Slotting blasting model experiment and PCA-PNN evaluation model of influencing factors of slitting effect

Qiang Li, Jinshan Sun, Xianqi Xie, Qian Dong, Jianguo Wang and Nan Jiang

PLOS ONE, 2025, vol. 20, issue 9, 1-23

Abstract: 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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0330548

DOI: 10.1371/journal.pone.0330548

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