粉床融合的先进数值建模:从基于物理的模拟到人工智能增强的数字双胞胎
Łukasz Łach1, Dmytro Svyetlichnyy1
1AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, al. Mickiewicza 30, 30-059 Krakow, Poland.
Materials (Basel, Switzerland)
|January 28, 2026
概括
数字双胞胎正在彻底改变粉床融合 (PBF),一种增材制造工艺. 通过整合基于物理的模拟和机器学习,这些先进的模型提高了PBF应用的质量,可靠性和可扩展性.
科学领域:
- 增材制造 (AM) 是一种增材制造.
- 材料科学 材料科学 材料科学
- 计算建模 计算建模
背景情况:
- 粉床融合 (PBF) 是用于高分辨率制造的关键增材制造 (AM) 技术.
- PBF的复杂性需要先进的建模,以确保质量,可靠性和可扩展性.
- 当前的建模方法在捕捉复杂的过程动态方面面临挑战.
研究的目的:
- 对PBF的建模框架的进展进行批判性综合.
- 突出数字双胞胎在PBF中的作用.
- 确定PBF数字双胞胎发展的挑战和机遇.
主要方法:
- 对基于物理的模拟,机器学习和数字双胞胎框架的审查.
- 分析微,中和部分尺度的进展情况.
- 强调混合物理数据驱动的方法和实时传感集成.
主要成果:
- 在PBF的多尺度建模方面取得了重大进展,从池动态到残余应力.
- 混合物理数据驱动方法在捕捉过程结构属性 (PSP) 关系方面越来越重要.
- 数字双胞胎,集成传感,多尺度建模和人工智能,是先进PBF的基础.
结论:
- 数字双胞胎代表了PBF的变革性范式.
- 解决计算成本和数据稀缺等挑战对于行业的采用至关重要.
- 未来的研究应该专注于可扩展,可解释和行业准备的PBF数字双胞胎平台.
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