数据驱动的可打印模拟水凝用于精确的直接墨水写作,基于风学性质
Eun Hui Jeong1, Jiho Choi2, Han Bi Park2
1Department of Chemical and Biological Engineering, Sookmyung Women's university, Seoul, 04310, Republic of Korea.
概括
这项研究将水凝类风湿学与使用机器学习的3D打印成功联系起来. 了解这些特性,可以更好地设计用于软机器人和电子设备的水凝油墨.
科学领域:
- 材料科学 材料科学 材料科学
- 机器人工程 机器人工程 机器人工程
- 聚合物化学 聚合物化学
背景情况:
- 由于其机械性能和可持续性,水凝越来越多地用于软机器人和电子产品.
- 水凝的三维 (3D) 打印是一种关键的制造方法,但打印能力尚不清楚.
- 对于成功的水凝3D打印而言,风病学行为至关重要.
研究的目的:
- 量化研究水凝类风湿学与3D打印能力之间的关系.
- 使用机器学习开发水凝打印能力的预测模型.
- 确定影响软机器人和电子应用的打印能力的关键质参数.
主要方法:
- 编制150个3D打印水凝的数据库,其中包括风湿学数据.
- 使用了非线性风湿学指标,包括大振幅振荡剪切 (LAOS).
- 采用机器学习 (随机森林回归) 来基于风湿学数据建模可打印性.
主要成果:
- 开发了一种具有10%误差率的水凝打印能力的预测模型.
- 确定水平打印能力取决于挤出后的恢复和放松.
- 确定垂直打印能力在高应变率挤出过程中受粘性反应的控制.
结论:
- 建立了水凝类风湿学和3D打印能力之间的定量联系.
- 这些发现促进了水凝油墨的可持续设计,用于先进制造.
- 通过优化的3D打印工艺,实现了软机器人和电子结构的精确制造.
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