Bi-Directional Prediction Model for Hot Pressing Production Parameters and Quality of High-Performance Bamboo-Based Fiber Composites Based on cHGWOSCA-SVR

被引:0
作者
Ding, Yucheng [1 ]
Zhang, Jiawei [1 ]
Meng, Fanwei [2 ]
Tan, Shaolin [2 ]
Xu, Qinguo [2 ]
Yang, Chunmei [2 ]
Yu, Wenji [3 ]
机构
[1] Northeast Forestry Univ, Coll Comp & Control Engn, Harbin 150040, Peoples R China
[2] Northeast Forestry Univ, Coll Mech & Elect Engn, Harbin 150040, Peoples R China
[3] Chinese Acad Forestry, Beijing 100080, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 15期
关键词
bamboo-based fiber composite; bi-directional prediction model; production parameters; boards quality;
D O I
10.3390/app14156691
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In the hot press process of high-performance bamboo-based fiber composites, there is a highly nonlinear relationship between the production parameters of hot press and the quality parameters of the finished boards. Consequently, it is challenging to accurately predict the quality of the boards based on the given production parameters, and it is equally difficult to preset the production parameters to achieve the desired board quality. The current approach relies on manual experience, which may result in subpar board quality and material waste. To address these issues, this paper proposes a bi-directional prediction model based on cHGWO-SCA-SVR, using the collaboration-based hybrid GWO-SCA optimizer to optimize the relevant parameters of the SVR, and then accurately predicting the production parameters and the quality of the finished boards in both directions. Finally the cHGWO-SCA-SVR prediction model achieves an average R2 of 0.9591 for the forward prediction model and lower MAE and MSE values compared to other models; for the reverse prediction model, it attains an average R2 of 0.9553 and lower MAE and MSE values compared to other models. The results demonstrate the superiority of the cHGWO-SCA-SVR prediction model in comparison with other existing models, proving its significance in guiding the production of high-performance bamboo-based fiber composites by hot compression.
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页数:14
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  • [1] A novel non-woven fabric from bamboo fiber in medical lifestyle products
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    Chombhuphan, Rath
    Rattanaporn, Kasidit
    Srivorradatphisan, Supanicha
    Ruangnarong, Chanakarn
    Khojitmate, Sujira
    [J]. HELIYON, 2024, 10 (09)
  • [2] A collaboration-based hybrid GWO-SCA optimizer for engineering optimization problems
    Duan, Yuchen
    Yu, Xiaobing
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2023, 213
  • [3] Study on the properties of the recombinant bamboo by finite element method
    Fu, Yang
    Fang, Hai
    Dai, Fengyu
    [J]. COMPOSITES PART B-ENGINEERING, 2017, 115 : 151 - 159
  • [4] Bi-directional Prediction of Wood Fiber Production Using the Combination of Improved Particle Swarm Optimization and Support Vector Machine
    Gao, Yunbo
    Hua, Jun
    Chen, Guangwei
    Cai, Liping
    Jia, Na
    Zhu, Liangkuan
    [J]. BIORESOURCES, 2019, 14 (03) : 7229 - 7246
  • [5] Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters
    Garcia-Nieto, Paulino Jose
    Garcia-Gonzalo, Esperanza
    Arbat, Gerard
    Duran-Ros, Miquel
    Pujol, Toni
    Puig-Bargues, Jaume
    [J]. BIOSYSTEMS ENGINEERING, 2024, 243 : 42 - 56
  • [6] Prediction of compressive strength of rice husk ash concrete: A comparison of different metaheuristic algorithms for optimizing support vector regression
    Huang, Yifan
    Lei, Yu
    Luo, Xuedong
    Fu, Chao
    [J]. CASE STUDIES IN CONSTRUCTION MATERIALS, 2023, 18
  • [7] Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms
    Li, Enming
    Yang, Fenghao
    Ren, Meiheng
    Zhang, Xiliang
    Zhou, Jian
    Khandelwal, Manoj
    [J]. JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING, 2021, 13 (06) : 1380 - 1397
  • [8] Prediction of coal spontaneous combustion temperature based on improved grey wolf optimizer algorithm and support vector regression
    Li, Shuang
    Xu, Kun
    Xue, Guangzhe
    Liu, Jiao
    Xu, Zhengquan
    [J]. FUEL, 2022, 324
  • [9] Intelligent prediction model of a polymer fracture grouting effect based on a genetic algorithm-optimized back propagation neural network
    Liang, Jiasen
    Du, Xueming
    Fang, Hongyuan
    Li, Bin
    Wang, Niannian
    Di, Danyang
    Xue, Binghan
    Zhai, Kejie
    Wang, Shanyong
    [J]. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY, 2024, 148
  • [10] High-performance, low-cost, chemical-free, and reusable bamboo drinking straw: An all-natural substitute for plastic straws
    Luan, Yu
    Huang, Bin
    Chen, Lin
    Wang, Xianke
    Ma, Yifan
    Yin, Mingliang
    Song, Yifei
    Liu, Huanrong
    Ma, Xinxin
    Zhang, Xiubiao
    Sun, Fengbo
    Fang, Changhua
    Fei, Benhua
    [J]. INDUSTRIAL CROPS AND PRODUCTS, 2023, 200