Reconfigurable robotic automation systems are a key part of the next industrial revolution, giving companies the flexibility to customise a multitude of manufacturing operations according to demand at low cost. Under the most demanding conditions, robot performance can vary when being used in a reconfigurable fashion. This project quantifies this variability for a new nuclear pressure vessel machining method.
Portable robotic machine tools potentially allow feature machining processes to be brought to large parts in various industries, creating an opportunity for capital expenditure and operating cost reduction. However, robots lack the machining capability of conventional equipment, which ultimately results in dimensional errors in parts. This project showcased a low-cost hexapod-based robotic machine tool and undertook experimental research to investigate how the widely researched robotic machining challenges, e.g. structural dynamics and kinematics, translate to achievable tolerance ranges in flexible real-world production. This project highlighted the degree to which tolerance conformity varied according to plausible robot reconfigurations for various machined features and use cases.
Machining trials assessed the total dimensional errors in the final part over multiple geometries. A key finding was error variation which is in the sub-millimetre range, although, in some cases, upper tolerance limits <100 microns were achieved. Practical challenges were also noted. Most significantly, it was demonstrated that dimensional machining error is mainly systematic in nature and therefore that the total error could be dramatically reduced with in situ measurement and compensation. Potential was therefore found to achieve a flexible, high-performance robotic machining capability despite complex and diverse underlying scientific challenges.
Overall, the work undertaken highlighted achievable tolerances in low-cost robotic machining and opportunities for improvement, also providing a practical benchmark useful for process selection in reconfigurable manufacturing.
This work was published in The International Journal of Advanced Manufacturing Technology in 2017. For more information please contact us or see the full article: