在聚合物材料中注射成型符合常矩机制的计算和实验优化
Tran Minh The Uyen1, Hai Nguyen Le Dang1, Van-Thuc Nguyen1
1Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City 71307, Vietnam.
Polymers
|September 27, 2025
概括
这项研究优化了使用计算,人工神经网络 (ANN) 和实验方法的聚合物兼容恒定扭矩机制 (CTM). 圆形设计显示出优越的强度,ANN和实验结果与这些先进的聚合物CTM密切匹配.
科学领域:
- 机械工程 机械工程
- 材料科学 材料科学 材料科学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 符合常数扭矩机制 (CTM) 对于需要一致的旋转阻力的应用至关重要.
- 聚合物材料的注塑成型为CTM提供了一个可扩展的制造途径.
- 优化CTM性能需要了解对扭转行为的几何影响.
研究的目的:
- 通过注射成型制造的聚合物CTM的计算和实验优化.
- 研究几何变化对CTM扭力强度的影响.
- 通过实验数据验证人工神经网络 (ANN) 模型的预测准确性.
主要方法:
- 利用计算机辅助工程 (CAE) 来进行CTM几何学的数值模拟.
- 进行实验验证,以量化CTM的机械性能.
- 开发并使用人工神经网络 (ANN) 模型进行性能预测.
主要成果:
- 几何配置显著影响CTM扭矩行为;圆形截面表现出优越的强度.
- ANN模型表现出高预测准确度,与实验结果密切一致 (与模型4相似97%).
- CAE模拟预测了0.142 N·m的23-44度范围,而实验和ANN显示了0.164 N·m的20-46度范围更广.
结论:
- 整合CAE,ANN和实验技术对于优化基于聚合物的CTM至关重要.
- 这些差异凸显了对精细材料建模和制造工艺的需求.
- 优化的聚合物CTM显示了精密工程,生物医学设备和软机器人的前景.
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