Li Qiang, Sun Jinshan, Xie Xianqi, Dong Qian, Wang Jianguo, Jiang Nan
State Key Laboratory of Precision Blasting Engineering, Jianghan University, Wuhan, Hubei, PR China.
Faculty of land Resources Engineering, Kunming University of Science and Technology, Kunming, Yunnan, PR China.
PLoS One. 2025 Sep 10;20(9):e0330548. doi: 10.1371/journal.pone.0330548. eCollection 2025.
Numerous parameters influence the slotting performance of slotted cartridge, to facilitate rapid, efficient, and accurate predictions of the slitting performance, statistical analysis of PMMA blasting experiments with six different slitted cartridge parameters yielded 12 evaluation indicators. Subsequently, a principal component analysis (PCA) method was introduced to reduce the dimensionality of the data associated with these indicators, and three new comprehensive indicators were extracted for a comprehensive assessment of the slotting performance. The PCA scores ranked the influence of the six slotted cartridge parameters on slotting performance as follows: decoupling coefficient, slotting width, slotting angle, slotting tube thickness, slotting tube material, and charge amount. This ranking serves as a guideline for selecting suitable slotted cartridge parameters. The predictive results demonstrated that the PCA-PNN model performed well across eight different training and testing sample configurations, achieving correct prediction rates of 100%, 100%, 96.43%, 96.43%, 92.86%, 89.29%, 89.29% and 85.71%, respectively. This corresponded to an average accuracy improvement of 12.95% compared to data that were not subjected to PCA dimensionality reduction. Moreover, the PCA-PNN model was validated as a robust and feasible approach for evaluating the slotting performance of slotted cartridge.
许多参数会影响割缝药柱的割缝性能，为了便于快速、高效且准确地预测割缝性能，对具有六个不同割缝药柱参数的聚甲基丙烯酸甲酯爆破实验进行统计分析，得出了12个评价指标。随后，引入主成分分析（PCA）方法来降低与这些指标相关的数据维度，并提取了三个新的综合指标以全面评估割缝性能。主成分分析得分对六个割缝药柱参数对割缝性能的影响进行了如下排序：不耦合系数、割缝宽度、割缝角度、割缝管厚度、割缝管材料和装药量。该排序为选择合适的割缝药柱参数提供了指导。预测结果表明，主成分分析-概率神经网络（PCA-PNN）模型在八种不同的训练和测试样本配置中均表现良好，分别实现了100%、100%、96.43%、96.43%、92.86%、89.29%、89.29%和85.71%的正确预测率。与未进行主成分分析降维的数据相比，这相当于平均准确率提高了12.95%。此外，主成分分析-概率神经网络模型被验证为一种评估割缝药柱割缝性能的稳健且可行的方法。
