Temperature Field Prediction of Melt-cast Explosives Based on a B-spline Neural Network

Lei TAO; Jianhua LIU; Huanxiong XIA; Xiaohui AO; Feng GAO

doi:10.12382/bgxb.2022.0086

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Temperature Field Prediction of Melt-cast Explosives Based on a B-spline Neural Network

更新时间：2025-08-18

- Temperature Field Prediction of Melt-cast Explosives Based on a B-spline Neural Network
- Acta Armamentarii Vol. 44, Issue 5, Pages: 1339-1349(2023)
- 作者机构：
  
  1. 北京理工大学机械与车辆学院, 北京 100081
  2. 北京理工大学唐山研究院, 河北唐山 063015
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2022.0086
  CLC：
- Received：15 February 2022，
  
  Published Online：19 July 2023，
  
  Published：31 May 2023
- 稿件说明：
移动端阅览
Lei TAO, Jianhua LIU, Huanxiong XIA, et al. Temperature Field Prediction of Melt-cast Explosives Based on a B-spline Neural Network[J]. Acta Armamentarii, 2023, 44(5): 1339-1349.
DOI：

Lei TAO, Jianhua LIU, Huanxiong XIA, et al. Temperature Field Prediction of Melt-cast Explosives Based on a B-spline Neural Network[J]. Acta Armamentarii, 2023, 44(5): 1339-1349. DOI： 10.12382/bgxb.2022.0086.

摘要

熔铸装药过程中模具内部温度场分布及其变化规律对装药质量具有重要影响。建立基于B样条神经网络的水/油浴熔铸装药工艺瞬态温度场预测模型

通过数值仿真的正交试验

获得不同工艺条件下熔铸装药温度场演变的数据样本;利用B样条神经网络对数据样本进行训练

得到水/油浴工艺的温控参数与药柱内部温度场之间的关系模型

实现温度场及其凝固前沿演变的快速准确预测。所得成果为熔铸装药的温控参数优化和在线控制提供了高效预测方法

为解决熔铸装药智能化发展中的物理场预测问题提供了方法的借鉴。

Abstract

The temperature field distribution and evolution inside the mold play a crucial role in determining the casting quality of melt-cast explosive processes. A fast prediction model is developed based on a B-spline neural network for the transient temperature field in a melt-cast explosive process with a water/oil bath. The model is created by first obtaining temperature evolution data samples under different processing conditions through orthogonal numerical experiments. The B-spline neural network is then trained on these data samples to establish a prediction model that represents the relationship between temperature-control parameters and the temperature field inside the grain. This model enables rapid and accurate prediction of the temperature field and solidification front

providing an efficient prediction method for parameter optimization and online control of melt-cast explosive processes. This study serves as a valuable reference for predicting other physical fields in the intelligent development of similar processes in the future.

关键词

Keywords

references

田勇 . 炸药熔铸成型过程监测评价及数值模拟研究 [D ] . 北京 : 中国科学院研究生院 , 2009 .

TIAN Y . Process monitoring/evaluation and numerical simulation during casting explosive solidification [D ] . Beijing : Graduate University of Chinese Academy of Sciences , 2009 . (in Chinese)

谢志毅 , 王冬磊 , 刘建波 , 等 . 炸药熔铸过程数值模拟研究 [J ] . 太赫兹科学与电子信息学报 , 2014 , 12 ( 4 ): 617 - 621 .

XIE Z Y , WANG D L , LIU J B , et al. Numerical simulation of melt-cast explosive solidification process [J ] . Journal of Terahertz Science and Electronic Information Technology , 2014 , 12 ( 4 ): 617 - 621 . (in Chinese)

焦云多 , 曾晓华 , 陈洋 , 等 . 大当量DNAN基熔铸炸药装药质量控制方法 [J ] . 兵工自动化 , 2019 , 38 ( 8 ): 23 - 26 .

JIAO Y D , ZENG X H , CHEN Y , et al. Method of charge quality control for large equivalent DNAN-base melt cast explosive [J ] . Ordnance Industry Automation , 2019 , 38 ( 8 ): 23 - 26 . (in Chinese)

蒙君煚 , 周霖 , 金大勇 , 等 . 成型工艺对2,4-二硝基苯甲醚基熔铸炸药装药质量的影响 [J ] . 兵工学报 , 2018 , 39 ( 9 ): 1719 - 1726 . DOI: 10.3969/j.issn.1000-1093.2018.09.007 http://doi.org/10.3969/j.issn.1000-1093.2018.09.007 为了提高2,4-二硝基苯甲醚（DNAN）基熔铸炸药的装药质量，采用压力浇铸与真空浇铸成型工艺，研究其对DNAN基熔铸炸药温度场、缩孔疏松、相对密度及抗拉强度的影响规律。结果表明：压力浇铸使DNAN基熔铸炸药凝固时间缩短，药柱内部缩孔疏松及气孔减少；当成型压力达到0.8 MPa时，DNAN/奥克托今（HMX）炸药相对密度和抗拉强度分别提高了6.4%、9.9%，药柱无裂纹；DNAN/黑索今（RDX）炸药的相对密度提高了2.7%，但抗拉强度降低了40.8%，同时药柱存在裂纹。真空浇铸对DNAN基熔铸炸药凝固过程温度场无影响，使药柱内部缩孔疏松及气孔减少；当真空度达到0.08 MPa时，DNAN/RDX炸药的相对密度及抗拉强度分别提高了2.0%、14.3%，药柱无裂纹。因此，为了获得高质量的熔铸炸药，DNAN/HMX炸药可采用压力浇铸；DNAN/RDX炸药可采用真空浇铸。

MENG J J , ZHOU L , JIN D Y , et al. Effect of forming process on casting quality of 2,4-dinitroanisole-based casting explosive [J ] . Acta Armamentarii , 2018 ,39( 9 ):1719-1726.[5 ] JI C, LIN C S.The solidification process of melt casting explosives in shell [J ] . Propellants,Explosives,Pyrotechnics , 1998, 23 : 137 - 141 . (in Chinese)

JI C , LIN C S , RAPPAZ M . The solidification process of melt casting explosives in shell [J ] . Propellants,Explosives,Pyrotechnics , 1998 , 23 : 137 - 141 . DOI: 10.1002/(ISSN)1521-4087 http://doi.org/10.1002/(ISSN)1521-4087 http://doi.wiley.com/10.1002/%28ISSN%291521-4087 http://doi.wiley.com/10.1002/%28ISSN%291521-4087

PEQUET C , GREMAUD M , RAPPAZ M . Modeling of microporosity,macroporosity and pipe-shrinkage formation during the solidification of alloys using a mushy-zone refinement method: applications to aluminum alloys [J ] . Metallurgical and Materials Transactions A , 2002 , 33 ( 7 ): 2095 - 2106 . DOI: 10.1007/s11661-002-0041-5 http://doi.org/10.1007/s11661-002-0041-5 https://link.springer.com/10.1007/s11661-002-0041-5 https://link.springer.com/10.1007/s11661-002-0041-5

郭朋林 , 罗观 , 习彦 , 等 . TNT基熔铸炸药冷却过程温度场变化规律研究 [J ] . 含能材料 , 2010 , 18 ( 1 ): 93 - 96 .

GUO P L , LUO G , XI Y , et al. Study on temperature field variation during cooling process of TNT-based melt-cast explosive [J ] . Chinese Journal of Energetic Materials , 2010 , 18 ( 1 ): 93 - 96 . (in Chinese)

马松 , 袁俊明 , 刘玉存 , 等 . DNAN炸药凝固过程的实验与数值模拟 [J ] . 含能材料 , 2014 , 22 ( 2 ): 240 - 244 .

MA S , YUAN J M , LIU Y C , et al. Experiment and numerical simulation of DNAN solidification process [J ] . Chinese Journal of Energetic Materials , 2014 , 22 ( 2 ): 240 - 244 . (in Chinese)

胡菲 , 刘玉存 , 袁俊明 , 等 . 炸药熔铸装药成型质量监测技术研究进展 [J ] . 兵器装备工程学报 , 2021 , 42 ( 9 ): 1 - 6 .

HU F , LIU Y C , YUAN J M , et al. Review on monitoring technology for quality of casting explosive solidification [J ] . Journal of Sichuan Ordnance , 2021 , 42 ( 9 ): 1 - 6 . (in Chinese)

马松 , 袁俊明 , 刘玉存 , 等 . 水浴和自然条件下熔铸炸药凝固过程的数值计算 [J ] . 火炸药学报 , 2013 , 36 ( 3 ): 54 - 58 . 以3-丁烯-1-醇为原料，经环氧化、硝化两步反应合成出3，4-环氧基丁醇硝酸酯(NEO），并用红外光谱、核磁共振氢谱及元素分析对产品的结构进行表征。通过实验确定了最佳硝化条件：硝化剂与3，4-环氧基丁醇（EBO）的摩尔比为1.5∶1.0，反应温度为-10℃，滴加完毕后待温度开始下降时立即终止反应，硝化反应的收率为65.1%，产品纯度为98.2%。

MA S , YUAN J M , LIU Y C , et al. Numerical calculation of solidification process of melt-cast explosive in water bath and natural conditions [J ] . Chinese Journal of Explosives and Propellants , 2013 , 36 ( 3 ): 54 - 58 . (in Chinese)

刘威 , 曹红松 , 张虎成 , 等 . 分层浇铸工艺对熔铸药柱缩孔的影响研究 [J ] . 机电技术 , 2015 ( 6 ): 122 - 125 .

LIU W , CAO H S , ZHANG H C , et al. Study of the influence of layer casting process on shrinkage cavity of melt-cast explosive [J ] . Mechanical and Electrical Technology , 2015 ( 6 ): 122 - 125 . (in Chinese)

MENG J J , ZHOU L , ZHANG X R . Effect of pressure of the casting vessel on the solidification characteristics of a DNAN/RDX melt-cast explosive [J ] . Journal of Energetic Materials , 2017 , 35 ( 4 ): 385 - 396 .

岳晓媛 , 张会锁 , 韩雪莲 , 等 . 大长径比熔铸装药热芯棒凝固工艺优化仿真 [J ] . 高压物理学报 , 2021 , 35 ( 1 ): 1 - 8 .

YUE X Y , ZHANG H S , HAN X L , et al. Optimization simulation of hot mandrel solidification process of large length-diameter ratio melt-cast explosive [J ] . Chinese Journal of High Pressure Physics , 2021 , 35 ( 1 ): 1 - 8 . (in Chinese)

张明明 , 张斌宏 , 万大奎 , 等 . 熔铸装药凝固过程温度场变化分析 [J ] . 弹药先进制造技术 , 2021 , 40 ( 6 ): 93 - 96 .

ZHANG M M , ZHANG B H , WANG D K , et al. Study on temperature field changes during casting explosive solidification process [J ] . Ordnance Industry Automation , 2021 , 40 ( 6 ): 93 - 96 . (in Chinese)

MENG L B , MCWILLIAMS B , JAROSINSKI W , et al. Machine learning in additive manufacturing:a review [J ] . The Minerals,Metals and Materials Society , 2020 , 72 ( 6 ): 2363 - 2377 .

WANG C , TAN X P , TOR S B , et al. Machine learning in additive manufacturing:state-of-the-art and perspectives [J ] . Additive Manufacturing , 2020 , 36 : 101538 . DOI: 10.1016/j.addma.2020.101538 http://doi.org/10.1016/j.addma.2020.101538 https://linkinghub.elsevier.com/retrieve/pii/S2214860420309106 https://linkinghub.elsevier.com/retrieve/pii/S2214860420309106

郝永志 , 赵海东 , 林嘉华 . 基于机器学习的挤压铸造铝合金力学性能预测 [J ] . 特种铸造及有色合金 , 2019 , 39 ( 8 ): 859 - 862 . DOI: 10.15980/j.tzzz.2019.08.012 http://doi.org/10.15980/j.tzzz.2019.08.012 基于现有挤压铸造研究数据，以不同合金元素及其含量下铝合金的力学性能作为训练数据，结合带有因子分解机（Factorization Machine，FM）的多项式回归模型，通过机器学习算法，以梯度下降策略对模型进行训练学习。然后，以合金的元素含量作为输入条件，预测该成分下合金的力学性能，并与试验力学性能作对比验证。结果表明，该模型能较好地预测不同元素含量铝合金的抗拉强度、屈服强度、硬度和伸长率等力学性能指标。

HAO Y Z , ZHAO H D , LIN J H . Prediction of mechanical properties of squeeze-cast aluminum alloys based on machine learning [J ] . Special Casting and Nonferrous Alloys , 2019 , 39 ( 8 ): 859 - 862 . (in Chinese)

SHAHANE S , ALURU N , FERREIRA P , et al. Optimization of solidification in die casting using numerical simulations and machine learning [J ] . Journal of Manufacturing Processes , 2020 , 51 : 130 - 141 . DOI: 10.1016/j.jmapro.2020.01.016 http://doi.org/10.1016/j.jmapro.2020.01.016 https://linkinghub.elsevier.com/retrieve/pii/S152661252030027X https://linkinghub.elsevier.com/retrieve/pii/S152661252030027X

QI X B , CHEN G F , LI Y , et al. Applying neural-network-based machine learning to additive manufacturing: current applications,challenges,and future perspectives [J ] . Engineering , 2019 , 5 ( 4 ): 721 - 729 . DOI: 10.1016/j.eng.2019.04.012 http://doi.org/10.1016/j.eng.2019.04.012 https://linkinghub.elsevier.com/retrieve/pii/S2095809918307732 https://linkinghub.elsevier.com/retrieve/pii/S2095809918307732

JIN N , LIU D R . Wavelet basis function neural networks for sequential learning [J ] . IEEE Transactions on Neural Networks , 2008 , 19 ( 3 ): 523 - 528 . DOI: 10.1109/TNN.2007.911749 http://doi.org/10.1109/TNN.2007.911749 In this letter, we develop the wavelet basis function neural networks (WBFNNs). It is analogous to radial basis function neural networks (RBFNNs) and to wavelet neural networks (WNNs). In WBFNNs, both the scaling function and the wavelet function of a multiresolution approximation (MRA) are adopted as the basis for approximating functions. A sequential learning algorithm for WBFNNs is presented and compared to the sequential learning algorithm of RBFNNs. Experimental results show that WBFNNs have better generalization property and require shorter training time than RBFNNs.

GÁLVEZ A , IGLESIAS A , PUIG J . Iterative two-step genetic-algorithm-based method for efficient polynomial B-spline surface reconstruction [J ] . Information Sciences , 2012 , 182 : 56 - 76 . DOI: 10.1016/j.ins.2010.09.031 http://doi.org/10.1016/j.ins.2010.09.031 https://linkinghub.elsevier.com/retrieve/pii/S0020025510004858 https://linkinghub.elsevier.com/retrieve/pii/S0020025510004858

COELHO L , PESSOA M . Nonlinear identification using a B-spline neural network and chaotic immune approaches [J ] . Mechanical Systems and Signal Processing , 2009 , 23 : 2418 - 2434 . DOI: 10.1016/j.ymssp.2009.01.013 http://doi.org/10.1016/j.ymssp.2009.01.013 https://linkinghub.elsevier.com/retrieve/pii/S0888327009000090 https://linkinghub.elsevier.com/retrieve/pii/S0888327009000090

WANG J J , CHEN B L , YANG C J . Approximation of algebraic and trigonometric polynomials by feedforward neural networks [J ] . Neural Computing and Applications , 2012 , 21 : 73 - 80 . DOI: 10.1007/s00521-011-0617-3 http://doi.org/10.1007/s00521-011-0617-3 http://link.springer.com/10.1007/s00521-011-0617-3 http://link.springer.com/10.1007/s00521-011-0617-3

DOKUR Z , OLMEZ T . Heartbeat classification by using a convolutional neural network trained with Walsh functions [J ] . Neural Computing and Applications , 2020 , 32 : 12515 - 12534 . DOI: 10.1007/s00521-020-04709-w http://doi.org/10.1007/s00521-020-04709-w

HAHM N , HONG B . The capability of localized neural network approximation [J ] . Honam Mathematical , 2013 , 35 ( 4 ): 729 - 738 .

徐应祥 . 一种新型B-样条拟插值 [J ] . 仲恺农业工程学院学报 , 2021 , 34 ( 4 ): 60 - 65 .

XU Y X . A new B-spline quasi-interpolation [J ] . Journal of ZhongKai University of Agriculture and Technology , 2021 , 34 ( 4 ): 60 - 65 . (in Chinese)

HONG X , IPLIKCI S , CHEN S , et al. A model-based PID controller for Hammerstein systems using B-spline neural networks [J ] . International Journal of Adaptive Control and Signal Processing , 2014 , 28 : 412 - 428 . DOI: 10.1002/acs.v28.3-5 http://doi.org/10.1002/acs.v28.3-5 http://doi.wiley.com/10.1002/acs.v28.3-5 http://doi.wiley.com/10.1002/acs.v28.3-5

HONG X , CHEN S . The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization [J ] . Neurocomputing , 2012 , 82 : 216 - 223 . DOI: 10.1016/j.neucom.2011.11.016 http://doi.org/10.1016/j.neucom.2011.11.016 https://linkinghub.elsevier.com/retrieve/pii/S092523121100703X https://linkinghub.elsevier.com/retrieve/pii/S092523121100703X

SETIAWAN I , PRIYADI A , MIYAUCHI H , et al. Adaptive B-spline neural network-based vector control for a grid side converter in wind turbine-DFIG systems [J ] . IEEE Transactions on Electrical and Electronic Engineering , 2015 , 10 : 674 - 682 .

WU Z Q , JIA W J , ZHAO L R , et al. Short-term wind speed forecast based on B-spline neural network optimized by PSO [J ] . Mathematical Problems in Engineering , 2015 : 278635 .

谢心和 , 赵东明 , 吕明昊 , 等 . B样条神经网络用于区域重力异常曲面拟合 [C ] ∥第十六届国家安全地球物理专题研讨会论文集 . 北京:中国地球物理学会 , 西安 : 陕西省地球物理学会 , 2020 : 39 - 44 .

XIE X H , ZHAO D M , LÜ M H , et al. Surface fitting of regional gravity anomaly based on B-spline neural network [C ] ∥Proceedings of the 16th National Security Geophysics Symposium.Beijing:Chinese Geophysical Society, Xi’an : Shaanxi Geophysical Society , 2020 : 39 - 44 . (in Chinese)

CHITRA K , VENNILA C . A novel patch selection technique in ANN B-Spline Bayesian hyperprior interpolation VLSI architecture using fuzzy logic for highspeed satellite image processing [J ] . Journal of Ambient Intelligence and Humanized Computing , 2020 , 12 ( 6 ): 6491 - 6504 . DOI: 10.1007/s12652-020-02264-9 http://doi.org/10.1007/s12652-020-02264-9

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