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Updated: Feb 14, 2026

Using Q Suture to Enhance Resistance to Gap Formation and Tensile Strength of Repaired Flexor Tendons
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人工智能驱动的热指纹:通过施莱伦成像预测PLA拉伸强度

Mason Corey1, Kyle Weber1, Babak Eslami1

  • 1Mechanical Engineering Department, Widener University, Chester, PA 19013, USA.

Polymers
|February 13, 2026
PubMed
概括

本研究介绍了热指纹,一种低成本,非破坏性的方法,使用面向背景的施莱伦 (BOS) 成像和机器学习来实时预测结沉积建模 (FDM) 打印中的拉伸强度. 该框架使增材制造的现场质量保证成为可能.

关键词:
通过3D打印打印3D打印.在FDM的FDM.机器学习是机器学习.材料的表征材料的表征.施莱伦的成像技术

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