Machine Learning-Driven Grayscale Digital Light Processing for Mechanically Robust 3D-Printed Gradient Materials (Adv. Mater. 42/2025)

IF 26.8 1区材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Advanced Materials Pub Date : 2025-10-23 DOI:10.1002/adma.70839

Jisoo Nam, Boxin Chen, Miso Kim

引用次数: 0

Abstract

Machine Learning-Driven Grayscale Digital Light Processing

In their Research Article (DOI: 10.1002/adma.202504075), Jisoo Nam, Boxin Chen, and Miso Kim combine machine learning-driven optimization with grayscale digital light processing to create 3D-printed gradient materials with tailored mechanical properties. Pixel-level light control enables seamless stiffness modulation, improving stress distribution, toughness, and fatigue life. The cover image illustrates the translation of digital design data into architected gradients for durable, high-performance impact absorbers and protective structures.

Abstract Image

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机械鲁棒3d打印梯度材料的机器学习驱动灰度数字光处理（Adv. Mater. 42/2025）

机器学习驱动的灰度数字光处理在他们的研究文章(DOI: 10.1002/adma。202504075), Jisoo Nam， Boxin Chen和Miso Kim将机器学习驱动优化与灰度数字光处理相结合，创建具有定制机械性能的3d打印梯度材料。像素级光控制实现无缝刚度调制，改善应力分布，韧性和疲劳寿命。封面图片说明了将数字设计数据转化为耐用、高性能冲击减震器和保护结构的建筑梯度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advanced Materials 工程技术-材料科学：综合

CiteScore

43.00

自引率

4.10%

发文量

2182

审稿时长

2 months

期刊介绍： Advanced Materials, one of the world's most prestigious journals and the foundation of the Advanced portfolio, is the home of choice for best-in-class materials science for more than 30 years. Following this fast-growing and interdisciplinary field, we are considering and publishing the most important discoveries on any and all materials from materials scientists, chemists, physicists, engineers as well as health and life scientists and bringing you the latest results and trends in modern materials-related research every week.