This research aims to enable modular-collaborative robots to autonomously deal with part and process variations in complex electronics assembly.
Final assembly of complex electronics products is a challenging task that is still largely performed by human workers. For example, components found in a motor drive are small, complex and irregular in shape. Fully automating their assembly is currently only possible through costly bespoke automation. In addition, automation may require changes in product design or in methods for part delivery, increasing the overall product cost.
An alternative approach is through modular-collaborative robots, which are capable, flexible and yet low-cost. In lab environments, they have been shown to successfully collaborate in order to assemble complex electronics components. However, the industrialisation of such robots is challenging due to the reality of day-to-day shop floor operations that introduce numerous part and process variations. These variations, when combined with the complexity of the assembly tasks and the limitations of existing end-effectors, form a barrier that stops the modular-collaborative robot concept from being exploited further. The proposed research seeks to address this barrier by utilising recent advances in low-cost sensing, control systems and artificial intelligence.
This project will be undertaken in collaboration between the University of Sheffield and Siemens. The Siemens Congleton facility will be available for the selected PhD candidate to be used for their development work. The student will also get the opportunity to work with the Productivity through Automation (PtA) team within Siemens, Congleton getting the exposure to cutting edge robot integration technologies and Siemens hardware and software.