利用机器学习算法预测和优化多乳酸在生物医学使用中的各种特性:综合性审查
J M Chandra Hasa1, P Narayanan2, R Pramanik3
1Department of Aerospace Engineering, Indian Institute of Technology Madras, 600036 Chennai, India.
Biomedical materials (Bristol, England)
|January 9, 2025
概括
机器学习 (ML) 通过增材制造 (AM) 来增强聚乳酸 (PLA) 生物医学设备. 这次审查探讨了ML的ML.
科学领域:
- 聚合物化学和材料科学 聚合物化学和材料科学
- 生物医学工程 生物医学工程
- 化学工程是化学工程的重要组成部分.
背景情况:
- 在生物医学设备开发中,个性化对于患者康复至关重要.
- 聚乳酸 (PLA) 为各种生物医学应用提供生物相容性和生物降解性.
- 增材制造 (AM) 可以通过调整打印参数来优化PLA组件的性能.
研究的目的:
- 审查基于PLA的生物医学设备在AM中的机器学习 (ML) 应用的最新进展.
- 突出ML的潜力,以提高PLA材料的性能和优化制造工艺.
- 解决生物医学工程中ML对PLA影响的综合分析缺口.
主要方法:
- 关于ML和AM集成的最新发展的全面文献综述.
- 分析ML在增强生物医学应用PLA特性中的作用.
- 检查ML对优化基于PLA的设备的AM流程的影响.
主要成果:
- ML显著提高了PLA在AM中的定制性和性能,用于生物医学用途.
- ML集成优化了PLA材料的性能,从而提高了生物相容性和功能.
- ML促进对AM流程的精确控制,确保量身定制的PLA设备特性.
结论:
- 通过AM,ML正在通过AM彻底改变PLA在生物医学工程中的使用.
- 进一步研究ML驱动的PLA的AM将促进个性化医学和生物基聚合物应用.
- 本综述有助于理解生物基聚合物化学和材料科学在生物基聚合物的背景下.
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