Rapid-fire Weapon Barrel Fatigue Life Reliability Research And Degradation Analysis

In this paper, a certain single-barrel machine-gun was used as the research project. The researches of this gun’s thermal-elastic coupling problem, fatigue life and reliability analysis under the conditions of high transient, strong load and extreme environments were conducted. The main contents are summarized as follows:The failure mechanism of the barrel was disclosed, and the reasons of coating cracking and spallation was analyzed. Based on the model of interfacial edge fatigue, a model for the gun tube fatigue life was set up.The thermal-elastic coupling FEM model of the machine-gun was established. The synthetical effects of the transient heat pulse and pressure pulse during firing with higher firing rate were calculated and analyzed deeply and the mechanism that the heat pulse and the transient pulse pressure accelerated the barrel fatigue and destruction was consequently disclosed. The analysis has made preparations for the estimation of barrel fatigue life.The fatigue life of the gun tube was estimated by the model proposed previously. It confirmed the correctness of the life prediction model and provided a theoretical prediction method the weapon barrel.The evaluation of the random local stress and local strain was completed by the establishment of polynomials. The applicability of the three improved Miner’s rules were analyzed. The results proved that the statistic Miner’s rule can reflect the random of damage accumulation reasonably. In the end, the mean value and variance of the fatigue life was obtained through the establishment of polynomials.Considered a caliber weapon barrel as the research object, based on the characteristics of weapon barrel, the muzzle velocity of projectile and the bore wear were selected as degradation parameters, and a related test was carried out to obtain the performance degradation data. The life reliability of weapon barrel was assessed by multivariate degradation data, the analysis results gave the predicted reliability variation over time, as well as the connection between the reliability and the correlation degree of different degradation data.