[论文解读] Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers for Better Biocomposite Materials

Faris M. AL‐Oqla, Hossam Faris+2 more|arXiv (Cornell University)|Feb 20, 2024

Natural Fiber Reinforced Composites被引用 4

一句话总结

本研究开发了动态遗传编程树模型，利用关键化学和结构特性（纤维素、半纤维素、木质素、含水量和微纤维角）预测天然绿色纤维的抗拉强度、弹性模量和断裂伸长率。模型预测精度高，其中微纤维角对抗拉强度预测的贡献率达44.7%，显著减少了生物复合材料开发中对昂贵实验测试的需求。

ABSTRACT

Advanced modern technology and industrial sustainability theme have contributed implementing composite materials for various industrial applications. Green composites are among the desired alternatives for the green products. However, to properly control the performance of the green composites, predicting their constituents properties are of paramount importance. This work presents an innovative evolving genetic programming tree models for predicting the mechanical properties of natural fibers based upon several inherent chemical and physical properties. Cellulose, hemicellulose, lignin and moisture contents as well as the Microfibrillar angle of various natural fibers were considered to establish the prediction models. A one-hold-out methodology was applied for training/testing phases. Robust models were developed to predict the tensile strength, Young's modulus, and the elongation at break properties of the natural fibers. It was revealed that Microfibrillar angle was dominant and capable of determining the ultimate tensile strength of the natural fibers by 44.7% comparable to other considered properties, while the impact of cellulose content in the model was only 35.6%. This in order would facilitate utilizing artificial intelligence in predicting the overall mechanical properties of natural fibers without experimental efforts and cost to enhance developing better green composite materials for various industrial applications.

研究动机与目标

• 开发基于人工智能的预测模型，用于预测天然绿色纤维的机械性能，以降低实验成本。
• 识别在决定抗拉强度、杨氏模量和断裂伸长率方面最具影响力的化学和结构纤维特性。
• 应用动态遗传编程树模型，建立纤维组成与机械行为之间稳健且可解释的关系。
• 通过实现准确、数据驱动的纤维性能预测，支持可持续复合材料的设计。

提出的方法

• 采用遗传编程（GP）演化树状结构的数学模型，将输入的纤维特性映射到机械输出结果。
• 输入特征包括各种天然纤维的纤维素、半纤维素、木质素、含水量和微纤维角。
• 采用留一法交叉验证策略，将数据划分为训练集和测试集以评估模型性能。
• 通过在测试集上优化决定系数（R²）、平均绝对误差和均方误差的适应度函数，引导模型演化。
• 采用基于置换的分析方法量化特征重要性，评估各输入变量的贡献度。
• 最终模型具备可解释性，提供输入变量与机械输出之间明确的数学表达式。

实验结果

研究问题

• RQ1 哪些纤维特性对天然绿色纤维的抗拉强度影响最为显著？
• RQ2 遗传编程树模型在预测杨氏模量和断裂伸长率等机械性能方面精度如何？
• RQ3 微纤维角与化学组成（如纤维素、木质素）在预测纤维强度方面相比，其相对贡献如何？
• RQ4 动态遗传编程模型能否在预测绿色纤维机械行为时同时实现高精度与可解释性？

主要发现

• 微纤维角对最终抗拉强度预测的贡献率达44.7%，是影响最大的因素。
• 纤维素含量对抗拉强度预测的贡献率为35.6%，表明其具有较强但次于微纤维角的作用。
• 所开发的GP模型实现了高预测精度，可实现无需实验测试的可靠机械性能估算。
• 模型在未见的测试数据上表现出稳健的泛化能力，验证了其在真实生物复合材料设计中的可靠性。
• 所演化树模型的可解释性使人们能够清晰洞察纤维结构与机械响应之间的物理关系。