火炮身管内膛损伤评估方法及其研究现状

罗茜; 景小飞; 杨九州; 李立; 陈汉宾; 詹青青; 吴厦; 周少兰; 宋凯强; 丛大龙; 李忠盛

doi:10.16490/j.cnki.issn.1001-3660.2026.09.020

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表面技术 ›› 2026, Vol. 55 ›› Issue (9) : 244-256. DOI: 10.16490/j.cnki.issn.1001-3660.2026.09.020

装备表面工程

火炮身管内膛损伤评估方法及其研究现状

罗茜¹, 景小飞², 杨九州¹, 李立¹, 陈汉宾¹, 詹青青¹, 吴厦¹, 周少兰¹, 宋凯强¹, 丛大龙^1,*, 李忠盛^1,*

作者信息 +

Research Progress on Damage Assessment Methods of Gun Barrel Bore

LUO Xi¹, JING Xiaofei², YANG Jiuzhou¹, LI li¹, CHEN Hanbin¹, ZHAN Qingqing¹, WU Xia¹, ZHOU Shaolan¹, SONG Kaiqiang¹, CONG Dalong^1,*, LI Zhongsheng^1,*

Author information +

文章历史 +

摘要

身管内膛损伤高效可靠评估是破解严酷工况下身管性能退化、支撑损伤机理研究与延寿技术发展的基础,对提升火炮综合作战效能具有重要意义。本文综述了热化学反应主导的烧蚀、机械作用驱动的磨损及循环载荷诱发的疲劳三类典型损伤的形成机制及量化理论模型,系统梳理了基于试验检测、仿真模拟和智能数据分析的主流身管内膛损伤评估方法,深入剖析了其适用性与核心局限性。其中,试验检测法通过尺寸测量、图像检测、物理量监测等途径获取实弹实测数据,是评估内膛损伤宏微观表现最直接和可靠的方法,但极端工况下精度有待提升;仿真模拟依托有限元技术复现复杂工况,可捕捉身疲劳裂纹萌生扩展等内膛损伤演化信息,为机理解析提供支撑,但多物理场耦合建模及参数标定难度大;智能数据分析主要借助神经网络、支持向量机等算法,适配小样本场景下损伤预测与寿命评估,但存在异口径场景泛化能力弱、模型可解释性不足等问题。此外,本文指出了当前评估技术面临的极端环境检测适配性差、多因素耦合损伤效应刻画不精确等核心挑战,未来需聚焦新型身管内膛损伤检测技术研发、深化多物理场耦合仿真与人工智能融合,构建机理-数据双驱动的评估方法。该综述可为损伤评估技术创新与工程应用提供系统性参考,推动火炮身管内膛损伤评估技术向精准化、精细化、工程化方向发展。

Abstract

The efficient and reliable assessment of gun barrel bore damage serves as the foundation for research on barrel damage mechanisms and advancement of life extension technologies under severe thermal-chemical-mechanical coupled operating conditions. As the core component of artillery responsible for imparting initial velocity and firing direction to projectiles, the gun barrel directly dominates critical tactical and technical indicators such as firing accuracy and range. During continuous firing, the gun bore is subject to extreme environments including scouring by high-temperature (up to 3 000 K) and high-pressure propellant gas, as well as friction from high-speed projectile driving bands. These harsh conditions readily induce multiple coupled damage forms, leading to performance degradation and service life attenuation of the gun barrel, which severely restricts the operational efficiency and sustainability of artillery weapons. Consequently, developing efficient and accurate bore damage assessment techniques has become a pivotal research topic in the field of artillery, holding significant engineering application value and military strategic significance for reliable barrel life prediction, performance optimization, and operational effectiveness assurance.
The work aims to conduct a comprehensive overview on the formation mechanisms and quantitative theoretical models of typical damage forms in gun barrel bore, such as ablation, wear, and fatigue cracking. Ablation damage originates from intense thermochemical reactions induced by high-temperature propellant gas, where corrosive gases react with the barrel inner wall to form loose "white layers" (composed of iron oxides and sulfides) that easily detach under subsequent projectile movement and gas scouring. Wear damage, dominated by mechanical effects, is categorized into adhesive wear (micro-welding and shearing of metal protrusions), abrasive wear (scouring by solid particles), and corrosive wear (synergy of chemical corrosion and mechanical erosion) which collectively exacerbate surface roughness and material loss. Fatigue damage stems from cyclic loading during firing, leading to alternating stress/strain that initiates and propagates cracks at weak locations such as micro-defects or stress concentration areas, with significant impacts on surface-strengthened gun barrels (e.g., chrome-plated ones) due to coating spallation.
The current mainstream assessment methods for barrel bore damage are systematically sorted out. The experimental testing method is employed to obtain live fire testing data through size measurement, image detection and physical quantity monitoring, which is the most direct and reliable method for evaluating the bore damage. The evaluation method based on simulation relies on finite element technology to reproduce complex operating conditions, capture the evolution information of bore damage and reveal the damage mechanism. The evaluation method based on intelligent data analysis is used to predict the bore damage and life of gun barrel through small sample data through artificial intelligence algorithms, such as neural networks and support vector machines. Moreover, according to the complex and harsh service environment in gun bore, the challenges faced by the damage evolution in terms of detection accuracy and multi-factor coupling damage effects are pointed out. At the same time, it is proposed that efforts can be made in the future to promote the development of artillery barrel damage assessment technology by developing new barrel damage assessment technologies and deepening the integration of multi physics field coupling simulation and artificial intelligence technology. This research holds significant engineering application value for reliable life prediction and artillery performance improvement.

导出引用

罗茜, 景小飞, 杨九州, 李立, 陈汉宾, 詹青青, 吴厦, 周少兰, 宋凯强, 丛大龙, 李忠盛. 火炮身管内膛损伤评估方法及其研究现状[J]. 表面技术. 2026, 55(9): 244-256

LUO Xi, JING Xiaofei, YANG Jiuzhou, LI li, CHEN Hanbin, ZHAN Qingqing, WU Xia, ZHOU Shaolan, SONG Kaiqiang, CONG Dalong, LI Zhongsheng. Research Progress on Damage Assessment Methods of Gun Barrel Bore[J]. Surface Technology. 2026, 55(9): 244-256

中图分类号： TJ303

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