机器学习算法用于预测基于格子结构的聚合物泡的机械刚度
Mohammad Javad Hooshmand1, Chowdhury Sakib-Uz-Zaman1, Mohammad Abu Hasan Khondoker1
1Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada.
Materials (Basel, Switzerland)
|November 25, 2023
概括
机器学习准确地预测了3D打印格子结构的机械刚度. 人工神经网络 (ANN) 显示了最高的准确性,使网格部分的设计能够针对特定应用进行优化.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 聚合物泡具有良好的机械和能量吸收能力,但很难连续生产.
- 可通过增材制造制造的格子结构允许对材料特性进行更大的设计控制.
研究的目的:
- 为了研究格子参数对增材制造的格子部件的机械刚度的影响.
- 为了比较各种机器学习算法在预测格子部分刚性的有效性.
主要方法:
- 设计了360个格子部分,格子类型,细胞尺寸 (X,Y,Z) 和细胞壁厚度各不相同.
- 在一致的负载条件下进行计算分析以记录应变.
- 对比线性回归,多项式回归,决策树,随机森林和人工神经网络 (ANN) 进行预测准确性.
主要成果:
- 所有机器学习算法都显示出较低的预测误差 (MSE,RMSE,MAE).
- 人工神经网络 (ANN) 显示出卓越的性能,相关系数为0.93.
- 通过相对错误分析和实际与预测值图表来验证ANN的准确性.
结论:
- 机器学习,特别是ANN,可以根据设计参数准确预测网格部分的机械刚性.
- 这种预测能力有助于为所需的机械性质优化格子结构的设计.
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