In this paper, using the production system simulation software Plant Simulation to model and simulate an automobile engine block machining line, and then analysis the simulation data. Experimental data shows that the production line program has a strong production capacity and good economic, which can be implemented. 1. The basic steps of Simulation and Modeling General computer simulation activities include three stages, which are preparation, implementation and evaluation. The main aspects of the various stages included in the figure below. Fig. 1 Computer simulation steps to solve the problem In the preparation stage is mainly on the issue of quantitative or qualitative description and Clear purpose and mission of the simulation. Get Comprehensive and in-depth understanding of the system through investigation and analysis and then abstract and separate the problem. Isolating study object from the complex problems, which can be reflected the characteristics by, to describe the system in detail as possible. According to the set simulation target, completing selection, organization, abstraction and simplification of the system, then build the simulation model. The main contents that needed to be consider of when modeling are: System processes, operating procedures and logical relationships. Start by establishing a relatively simple model, which reflect the principal contradiction of the system, And then gradually to supplement and improve it. The complexity of the model should adapt the research objectives you want to achieve. The implementation phase of work includes experimental design, experimental run and model validation. The assessment phase of the work is to read and interpret the results of the simulation, for Illustrating the effect of the decision variables to the system. 2. Automobile engine block machining line systems analysis Automobile engine block machining line design. The program reference certain engine block production line, Production line consists of five sets of CNC, 3 sets of plane, 2 sets of online gages, 2 washing machine, one honing machine and two auxiliary. in addition to offline gages, the outside 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2015) © 2015. The authors Published by Atlantis Press 2149 equipment include one CMM and one coordinate washing machine. The process production line use U-shaped and annular layout, and the entire cylinder line use parallel, serial hybrid arrangement. Since most CNC have longer beats, they are divided into A/B two parallel modules. Auxiliary equipment, washing machine and honing machine have shorter beats, so they use the serial arrangement. Since the box parts are similar in processing complexity and processing time, when shunting parts, use the control strategy of percentage. The two machining centers are average used averagely, and each fraction was 50%, making maximum utilization of the machining center. Layout diagram is shown below. Fig. 2 Production line layout1 Fig. 3 Production line layout2 3. Creation of the simulation model 3.1The basic modeling steps of Plant Simulation. 1.Selection of the object entity: Select the appropriate object according to the function that the system of simulation modeling need to achieve,corresponding software Plant Simulation function in the object. Mapping relationships between entities and software objects are shown in Table 1, For an object not in the software, you can create your own,Plant Simulation provides a good secondary development environment. 2.Construction of the system layout: According to the actual assembly line layout, drop the required libraries from the toolbox or drag to the appropriate hierarchy. 3.Determination of the object flow: According to the actual production process systems, link model objects and build the basic flow simulation system. 4.Determination of the parameters and rules: In order to realize the function of the actual system, the model object must be set and the corresponding rules are written in the software, which can be completed by using method in the software.
[1]
A. Delchambre,et al.
Hybrid assembly line design and user's preferences
,
2002
.
[2]
Taho Yang,et al.
Solving a multi-objective simulation model using a hybrid response surface method and lexicographical goal programming approach—a case study on integrated circuit ink-marking machines
,
2002,
J. Oper. Res. Soc..
[3]
R E Gomory,et al.
ON THE RELATION BETWEEN INTEGER AND NONINTEGER SOLUTIONS TO LINEAR PROGRAMS.
,
1965,
Proceedings of the National Academy of Sciences of the United States of America.
[4]
Ju Hyun Chul,et al.
A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines
,
1998,
Comput. Oper. Res..
[5]
S. Sethi,et al.
A Note on "Level Schedules for Mixed-Model Assembly Lines in Just-in-Time Production Systems"
,
1991
.
[6]
Wolfgang Kuehn,et al.
Digital Factory - Simulation Enhancing the Product and Production Engineering Process
,
2006,
Proceedings of the 2006 Winter Simulation Conference.
[7]
Nick T. Thomopoulos,et al.
Line Balancing-Sequencing for Mixed-Model Assembly
,
1967
.
[8]
Gregory Levitin,et al.
Genetic algorithm for assembly line balancing
,
1995
.
[9]
Brahim Rekiek,et al.
Designing mixed-product assembly lines
,
2000,
IEEE Trans. Robotics Autom..
[10]
Bernard P. Zeigler,et al.
Theory of modeling and simulation
,
1976
.
[11]
Loren Paul Rees,et al.
Sequencing mixed-model assembly lines with genetic algorithms
,
1996
.
[12]
J. Miltenberg,et al.
Level schedules for mixed-model assembly lines in just-in-time production systems
,
1989
.
[13]
John B. Kidd,et al.
Toyota Production System
,
1993
.
[14]
Mullen,et al.
JIT sequencing for mixed-model assembly lines with setups using Tabu Search
,
1998
.
[15]
Avraham Shtub,et al.
SEQUENCING MIXED-MODEL ASSEMBLY LINES TO LEVEL PARTS USAGE AND MINIMIZE LINE LENGTH
,
1994
.
[16]
Philip Y. Huang,et al.
A comparative study of priority dispatching rules in a hybrid assembly/job shop
,
1984
.
[17]
E. Balas.
An Additive Algorithm for Solving Linear Programs with Zero-One Variables
,
1965
.
[18]
S. Afshin Mansouri,et al.
A Multi-Objective Genetic Algorithm for mixed-model sequencing on JIT assembly lines
,
2005,
Eur. J. Oper. Res..