Machine learning predictions on fracture toughness of multiscale bio-nano-composites

材料科学 复合材料 断裂韧性 韧性 夏比冲击试验 断裂力学 扫描电子显微镜 断裂(地质) 复合数
作者
Vahid Daghigh,Thomas E. Lacy,Hamid Daghigh,Grace X. Gu,Kourosh T. Baghaei,M.F. Horstemeyer,Charles U. Pittman
出处
期刊:Journal of Reinforced Plastics and Composites [SAGE]
卷期号:39 (15-16): 587-598 被引量:38
标识
DOI:10.1177/0731684420915984
摘要

Tailorability is an important advantage of composites. Incorporating new bio-reinforcements into composites can contribute to using agricultural wastes and creating tougher and more reliable materials. Nevertheless, the huge number of possible natural material combinations works against finding optimal composite designs. Here, machine learning was employed to effectively predict fracture toughness properties of multiscale bio-nano-composites. Charpy impact tests were conducted on composites with various combinations of two new bio fillers, pistachio shell powders, and fractal date seed particles, as well as nano-clays and short latania fibers, all which reinforce a poly(propylene)/ethylene–propylene–diene-monomer matrix. The measured energy absorptions obtained were used to calculate strain energy release rates as a fracture toughness parameter using linear elastic fracture mechanics and finite element analysis approaches. Despite the limited number of training data obtained from these impact tests and finite element analysis, the machine learning results were accurate for prediction and optimal design. This study applied the decision tree regressor and adaptive boosting regressor machine learning methods in contrast to the K-nearest neighbor regressor machine learning approach used in our previous study for heat deflection temperature predictions. Scanning electron microscopy, optical microscopy, and transmission electron microscopy were used to study the nano-clay dispersion and impact fracture morphology.
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