Determination of mass, damping coefficient, and stiffness of production system using convolution integral

Discrete event simulation (DES) is most widely used tool for modelling complex production systems. DES model requires skilful mapping of actual production process in a framework used for DES modelling. It also calls for extensive data collection for arriving at probability distributions followed by the time required by various activities involved in production processes and also the probability distribution of various occurrences affecting production process. System dynamics (SD) has also been used to model production system. SD model requires forming causal loop model (stock–flow diagram) showing interrelated influential variables affecting production process, their rates and mathematical relation between cause and effect. Continuous and discrete flow models had also been used for modelling production system. This work proposes a tool for simulating the production output which is simpler as compared to these two techniques. This investigation attempts to establish relationship between inputs to the production system, state of production system and number of units produced. Second-order differential equation analogizing production system with mechanical vibration system is devised and the constants of differential equation are determined. These constants signify mass, damping factor and natural frequency of mechanical vibration system. Differential equation formed for production system helps to simulate the production output in response to forces such as supply order, breakdown and preventive maintenance.

[1]  Christos G. Cassandras,et al.  Introduction to Discrete Event Systems , 1999, The Kluwer International Series on Discrete Event Dynamic Systems.

[2]  Benson H. Tongue,et al.  Principles of vibration , 1996 .

[3]  Ülo LEPIK Application of wavelet transform techniques to vibration studies , 2001, Proceedings of the Estonian Academy of Sciences. Physics. Mathematics.

[4]  R. Ghanem,et al.  A WAVELET-BASED APPROACH FOR THE IDENTIFICATION OF LINEAR TIME-VARYING DYNAMICAL SYSTEMS , 2000 .

[5]  Christos G. Cassandras,et al.  Perturbation Analysis and Optimization of Multiclass Multiobjective Stochastic Flow Models , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[6]  Tillal Eldabi,et al.  Simulation in manufacturing and business: A review , 2010, Eur. J. Oper. Res..

[7]  L. Ljung,et al.  Subspace-based multivariable system identification from frequency response data , 1996, IEEE Trans. Autom. Control..

[8]  Walter Ukovich,et al.  Continuous flow models for batch manufacturing: a basis for a hierarchical approach , 1995 .

[9]  Steve Peterson Software for model-building and simulation: An illustration of design philosophy , 1992 .

[10]  Howard A. Gaberson,et al.  The Use of Wavelets for Analyzing Transient Machinery Vibration , 2002 .

[11]  L LeeHau,et al.  Lot Sizing with Random Yields , 1995 .

[12]  Denis Royston Towill,et al.  System dynamics- background, methodology and applications. 1. Background and methodology , 1993 .

[13]  N. C. Nigam Introduction to Random Vibrations , 1983 .

[14]  M. Soeda,et al.  Identification of bilinear systems by wavelet connection coefficients , 2003, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[15]  Iyad Mourani,et al.  Perturbation analysis and optimisation of continuous flow transfer lines with delay , 2013 .

[16]  Nidhal Rezg,et al.  Joint optimization of preventive maintenance and inventory control in a production line using simulation , 2004 .

[17]  Chrwan-Jyh Ho,et al.  Evaluating the impact of operating environments on MRP system nervousness , 1989 .

[18]  Michael Pidd Guidelines for the design of data driven generic simulators for specific domains , 1992, Simul..

[19]  Mats Jägstam,et al.  A handbook for integrating discrete event simulation as an aid in conceptual design of manufacturing systems , 2002, Proceedings of the Winter Simulation Conference.

[20]  George Chryssolouris,et al.  An approach to the dynamic modelling of manufacturing systems , 1998 .

[21]  Yannis A. Phillis,et al.  A CONTINUOUS-FLOW MODEL FOR PRODUCTION NETWORKS WITH FINITE BUFFERS, UNRELIABLE MACHINES, AND MULTIPLE PRODUCTS , 1997 .

[22]  H. Zhang,et al.  Optimal and Hierarchical Controls in Dynamic Stochastic Manufacturing Systems: A Survey , 2002, Manuf. Serv. Oper. Manag..

[23]  Hamid Hajihosseini,et al.  Importance of Simulation in Manufacturing , 2009 .

[24]  Harish Parthasarathy,et al.  Wavelet Based Identification of Second Order Linear System , 2009 .

[25]  Brian W. Hollocks,et al.  Forty years of discrete-event simulation—a personal reflection , 2006, J. Oper. Res. Soc..

[26]  W.M.J. Geraerds The cost of downtime for maintenance : preliminary considerations , 1984 .

[27]  Hau L. Lee,et al.  Lot Sizing with Random Yields: A Review , 1995, Oper. Res..

[28]  M.A.A. Emam,et al.  A tyre-terrain interaction model for off-road vehicles , 2011 .

[29]  George Chryssolouris,et al.  Oscillator analogy for modelling the manufacturing systems dynamics , 2008 .

[30]  George Chryssolouris,et al.  Manufacturing Systems: Theory and Practice , 1992 .

[31]  Nathalie Sauer,et al.  Perturbation analysis for continuous and discrete flow models: a study of the delivery time impact on the optimal buffer level , 2013 .

[32]  Singiresu S. Rao Engineering Optimization : Theory and Practice , 2010 .

[33]  Yorai Wardi,et al.  Continuous flow models: modeling, simulation and continuity properties , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[34]  Nuno M. M. Maia,et al.  Modal analysis identification techniques , 2001, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[35]  F. Hemez,et al.  Use of response surface metamodels for identification of stiffness and damping coefficients in a simple dynamic system , 2005 .

[36]  Tim Baines,et al.  An opportunity for system dynamics in manufacturing system modelling , 1999 .