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生物油墨配方的数据驱动优化用于基于挤出的生物打印:一种预测建模方法

Rokeya Sarah1, Riley Rohauer2, Kory Schimmelpfennig3

  • 1Department of Sustainable Product Design and Architecture, Keene State College, 229 Main Street, Keene, NH 03435.

Journal of manufacturing science and engineering
|October 20, 2025
PubMed
概括

这项研究开发了用于组织工程的ALGEC生物墨水的预测模型. 这些模型优化生物墨水组成,以提高可打印性和再生医学应用中的结构完整性.

关键词:
在3D生物打印中使用3D生物打印在CAD/CAM/CAE中使用.添加剂制造 添加剂制造 添加剂制造先进的材料和加工和加工.生物医学制造业 生物医学制造业这是一种水凝.机器学习是机器学习.多重回归的多重回归方法多项式适合的多项式打印参数的参数快速原型和固体自由形式制造.

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Methods of Medium Optimization01:28

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Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...