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机器学习可以预测可持续的基于凝的电支架的形态学,地形学和机械性能
Elisa Roldán1, Neil D Reeves2, Glen Cooper3
1Department of Engineering, Faculty of Science & Engineering, Manchester Metropolitan University, Manchester, M1 5GD, UK. Elisa.Roldan-Ciudad@mmu.ac.uk.
Scientific reports
|September 9, 2024
概括
这项研究使用机器学习来预测绿色溶剂对凝支架的影响. 位置,规模和形状 (GAMLSS) 模型的通用添加模型显示了对脚手架属性的优异预测性能.
科学领域:
- 生物材料工程 生物材料工程
- 可持续的制造业 可持续的制造业
- 计算科学 计算科学
背景情况:
- 电产生纳米微微尺度的纤维性支架用于组织工程.
- 传统溶剂对健康和环境构成风险,阻碍了可持续的工业扩展.
- 开发基于绿色溶剂的电对于环保的脚手架生产至关重要.
研究的目的:
- 为了比较机器学习模型来预测绿色溶剂对凝支架性能的影响.
- 评估蒸水 (dH2O),酸 (HAc) 和二甲基硫氧化物 (DMSO) 对脚手架形态,地形和机械的影响.
- 确定最有效的机器学习方法,用于模拟脚手架制造中的绿色溶剂效应.
主要方法:
- 使用不同度的dH2O,HAc和DMSO制造基于凝的支架.
- 在12种机器学习方法中开发和比较72个模型 (例如GAMLSS,Random Forest,ANN).
- 预测关键的脚手架参数:纤维直径,纤维间的分离,粗度,最终的抗拉强度,Young的模量和断裂时的应变.
主要成果:
- 与传统的回归模型相比,定位,尺度和形状 (GAMLSS) 模型显示出更高的预测准确性 (R2提高6.868%) 和更低的平均绝对百分比误差 (MAPE提高21.16%).
- 酸 (HAc) 显著影响了脚手架的形态和地形.
- 甲基二硫化物 (DMSO) 对脚手架的机械性能产生了更大的影响.
- 将形态性质作为共变量结合起来,改善了对地形和机械模型的预测理解.
结论:
- 机器学习,特别是GAMLSS,为优化绿色溶剂电过程提供了强大的工具.
- 像HAc和DMSO这样的绿色溶剂可以有效地用于为特定应用量身定制凝支架的特性.
- 整合形态数据增强了脚手架地形和机械学的预测能力,为可持续生物材料开发铺平了道路.
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