Tensile Performance Mechanism for Bamboo Fiber-Reinforced, Palm Oil-Based Resin Bio-Composites Using Finite Element Simulation and Machine Learning

Abstract

Plant fiber-reinforced composites have the advantages of environmental friendliness, sustainability, and high specific strength and modulus. They are widely used as low-carbon emission materials in automobiles, construction, and buildings. The prediction of their mechanical performance is critical for material optimal design and application. However, the variation in the physical structure of plant fibers, the randomness of meso-structures, and the multiple material parameters of composites limit the optimal design of the composite mechanical properties. Based on tensile experiments on bamboo fiber-reinforced, palm oil-based resin composites, finite element simulations were carried out and the effect of material parameters on the tensile performances of the composites was investigated. In addition, machine learning methods were used to predict the tensile properties of the composites. The numerical results showed that the resin type, contact interface, fiber volume fraction, and multi-factor coupling significantly influenced the tensile performance of the composites. The results of the machine learning analysis showed that the gradient boosting decision tree method had the best prediction performance for the tensile strength of the composites (R2 was 0.786) based on numerical simulation data from a small sample size. Furthermore, the machine learning analysis demonstrated that the resin performance and fiber volume fraction were critical parameters for the tensile strength of composites. This study provides an insightful understanding and effective route for investigating the tensile performance of complex bio-composites.

Mechanical and thermal properties of recycled polypropylene bamboo fibre-reinforced composites

“…Figure 4 shows the tensile test of the r-PP/bamboo composite was carried out with a pulling speed of 5mm/minute. The average tensile strength and elongation are shown in Figure 4(a) and the modulus from five specimens of each treatment is shown in Figure 4 In Figure 4 it can be seen that the tensile strength and strain increase with increasing bamboo fraction; as also indicated by Wang et al [21]. Most of the specimens did not experience fracture while reaching the ultimate tensile point strength but experienced an elongation and reduction of the thickness.…”

Machine Learning-Enhanced Biomass Pressure Sensor with Embedded Wrinkle Structures Created by Surface Buckling

“…The XGboost algorithm was used to identify voice signals and analyze the important feature of voice signals for signal identification (Note S1, Table S3, and Table S4). ,, …”

“…Information analyses of long vibration signals by the machine learning method have been reported; meanwhile, it is still a challenge to detect and recognize short and similar vibration signals, which is critical for some applications such as human-machine interactions through short voice vibrations. Besides, the accuracy of machine learning analysis depends on the quantity and quality of data samples, machine learning methods, parameters of models, and so on . To reduce the time required for training machine learning models before customers use flexible devices, it is important to develop high-precision machine learning methods based on a small sample data set.…”