Unified variation modeling of sheet metal assembly considering rigid and compliant variations

Author(s):  
Na Cai ◽  
Lihong Qiao ◽  
Nabil Anwer
2000 ◽  
Author(s):  
S. Jack Hu ◽  
Yufeng Long ◽  
Jaime Camelio

Abstract Assembly processes for compliant non-rigid parts are widely used in manufacturing automobiles, furniture, and electronic appliances. One of the major issues in the sheet metal assembly process is to control the dimensional variation of assemblies throughout the assembly line. This paper provides an overview of the recent development in variation analysis for compliant assembly. First, the unique characteristics of compliant assemblies are discussed. Then, various approaches to variation modeling for compliant assemblies are presented for single station and multi-station assembly lines. Finally, examples are given to demonstrate the applications of compliant assembly variation models.


2013 ◽  
pp. 173-190
Author(s):  
Johan Segeborn ◽  
Anders Carlsson ◽  
Johan S. Carlson ◽  
Rikard Söderberg

Work ◽  
2011 ◽  
Vol 39 (2) ◽  
pp. 169-176 ◽  
Author(s):  
Ann Marie Dale ◽  
A.E. Rohn ◽  
A. Burwell ◽  
W. Shannon ◽  
J. Standeven ◽  
...  

1997 ◽  
Vol 119 (3) ◽  
pp. 368-374 ◽  
Author(s):  
S. Charles Liu ◽  
S. Jack Hu

Traditional variation analysis methods, such as Root Sum Square method and Monte Carlo simulation, are not applicable to sheet metal assemblies because of possible part deformation during the assembly process. This paper proposes the use of finite element methods (FEM) in developing mechanistic variation simulation models for deformable sheet metal parts with complex two or three dimensional free form surfaces. Mechanistic variation simulation provides improved analysis by combining engineering structure models and statistical analysis in predicting the assembly variation. Direct Monte Carlo simulation in FEM is very time consuming, because hundreds or thousands of FEM runs are required to obtain a realistic assembly distribution. An alternative method, based on the Method of Influence Coefficients, is developed to improve the computational efficiency, producing improvements by several orders of magnitude. Simulations from both methods yield almost identical results. An example illustrates the developed methods used for evaluating sheet metal assembly variation. The new approaches provide an improved understanding of sheet metal assembly processes.


Author(s):  
Johan Segeborn ◽  
Daniel Segerdahl ◽  
Fredrik Ekstedt ◽  
Johan S. Carlson ◽  
Mikael Andersson ◽  
...  

Sheet metal assembly is investment intense. Therefore, the equipment needs to be efficiently utilized. The balancing of welds has a significant influence on achievable production rate and equipment utilization. Robot line balancing is a complex problem, where each weld is to be assigned to a specific station and robot, such that line cycle time is minimized. Industrial robot line balancing has been manually conducted in computer aided engineering (CAE)-tools based on experience and trial and error rather than mathematical methods. However, recently an automatic method for robot line balancing was proposed by the authors. To reduce robot coordination cycle time losses, this method requires identical reach ability of all line stations. This limits applicability considerably since in most industrial lines, reach ability differs over the stations to further line reach ability and flexibility. Therefore, in this work we propose a novel generalized simulation-based method for automatic robot line balancing that allows any robot positioning. It reduces the need for robot coordination significantly by spatially separating the robot weld work loads. The proposed method is furthermore successfully demonstrated on automotive stud welding lines, with line cycle times lower than that of the corresponding running production programs. Moreover, algorithm central processing unit (CPU)-times are mere fractions of the lead times of existing CAE-tools.


2006 ◽  
Vol 32 (7-8) ◽  
pp. 690-697 ◽  
Author(s):  
Bing Li ◽  
Hui Tang ◽  
Xiaoping Yang ◽  
Hao Wang

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