用于预测绿色纤维力学性能以优化生物复合材料的进化遗传规划树模型
Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers for Better Biocomposite Materials
AI总结：
本研究提出进化遗传规划树模型，基于天然纤维的化学物理特性预测其力学性能，证实微纤丝角对拉伸强度的影响占比最高，可助力低成本开发优质绿色复合材料。
摘要：
先进现代技术与工业可持续发展主题推动了复合材料在各类工业应用中的落地。绿色复合材料是绿色产品的理想替代方案之一，但要合理调控绿色复合材料的性能，预测其组分的性能至关重要。本研究提出一种创新的进化遗传规划树模型，基于多种固有化学与物理特性预测天然纤维的力学性能。研究选取多种天然纤维的纤维素、半纤维素、木质素、水分含量以及微纤丝角来构建预测模型，采用留一法开展训练与测试阶段的工作，最终开发出可预测天然纤维拉伸强度、杨氏模量和断裂伸长率性能的稳健模型。研究结果显示，与其他所考虑的特性相比，微纤丝角占主导地位，对天然纤维极限拉伸强度的决定作用达44.7%，而纤维素含量在模型中的影响占比仅为35.6%。该研究将有助于借助人工智能预测天然纤维的整体力学性能，无需耗费实验精力与成本，从而推动开发更优质的绿色复合材料，以应用于各类工业场景。
英文摘要：
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.
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