Improved parameter optimization method for complex assembly process in robotic manufacturing
Abstract
Purpose
High precision assembly processes using industrial robots require the process parameters to be tuned to achieve desired performance such as cycle time and first time through rate. Some researchers proposed methods such as design-of-experiments (DOE) to obtain optimal parameters. However, these methods only discuss how to find the optimal parameters if the part and/or workpiece location errors are in a certain range. In real assembly processes, the part and/or workpiece location errors could be different from batch to batch. Therefore, the existing methods have some limitations. This paper aims to improve the process parameter optimization method for complex robotic assembly process.
Design/methodology/approach
In this paper, the parameter optimization process based on DOE with different part and/or workpiece location errors is investigated. An online parameter optimization method is also proposed.
Findings
Experimental results demonstrate that the optimal parameters for different initial conditions are different and larger initial part and/or workpiece location errors will cause longer cycle time. Therefore, to improve the assembly process performance, the initial part and/or workpiece location errors should be compensated first, and the optimal parameters in production should be changed once the initial tool position is compensated. Experimental results show that the proposed method is very promising in reducing the cycle time in assembly processes.
Research limitations/implications
The proposed method is practical without any limitation.
Practical implications
The proposed technique is implemented and tested using a real industrial application, a valve body assembly process. Hence, the developed method can be directly implemented in production.
Originality/value
This paper provides a technique to improve the assembly efficiency by compensating the initial part location errors. An online parameter optimization method is also proposed to automatically perform the parameter optimization process without human intervention. Compared with the results using other methods, the proposed technology can greatly reduce the assembly cycle time.
Keywords
Acknowledgements
The authors would like to thank ABB Inc. and Ford Motor Company for partially supporting the project.
Citation
Chen, H., Xu, J., Zhang, B. and Fuhlbrigge, T. (2017), "Improved parameter optimization method for complex assembly process in robotic manufacturing", Industrial Robot, Vol. 44 No. 1, pp. 21-27. https://doi.org/10.1108/IR-03-2016-0098
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited