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Research Papers

An Industrially Validated Method for Weld Load Balancing in Multi Station Sheet Metal Assembly Lines

[+] Author and Article Information
Johan Segeborn

Volvo Car Corporation,
Dept. 81620/PVÖS32,
Gothenburg SE-405-31, Sweden
e-mail: johan.segeborn@volvocars.com

Daniel Segerdahl

e-mail: daniel.segerdahl@fcc.chalmers.se

Fredrik Ekstedt

e-mail: fredrik.ekstedt@fcc.chalmers.se

Johan S. Carlson

e-mail: johan.carlson@fcc.chalmers.se
Fraunhofer-Chalmers Research Centre,
Chalmers Science Park,
Gothenburg SE-412-88, Sweden

Mikael Andersson

e-mail: mande535@volvocars.com

Anders Carlsson

e-mail: acarlss6@volvocars.com
Volvo Car Corporation,
Dept. 81620/PVÖS32,
Gothenburg SE-405-31, Sweden

Rikard Söderberg

Chalmers University of Technology,
Department of Product and Production Development,
Gothenburg SE-412-96, Sweden
e-mail: Rikard.soderberg@chalmers.se

Manuscript received November 22, 2011; final manuscript received August 12, 2013; published online November 5, 2013. Assoc. Editor: Steven J. Skerlos.

J. Manuf. Sci. Eng 136(1), 011002 (Nov 05, 2013) (7 pages) Paper No: MANU-11-1371; doi: 10.1115/1.4025393 History: Received November 22, 2011; Revised August 12, 2013

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.

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References

Segeborn, J., 2009, “Towards an Effective Virtual Sheet Metal Assembly Development Process Securing Geometrical Quality and Equipment Utilization,” Licentiate thesis, ISSN 1652-9243, Chalmers University of Technology, Department of Product and Production Development, Göteborg, Sweden.
Segeborn, J., Carlsson, A., Carlson, J. S., and Söderberg, R., 2009, “A Chronological Framework for Virtual Sheet Metal Assembly Design,” Proceedings of 11th CIRP International Conference on Computer Aided Tolerancing, Annecy, France.
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Segeborn, J., Segerdahl, D., Carlson, J. S., Carlsson, A., and Söderberg, R., 2009, “Load balancing of Welds in Multi Station Sheet Metal Assembly Lines,” Proceedings of ASME 2010 International Mechanical Engineering Congress & Exposition, IMECE2010, Nov. 12–18, 2010, Vancouver, British Columbia, Canada.
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Figures

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Fig. 1

Line weld load balancing [14]

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Fig. 2

The 3 station stud weld reference line

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Fig. 3

The proposed line balancing method

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Fig. 4

Simplified version of simulated annealing

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Fig. 5

Separation between two work loads

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Fig. 6

Correlation between station separation S and station cycle time after robot coordination

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Fig. 7

The distribution of station 1 before the station separation post step. Station separation S = 0.9.

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Fig. 8

The distribution of station 1 after the station separation post step. Station separation S = 7.6.

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Fig. 10

The distribution of station 2 after the station separation post step. Station separation S = 7.6.

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Fig. 11

The distribution of station 3 before the station separation post step. Station separation S = 1.4.

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Fig. 12

The distribution of station 3 after the station separation post step. Station separation S = 7.6.

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Fig. 9

The distribution of station 2 before the station separation post step. Station separation S = 6.7.

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