Tip Deflection Determination of a Barrel for the Effect of an Accelerating Projectile Before Firing Using Finite Element and Artificial Neural Network Combined Algorithm

FOR REALISTIC APPLICATIONS, DESIGN AND CONTROL ENGINEERS HAVE LIMITED MODELLING OPTIONS IN DEALING WITH SOME VIBRATION PROBLEMS THAT HOLD LOTS OF NONLINEAR CHARACTERISTICS SUCH AS NON-UNIFORM GEOMETRY, VARIABLE VELOCITY LOADINGS, INDEFINITE DAMPING CASES, ETC. FOR THESE REASONS NUMEROUS TIME CONSUMING EXPERIMENTAL STUDIES AT HIGH COSTS MUST BE DONE FOR DETERMINING THE ACTUAL BEHAVIOUR SUCH NON-LINEAR SYSTEMS. HOWEVER, USING ADVANTAGES OF MULTIPLE COMPUTA-TIONAL METHODS LIKE FINITE ELEMENT METHOD (FEM) TOGETHER WITH AN ARTIFICIAL INTELLIGENCE (ANN), MANY COMPLICATED ENGINEERING PROB-LEMS CAN BE HANDLED AND SOLVED TO SOME EXTENT. THIS STUDY, PRO-POSES A NEW COLLECTIVE METHOD TO DEAL WITH THE NONLINEAR VIBRATIONS OF THE BARRELS IN ORDER TO FULFIL ACCURATE SHOOTING EXPECTANCY. US-ING KNOWN ANALYTICAL METHODS, IN PRACTICAL, TO DETERMINE DYNAMIC BEHAVIOUR OF THE BARREL BEAM IS NOT POSSIBLE FOR ALL CONDITIONS OF FIRING THAT INCLUDE NUMEROUS VARIETIES OF AMMUNITION FOR DIFFERENT PURPOSES, AND EACH PROJECTILE OF DIFFERENT AMMUNITION HAS DIFFERENT MASS AND EXIT VELOCITY. IN ORDER TO COVER ALL CASES THIS STUDY PROPOS-ES A NEW METHOD THAT COMBINES A PRECISE FEM WITH ANN, AND CAN BE USED FOR DETERMINING THE EXACT DYNAMIC BEHAVIOUR OF A BARREL FOR SOME CASES AND THEN FOR PRECISELY PREDICTING THE BEHAVIOUR FOR ALL OTHER POSSIBLE CASES OF FIRING. IN THIS STUDY, THE WHOLE NONLINEAR BEHAVIOUR OF AN ANTIAIRCRAFT BARREL WERE OBTAINED WITH 3.5% ACCU-RACY ERRORS BY ANN TRAINED BY FEM USING CALCULATED ANALYSIS RESULTS OF AMMUNITIONS FOR A PARTICULAR RANGE. THE PROPOSED FEM-ANN COMBINED METHOD CAN BE VERY USEFUL FOR DESIGN AND CONTROL ENGI-NEERS IN DESIGN AND CONTROL OF BARRELS IN ORDER TO COMPENSATE THE EFFECT OF NONLINEAR VIBRATIONS OF A BARREL FOR ACHIEVING A HIGHER SHOOTING ACCURACY; AND CAN REDUCE HIGH-COST EXPERIMENTAL WORKS.

[1]  Volkan Kahya,et al.  Dynamic analysis of laminated composite beams under moving loads using finite element method , 2012 .

[2]  İsmail Esen,et al.  Dynamic response of a beam due to an accelerating moving mass using moving finite element approximation , 2011 .

[3]  Ping Lou,et al.  Finite element formulae for internal forces of Bernoulli–Euler beams under moving vehicles , 2013 .

[4]  Mehmet Akif Koç,et al.  Dynamic response of a 120 mm smoothbore tank barrel during horizontal and inclined firing positions , 2015 .

[5]  Mohammad Tawfik,et al.  Dynamics and Stability of Stepped Gun-Barrels with Moving Bullets , 2008 .

[6]  Ismail Esen,et al.  Optimization of a passive vibration absorber for a barrel using the genetic algorithm , 2015, Expert Syst. Appl..

[7]  George T. Michaltsos,et al.  DYNAMIC BEHAVIOUR OF A SINGLE-SPAN BEAM SUBJECTED TO LOADS MOVING WITH VARIABLE SPEEDS , 2002 .

[8]  M. Hosseini,et al.  Neural Network Approach for Estimation of Penetration Depth in Concrete Targets by Ogive-nose Steel Projectiles , 2015 .

[9]  Carlos A. Perez-Ramirez,et al.  New methodology for modal parameters identification of smart civil structures using ambient vibrations and synchrosqueezed wavelet transform , 2016, Eng. Appl. Artif. Intell..

[10]  Mehmet Emin Aydin,et al.  Creep modelling of polypropylenes using artificial neural networks trained with Bee algorithms , 2015, Eng. Appl. Artif. Intell..

[11]  edward l. wilson static and dynamic analysis of structures , 2007 .

[12]  Yusuf Çay,et al.  Prediction of a gasoline engine performance with artificial neural network , 2013 .

[13]  Massood Mofid,et al.  Investigation of critical influential speed for moving mass problems on beams , 2009 .

[14]  R. Clough,et al.  Dynamics Of Structures , 1975 .

[15]  Jacek Czarnigowski,et al.  A neural network model-based observer for idle speed control of ignition in SI engine , 2010, Eng. Appl. Artif. Intell..

[16]  İsmail Esen A new finite element for transverse vibration of rectangular thin plates under a moving mass , 2013 .

[17]  Mark E. Oxley,et al.  Neural networks for automatic target recognition , 1995, Neural Networks.

[18]  A. Cifuentes Dynamic response of a beam excited by a moving mass , 1989 .

[19]  Ahmed K. Noor Survey of computer programs for heat transfer analysis , 1986 .

[20]  İsmail Esen,et al.  A NEW FEM PROCEDURE FOR TRANSVERSE AND LONGITUDINAL VIBRATION ANALYSIS OF THIN RECTANGULAR PLATES SUBJECTED TO A VARIABLE VELOCITY MOVING LOAD ALONG AN ARBITRARY TRAJECTORY , 2015 .

[21]  James F. Doyle,et al.  Static and Dynamic Analysis of Structures: with An Emphasis on Mechanics and Computer Matrix Methods , 1991 .

[22]  Nader Jalili,et al.  Vehicle–passenger–structure interaction of uniform bridges traversed by moving vehicles , 2003 .

[23]  Ismail Esen,et al.  Artificial neural network application for modeling the rail rolling process , 2014, Expert Syst. Appl..

[24]  Ping Lou,et al.  A vehicle-track-bridge interaction element considering vehicle's pitching effect , 2005 .

[25]  Yeong-Bin Yang,et al.  FREQUENCY VARIATION IN VEHICLE–BRIDGE INTERACTION SYSTEMS , 2013 .

[26]  Anthony N. Kounadis,et al.  THE EFFECT OF A MOVING MASS AND OTHER PARAMETERS ON THE DYNAMIC RESPONSE OF A SIMPLY SUPPORTED BEAM , 1996 .

[27]  Yi-Ming Wang The transient dynamics of a moving accelerating/decelerating mass traveling on a periodic-array non-homogeneous composite beam , 2009 .

[28]  L Fryba,et al.  VIBRATION OF SOLIDS AND STRUCTURES UNDER MOVING LOADS (3RD EDITION) , 1999 .

[29]  Sunday Tunbosun Oni,et al.  TRANSVERSE MOTIONS OF RECTANGULAR PLATES RESTING ON ELASTIC FOUNDATION AND UNDER CONCENTRATED MASSES MOVING AT VARYING VELOCITIES , 2015 .

[30]  Eric L. Kathe Design and Validation of a Gun Barrel Vibration Absorber. , 1997 .

[31]  Ali Nikkhoo,et al.  Inspection of a Rectangular Plate Dynamics Under a Moving Mass With Varying Velocity Utilizing BCOPs , 2015 .

[32]  Rubem Matimoto Koide,et al.  LAMINATED COMPOSITES BUCKLING ANALYSIS USING LAMINATION PARAMETERS, NEURAL NETWORKS AND SUPPORT VECTOR REGRESSION , 2015 .

[33]  Bo Liu,et al.  Impact coefficient and reliability of mid-span continuous beam bridge under action of extra heavy vehicle with low speed , 2015 .

[34]  Kenneth Olsen,et al.  GUN BARREL VIBRATION ABSORBER TO INCREASE ACCURACY , 2001 .

[35]  Ruben Ruiz-Gonzalez,et al.  An Artificial Neural Network based expert system fitted with Genetic Algorithms for detecting the status of several rotary components in agro-industrial machines using a single vibration signal , 2015, Expert Syst. Appl..

[36]  Khaled Galal,et al.  A numerical element for vehicle–bridge interaction analysis of vehicles experiencing sudden deceleration , 2013 .

[37]  Manolis Papadrakakis,et al.  Neural network based prediction schemes of the non-linear seismic response of 3D buildings , 2012, Adv. Eng. Softw..

[38]  Heow Pueh Lee,et al.  Transverse vibration of a Timoshenko beam acted on by an accelerating mass , 1996 .

[39]  M. H Kadivar,et al.  Forced vibration of unsymmetric laminated composite beams under the action of moving loads , 1998 .

[40]  Yozo Fujino,et al.  Prediction of vehicle-induced local responses and application to a skewed girder bridge , 2011 .

[41]  L. Meirovitch Analytical Methods in Vibrations , 1967 .

[42]  J. Edward Alexander AGS Gun and Projectile Dynamic Modeling Correlation to Test Data , 2007 .