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使用机器学习方法建模香泡的材料特性.
Halime Ergün1, Mehmet Emin Ergün2
1Seydisehir Ahmet Cengiz Faculty of Engineering, Necmettin Erbakan University, Konya 42360, Turkey.
Polymers
|March 28, 2024
概括
这项研究探讨了新型泡材料的香草和纤维素纤维,展示了香草的香草.
科学领域:
- 材料科学与工程 材料科学与工程
- 生物聚合物的应用
- 可持续发展的材料开发
背景情况:
- 赞坦是一种天然的生物聚合物,广泛应用于制药,化品和食品行业.
- 它作为绝缘和包装的泡材料的应用存在有限的研究.
- 人工神经网络 (ANN) 建模尚未应用于基于香甘的泡.
研究的目的:
- 为了研究使用香甘和纤维素纤维的新型泡材料的生产.
- 评估这些生物基泡的物理和机械性能.
- 使用各种机器学习技术,包括ANN,建模泡特性.
主要方法:
- 泡材料是在酸介质中使用不同比例的香草和纤维素纤维生产的.
- 物理和机械特性 (密度,压缩模量,模量) 经过实验确定.
- 五种机器学习模型 (MLR,SVM,ANN,LS,GRNN) 用于数据分析和预测.
主要成果:
- 香草显著影响了泡的特性,远远超过纤维素.
- 泡密度从49.42到172.2kg/m3不等.
- 压缩模块在235.25-1257.52 KPa之间,曲模块在1939.76-12,736.39 KPa之间.
- 机器学习模型准确地预测了实验结果,提高了过程效率.
- 通用回归神经网络 (GRNN) 模型实现了R2值>0.97,用于预测关键属性.
结论:
- 赞坦是一种有前途的生物聚合物,可用于开发用于绝缘和包装的可持续泡材料.
- 机器学习,特别是GRNN,有效地模拟和预测这些新型生物泡的特性.
- 这种方法可以有效地获取定量数据,减少实验成本和时间.
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