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相关实验视频

Updated: Jul 5, 2025

Design of an Open-Source, Low-Cost Bioink and Food Melt Extrusion 3D Printer
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Design of an Open-Source, Low-Cost Bioink and Food Melt Extrusion 3D Printer

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开发了一种机器学习,使药品滴式增材制造打印机的整合配方和工艺设计框架成为可能.

Varun Sundarkumar1, Zoltan K Nagy1, Gintaras V Reklaitis1

  • 1Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, USA.

AIChE journal. American Institute of Chemical Engineers
|January 15, 2024
PubMed
概括
此摘要是机器生成的。

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本研究介绍了一种制剂和工艺设计框架,用于使用点击技术进行药品3D打印. 它可以自动预测印刷条件,以实现个性化药物制造.

科学领域:

  • 制药制造业 制药制造业 制药制造业
  • 工业创新 工业创新 工业创新
  • 增材制造 增材制造 增材制造

背景情况:

  • 制药行业需要工业4.0集成来实现先进制造.
  • 人工智能和3D打印等技术是自动化和个性化药物生产的关键.

研究的目的:

  • 开发一种配方和工艺设计 (FPD) 框架,用于制药点滴 (DoD) 3D打印.
  • 为了自动预测配方特性和打印条件,以确保药品质量的一致性.

主要方法:

  • 构建一个机器学习模型,从输入条件 (前问题) 预测国防部的打印结果.
  • 解决和验证反向问题:预测所需DoD打印结果的输入条件.

主要成果:

  • 开发了一个功能性的FPD框架.
  • 该框架成功模拟并预测了国防部的打印参数.

结论:

  • FPD框架增强了制药3D打印中的自动化.
  • 这种方法促进了具有特定属性的药物产品的个性化生产.
关键词:
3D打印,3D打印,机器学习药品的配方和工艺设计.工业4.0 工业4.0 工业4.0 工业4.0 工业4.0 是什么?药学4.0 制药公司添加剂制造 添加剂制造 添加剂制造人工神经网络的人工神经网络制药制造 制药制造 制药制造 制药制造制药产品的过程设计.

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