Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
권호 표지
DOI QR코드

DOI QR Code

Gaussian Mixture based K2 Rifle Chamber Pressure Modeling of M193 and K100 Bullets

가우시안 혼합모델 기반 탄종별 K2 소화기의 약실압력 모델링

  • Kim, Jong-Hwan (Department of Mechanical & Systems Engineering, Korea Military Academy) ;
  • Lee, Byounghwak (Department of Physics and Chemistry, Korea Military Academy) ;
  • Kim, Kyoungmin (Department of Computer Science, Korea Military Academy) ;
  • Shin, Kyuyong (Department of Computer Science, Korea Military Academy) ;
  • Lee, Wonwoo (Department of Electrical Engineering, Korea Military Academy)
  • 김종환 (육군사관학교 기계.시스템공학과) ;
  • 이병학 (육군사관학교 물리화학과) ;
  • 김경민 (육군사관학교 컴퓨터과학과) ;
  • 신규용 (육군사관학교 컴퓨터과학과) ;
  • 이원우 (육군사관학교 전자공학과)
  • Received : 2018.07.16
  • Accepted : 2018.12.07
  • Published : 2019.02.05
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents a chamber pressure model development of K2 rifle by applying Gaussian mixture model. In order to materialize a real recoil force of a virtual reality shooting rifle in military combat training, the chamber pressure which is one of major components of the recoil force needs to be investigated and modeled. Over 200,000 data of the chamber pressure were collected by implementing live fire experiments with both K100 and M193 of 5.56 mm bullets. Gaussian mixture method was also applied to create a mathematical model that satisfies nonlinear, asymmetry, and deviations of the chamber pressure which is caused by irregular characteristics of propellant combustion. In addition, Polynomial and Fourier Regression were used for comparison of results, and the sum of squared errors, the coefficient of determination and root-mean-square errors were analyzed for performance measurement.

Keywords

GSGGBW_2019_v22n1_27_f0001.png 이미지

Fig. 1. An example of gaussian mixture model

GSGGBW_2019_v22n1_27_f0002.png 이미지

Fig. 3. The interior pressure results of K100 bullet and proposed result colored in red

GSGGBW_2019_v22n1_27_f0003.png 이미지

Fig. 4. The interior pressure results of M193 bullet and proposed result colored in red

GSGGBW_2019_v22n1_27_f0004.png 이미지

Fig. 5. The standard pressure results(proposed) of K100 bullet and five Gaussian mixtures

GSGGBW_2019_v22n1_27_f0005.png 이미지

Fig. 6. M193 bullet standard pressure result(proposed) and five Gaussian mixtures

GSGGBW_2019_v22n1_27_f0006.png 이미지

Fig. 7. Model comparison between K100 and M193 created by Gaussian mixture model.

GSGGBW_2019_v22n1_27_f0007.png 이미지

Fig. 2. (left) Interior ballistic pressure test-bed with four piezoelectric pressure sensors and (right) interior ballistic experimental environment

Table 1. Specifications of K100 and M193 bullets

GSGGBW_2019_v22n1_27_t0001.png 이미지

Table 2. The number of gaussian analyzed by BIC

GSGGBW_2019_v22n1_27_t0002.png 이미지

Table 3. The parameters of gaussian mixtures

GSGGBW_2019_v22n1_27_t0003.png 이미지

Table 4. The proposed model results of statistical criterions

GSGGBW_2019_v22n1_27_t0004.png 이미지

References

  1. F. Morelli, J. M. Neugebauer, M. E. LaFiandra, P. Burcham, and C. T. Gordon, "Recoil Measurement, Mitigation Techniques, and Effects on Small Arms Weapon Design and Marksmanship Performance," IEEE Transactions on Human-Machine Systems, Vol. 44, pp. 422-428, 2014. https://doi.org/10.1109/THMS.2014.2301715
  2. K. Monti and D. Marse, "Method and Apparatus for Firearm Recoil Simulation," ed: Google Patents, 2018.
  3. Park B. U., Yun J. H., Lee K. S., Kang S. J., "Simulation Shooting Gun," Korean Patent, 1020140118535, 2015.
  4. T. LUKAC, R. VITEK, L. DO DUC, and V. HORAK, "Experimental Mechanical Device for Recoil Simualtion," International Scientific Committee, p. 337, 2016.
  5. Haptech Product Descriptions, http://www.haptech.co/products [online, retrieved 2.3.2016]
  6. A. Fedaravicius, A. Survila, L. Patasiene, and E. Slizys, "Design, Research and Practical Implementation of the Laser Shooting Simulation System for 5.56 mm G-36, 7.62 mm FN MAG and 84 mm Carl Gustaf," Problemy Mechatroniki: uzbrojenie, lotnictwo, inzynieria bezpieczenstwa, Vol. 7, pp. 7-18, 2016.
  7. T. J. Cyders, J. J. DiGiovanni, and W. Jay, "Characterization of Natural and Recoil-Induced Vibration of an AR-15 Rifle at the Cheekbone-Stock Interface," in Proceedings of Meetings on Acoustics 173EAA, p. 065011, 2017.
  8. F. Morelli, J. M. Neugebauer, C. A. Haynes, T. C. Fry, S. V. Ortega, D. J. Struve, et al., "Shooter-System Performance Variability as a Function of Recoil Dynamics," Human Factors, Vol. 59, pp. 973-985, 2017. https://doi.org/10.1177/0018720817700537
  9. M. Grasser, M. Florian, H. Christian, M. Gerald, and S. Bergmoser, "Recoil-Measurement, Simulation and Analysis," in International Conference on Interactive Collaborative Learning, pp. 830-839, 2017.
  10. Xiaobing, Z., Ziming, J., Yaxiong, Y., "The Effect of the Random Variations of Loading Conditions on the Interior Ballistic Reliability," Ballistics-International Symposium, Vol. 1, 1995.
  11. L. Sangkil, K. Sungdo, and L. Gangyoung, "A Study on the Calculation of Muzzle Velocity Through the Gun Barrel Pressure Measurement," Journal of the Korean Society of Propulsion Engineers, Vol. 12, pp. 60-66, 10 2008.
  12. A. Goshtasby and W. D. Oneill, "Curve Fitting by a Sum of Gaussians," CVGIP: Graphical Models and Image Processing, Vol. 56, pp. 281-288, 1994. https://doi.org/10.1006/cgip.1994.1025
  13. K. Levenberg, "A Method for the Solution of Certain Non-Linear Problems in Least Squares," Quarterly of Applied Mathematics, Vol. 2, pp. 164-168, 1944. https://doi.org/10.1090/qam/10666
  14. Bhat, H. S. and N. Kumar, "On the Derivation of the Bayesian Information Criterion," School of Natural Sciences, University of California, 2010.
  15. J.-H. Kim and S. Jo, "Recursive Bayesian Filter based Strike Velocity Estimation for Small Caliber Projectile," Journal of the Korea Institute of Military Science and Technology, Vol. 19, pp. 177-184, 2016. https://doi.org/10.9766/KIMST.2016.19.2.177