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Motion optimisation for improved cycle time and reduced vibration in robotic assembly of electronic components
AIMS Electronics and Electrical Engineering, Volume: 3, Issue: 3, Pages: 274 - 289
Swansea University Authors: Christian Griffiths, Cinzia Giannetti
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DOI (Published version): 10.3934/electreng.2019.3.274
Traditionally, six axis robots have not been used in electronic surface mount assembly. However, the need for more flexible production systems that can be used for low to medium production builds, means that these robots can be used due to their high degrees of flexibility. This research investigate...
|Published in:||AIMS Electronics and Electrical Engineering|
American Institute of Mathematical Sciences (AIMS)
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Traditionally, six axis robots have not been used in electronic surface mount assembly. However, the need for more flexible production systems that can be used for low to medium production builds, means that these robots can be used due to their high degrees of flexibility. This research investigated the application of an articulated robot to assemble a multi component PCB for an electronic product. To increase the potential of using this method of automation, a genetic algorithm was used to improve cycle time and condition monitoring was performed to assess the vibrations within the robot structure, during operation. By also studying the motion types the robot movements can be optimized in order to minimize the cycle time and maximize the production throughput with reduced vibrations to improve the accuracy of the assembly process. The study utilised a robotics assembly cell and a robot programmed with different velocities. Vibrations were present throughout out the assembly cycle and by analysing when these large vibrations occur and for which types of motion, an optimal selection could be made. The point-to-point motion type running at 50% speed had a faster assembly time and significantly lower accelerations and oscillations than the other motion types. The spline-linear motion type running at around 30% speed was best for the component insertion due to its linear nature and improved repetition accuracy.
genetic algorithm; assembly optimization; electronics assembly; KUKA robotics; condition monitoring
Faculty of Science and Engineering