ANFIS Modeling for Prediction of Particle Size in Nozzle Assisted Solvent-Antisolvent Process for Making Ultrafine CL-20 Explosiv
Dinesh K. Pal, Shallu Gupta, Deepika Jindal, Anil Kumar, Arun Aggarwal, Prem Latadownload PDF
Abstract. Physical properties such as particle size, surface area and shape of explosive control the rapidity and reliability of initiation, and detonation and thus determine the performance of an explosive device such as slapper detonators. In this paper, Nozzle assisted solvent/antisolvent (NASAS) process for recrystallisation of CL-20 explosive is established. Many process parameters are involved which affect the particle size of the explosive. Therefore an accurate prediction of particle size is required to tailor the particle size. In the present work, an intelligent algorithm is applied to build a simplified relationship between recrystallization process parameters and particle size. This can be used to predict explosive particle size with a wide range of process parameters through an adaptive neuro-fuzzy inference system (ANFIS). The model is trained using experimental data obtained from design of experiment techniques utilizing a MATLAB software. Six process parameters such as Solution pressure, Antisolvent pressure, Antisolvent temperature, Stirrer speed, Solution concentration and Nozzle diameter are used as input variables of the model and the particle size is used as the output variable. The predicted results are in close agreement with experimental values and the accuracy of the model has been tested by comparing the simulated data with actual data from the explosive recrystallization experiments and found to be inacceptable range with maximum absolute percentage error of 11.52 %. The ultrafine CL-20 prepared by NASAS process is used in Slapper detonator application. The threshold initiation voltages for CL-20 based slapper detonator is found to be in the range of 0.9 kV with standard deviation of ±0.1 kV.
Ultrafine CL-20, Artificial Neural Network, ANFIS, Neuro-Fuzzy
Published online 7/30/2019, 7 pages
Copyright © 2019 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Dinesh K. Pal, Shallu Gupta, Deepika Jindal, Anil Kumar, Arun Aggarwal, Prem Lata, ANFIS Modeling for Prediction of Particle Size in Nozzle Assisted Solvent-Antisolvent Process for Making Ultrafine CL-20 Explosiv, Materials Research Proceedings, Vol. 13, pp 121-127, 2019
The article was published as article 21 of the book Explosion Shock Waves and High Strain Rate Phenomena
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
 J. Wang, et al., Study on Ultrasound‐and Spray‐Assisted Precipitation of CL‐20. Propellants, Explosives, Pyrotechnics, 37(6) (2012), 670-675. https://doi.org/10.1002/prep.201100088
 D. Spitzer, et al., Continuous crystallization of submicrometer energetic compounds, Propellants, Explosives, Pyrotechnics, 36(1) (2011), 65-74. https://doi.org/10.1002/prep.200900002
 M. Klaumünzer,J. Hübner, and D. Spitzer, Production of Energetic Nanomaterials by Spray Flash Evaporation, World Academy of Science, Engineering and Technology, International Journal of Chemical, Molecular, Nuclear, Materials and Metallurgical Engineering, 10(9) (2016), 1191-1195.
 G. Yang,et al., Preparation and Characterization of Nano‐TATB Explosive, Propellants, Explosives, Pyrotechnics: An International Journal Dealing with Scientific and Technological Aspects of Energetic Materials, 31(5) (2006), 390-394. https://doi.org/10.1002/prep.200600053
 S. Gupta, D. K.Pal, et al., Pressurized Nozzle‐Based Solvent/Anti‐Solvent Process for Making Ultrafine ϵ‐CL‐20 Explosive, Propellants, Explosives, Pyrotechnics, 42(7) (2017), 773-783. https://doi.org/10.1002/prep.201700002
 S. Gupta, et al., D. K. Pal, et. al., Micro Nozzle Assisted Spraying Process for Re‐crystallization of Submicrometer Hexanitrostilbene Explosive, Propellants, Explosives, Pyrotechnics, 43 (7) (2018), 721-731. https://doi.org/10.1002/prep.201800008
 S. Singh, et al., Neural network analysis of steel plate processing, Iron making and Steelmaking, 25(5) (1998), 355-365.
 J. M. Vitek,Neural networks applied to welding: two examples, ISIJ international, 39(10) (1999), 1088-1095. https://doi.org/10.2355/isijinternational.39.1088
 I.S. Kim, et al., Optimal design of neural networks for control in robotic arc welding, Robotics and computer-integrated manufacturing, 20(1) (2004), 57-63. https://doi.org/10.1016/s0736-5845(03)00068-1
 S. Pal, S.K. Pal, and A.K. Samantaray, Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals, Journal of materials processing technology, 202(1-3) (2008), 464-474. https://doi.org/10.1016/j.jmatprotec.2007.09.039
 A.K.Pannier,R.M. Brand, and D.D. Jones, Fuzzy modeling of skin permeability coefficients, Pharmaceutical research, 20(2) (2003.), 143-148.
 J. Jang,Neuro-fuzzy modeling: architectures, analyses and applications [dissertation]. California: University of Berkeley, 1992.
 J. Hines,L.H. Tsoukalas, and R.E. Uhrig, MATLAB supplement to fuzzy and neural approaches in engineering, John Wiley & Sons, Inc., 1997
 J. S. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE transactions on systems, man, and cybernetics, 1993, 23(3), 665-685. https://doi.org/10.1109/21.256541