基于凝固前沿演变特征的熔铸装药成型工艺参数智能优化

doi:10.12382/bgxb.2023.0797

摘要/Abstract

摘要：

熔铸装药成型过程中药浆凝固前沿的轮廓特征与成型后药柱内的缩孔缩松等缺陷具有显著相关性。为改善熔铸装药成型质量,提出凝固前沿轮廓特征度量指标,探究该指标与药柱缩孔缩松体积和最大孔隙率等缺陷的相关性。通过熔铸装药温度场仿真数据集训练装药关键工艺参数与药浆二维瞬态温度场的B样条神经网络模型,进而建立工艺参数与指标参数的代理模型,再基于遗传算法以极大化凝固前沿轮廓特征度量指标为目标,对装药关键工艺参数进行优化。研究结果表明:工艺参数组由初始参数P⁰=[100, 85, 0.25, 0.25, 90, 5, 0.6]^T优化至最佳参数P^*=[91.725, 94.961, 0.498, 0.151, 100, 6, 0.595]^T后,缩孔缩松体积和最大孔隙率等成型质量参数由19.832mm³和4.71%降至3.129mm³和0.66%,实现了熔铸装药成型质量的快速预测和优化;新提出的方法为熔铸装药的工艺优化提供了新思路和新策略,为高性能装药的发展贡献了解决方案,对提高生产效率、降低成本以及确保成型质量一致性具有借鉴意义。

关键词: 熔铸装药, 凝固前沿, B样条神经网络, 遗传算法, 工艺参数优化

Abstract:

Melt-cast explosive processes present a significant correlation among the profile features of solidification front during the molding process and the shrinkage cavity and porosity defects within the grain after molding. To improve the molding quality of melt-cast explosive, an indictor representing the solidification front profile is defined, and the correlations among the indictor and the defects, such as shrinkage cavity/porosity volume and maximum porosity, of the grain, are examined. A surrogate model is developed based on a B-spline neural network model that describes the relationship between the key process parameters and the two-dimensional transient temperature field of melt-cast explosive, and the neural network is trained using a simulated dataset. The key process parameters of melt-cast explosive are then optimized using a genetic algorithm with the aim of maximizing the indicator of solidification front. The results show that the quality parameters of shrinkage cavity/porosity volume and maximum porosity of the grain decrease from 19.832mm³ and 4.71% to 3.129mm³ and 0.66%, respectively, as the process parameters are optimized from P⁰=[100, 85, 0.25, 0.25, 90, 5, 0.6 ]^T to P^*=[91.725, 94.961, 0.498, 0.151, 100, 6, 0.595 ]^T, and a fast prediction and optimization for the molding quality of melt-cast explosive are achieved. The proposed method can provide new ideas and strategies for the process optimization of melt-casting explosives and contribute the solutions for the development of high-performance melt-casting explosives, which can be referred to improving the production efficiency, reducing the costs, and ensuring the consistency of molding quality.

Key words: melt-cast explosive, solidification front, B-spline neural network, genetic algorithm, process parameter optimization

中图分类号:

TJ55

夏焕雄, 李康, 高丰, 刘检华, 敖晓辉. 基于凝固前沿演变特征的熔铸装药成型工艺参数智能优化[J]. 兵工学报, 2024, 45(9): 2936-2950.

XIA Huanxiong, LI Kang, GAO Feng, LIU Jianhua, AO Xiaohui. Intelligent Optimization for Forming Quality of Melt-cast Explosives Based on the Evolution Characteristics of Solidification Front[J]. Acta Armamentarii, 2024, 45(9): 2936-2950.

图/表 19

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材料	温度	导热系数λ/ (W·m^-1K^-1)	密度ρ/ (kg·m^-3)	比热容c_p/ (J·kg^-1K^-1)	固相线温度 T_sol/℃	液相线温度 T_liq/℃	与外界的换热系数 h₁/(W·m^-2·K^-1)	与炸药的换热系数 h₂/(W·m^-2·K^-1)	环境温度/℃
DNAN	20℃	0.72	1858	1130
基炸药	65℃	0.70	1810	1320	85	90	20
	110℃	0.64	1700	1450					25
钢		50	7940	450			300	200
铝合金		176	2700	900			200	100

材料	温度	导热系数λ/ (W·m^-1K^-1)	密度ρ/ (kg·m^-3)	比热容c_p/ (J·kg^-1K^-1)	固相线温度 T_sol/℃	液相线温度 T_liq/℃	与外界的换热系数 h₁/(W·m^-2·K^-1)	与炸药的换热系数 h₂/(W·m^-2·K^-1)	环境温度/℃
DNAN	20℃	0.72	1858	1130
基炸药	65℃	0.70	1810	1320	85	90	20
	110℃	0.64	1700	1450					25
钢		50	7940	450			300	200
铝合金		176	2700	900			200	100

仿真实验组	度量指标 σ_min	最大孔隙率 P_max/%	缩孔缩松体积 V_s/mm³	仿真实验组	度量指标 σ_min	最大孔隙率 P_max/%	缩孔缩松体积 V_s/mm³
1	16.53991	3.29	9.863	12	11.98614	4.98	20.372
2	17.10425	2.67	7.919	13	15.83645	3.55	11.825
3	17.93704	2.31	7.125	14	16.56021	3.25	10.885
4	18.01297	2.81	8.863	15	11.76438	4.71	19.832
5	12.96612	4.15	17.275	16	17.64397	2.61	8.790
6	14.95398	4.18	13.338	17	12.43408	4.15	19.126
7	13.56158	4.27	18.709	18	14.35084	4.02	13.777
8	15.40288	3.77	12.048	19	13.52198	4.14	19.271
9	16.21673	3.3	10.507	20	15.30599	3.81	12.528
10	16.34581	3.32	10.767	21	16.15484	3.08	10.280
11	16.87580	2.97	9.293

仿真实验组	度量指标 σ_min	最大孔隙率 P_max/%	缩孔缩松体积 V_s/mm³	仿真实验组	度量指标 σ_min	最大孔隙率 P_max/%	缩孔缩松体积 V_s/mm³
1	16.53991	3.29	9.863	12	11.98614	4.98	20.372
2	17.10425	2.67	7.919	13	15.83645	3.55	11.825
3	17.93704	2.31	7.125	14	16.56021	3.25	10.885
4	18.01297	2.81	8.863	15	11.76438	4.71	19.832
5	12.96612	4.15	17.275	16	17.64397	2.61	8.790
6	14.95398	4.18	13.338	17	12.43408	4.15	19.126
7	13.56158	4.27	18.709	18	14.35084	4.02	13.777
8	15.40288	3.77	12.048	19	13.52198	4.14	19.271
9	16.21673	3.3	10.507	20	15.30599	3.81	12.528
10	16.34581	3.32	10.767	21	16.15484	3.08	10.280
11	16.87580	2.97	9.293