些微观颗粒开始扩大规模。尽管经济增长预期ESTA,什么不能预测谷物是,如果有的话,将享受这导致他们以更快的速度吸收他们的邻居特殊的优势。异常晶粒生长ESTA会导致设备的结构弱点或不稳定的地区。

Michele Manuel, Ph.D., Chair of UF’s Department of Materials Science & Engineering, is excited about the potential breakthroughs that harnessing AI’s powerful data analytics capabilities could uncover. “The term game-changing comes to mind, because that is exactly what this research is capable of doing,” Dr. Manuel said. “Unlocking the secrets of abnormal grain growth, in any material, will lead to incredible advances in materials science and engineering we’ve only dreamed about up to this point.”

博士。人同意克劳斯。

“最终,如果学习机可以识别并预测异常晶粒长大,从那里下一个步骤是检查最终行为不仅粮食生产过程中而且在设备的实现,”博士。克劳斯说。 “在这一点上,我们可以设计再更新,更好的成分比我们从今天的大多数看到的远远优于强度和断裂韧性性能先进陶瓷材料。”