Intelligent Optimization for Forming Quality of Melt-cast Explosives Based on the Evolution Characteristics of Solidification Front

doi:10.12382/bgxb.2023.0797

Abstract

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

CLC Number:

TJ55

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.

Figures/Tables 19

References 30

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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