Autonomous Inspection in Manufacturing & Remanufacturing (AIMaReM)

High value manufacturing is an essential component of the UK economy, contributing strongly to our economic prosperity and engineering status around the world. The growth in high value manufacturing to support aerospace, nuclear and other high integrity engineering components, has placed huge pressure on the rapid delivery of reliable and high quality Non-Destructive Evaluation (NDE) to inspect these parts. Currently, much inspection of safety critical components (sometimes requiring 100% part inspection) is performed manually, leading to significant bottlenecks associated with the NDE. Existing robots typically follow pre-programmed paths making them unsuitable to handle, inspect and disassemble parts with a significant tolerance or variability. A new end-to-end approach is needed, embracing manufacture, transport through factory, parts alignment, parts tracking, and inspection (both surface form metrology and NDE) with the associated high volume data management feeding into the quality and assurance compliance processes.
Exactly the same process bottlenecks occur when we translate the problem to the regime of Remanufacturing, hence the integrated approach taken through this proposal. Remanufacturing has been identified as being central to the creation of economic growth in the UK and global markets. With supplies of resources and energy limited, the transition to a low carbon economy with strong emphasis on resource efficiency is key to the UK's Industrial Strategy. Remanufacturing can support this transition by achieving significant impact in all industrial sectors through preventing waste, improving resource management, generating sustainable economic growth, increasing productivity and enhancing competitiveness.

AIMaReM (Autonomous Inspection in Manufacturing& Remanufacturing) provides a unique combination of data collection, processing and visualisation tools combined with efficient robot path planning and obstacle avoidance, with a focus on manufacturing inspection (NDE and surface form metrology). The project will deliver an automated, systems integrated solution, that will be of direct benefit to the manufacturing sector to allow faster integrated inspection and parts handling, thus saving time, and reducing costs whilst enhancing quality and throughput.

The impact of the AIMaReM project research will be quickly felt through uptake of new technologies and approaches by a range of industry. We identify specific application areas in the proposal including Aerospace Manufacturing (SPIRIT Aerosystems and RCNDE through industrial membership NDEvR, including Rolls Royce and TWI), Remanufacturing (Autocraft Drivetrains) and Nuclear Inspection (National Nuclear Laboratory). These areas represent key UK manufacturing and infrastructure assets. Aerospace and automotive manufacturing are at opposite ends of the spectrum, aerospace lifecycle typically being 50 years and high value, with automotive being lower cost, shorter lifecycle but with higher volumes. We have chosen to focus on Remanufacturing for the automotive sector to investigate the additional benefits to the environment and carbon commitments bythe introduction of the new technologies.
The underpinning research targets a number of areas that are still highly manual in operation, and as such provide barriers to throughput in the manufacturing sector (and hence increase cost). Our main focus (reflected in the partnerships), lies in high value aerospace manufacturing technology, where integration of quality and safety critical inspection procedures into the manufacturing cell is increasingly demanded from the manufacturers. Both surface form quality (for performance and assembly integrity) and internal defect compliance monitoring (through conventional NDE) are demanded by industry, and all the major UK aerospace manufacturers have a common desire to decrease inspection cycle time and improve throughput to cope with the projected demands for aerospace structures. Manual interpretation of data, with associated quality and coverage issues is not sufficient for parts demanding 100% inspection coverage. The drive is to full automation of the whole inspection process for large components (this includes transport through factory, alignment into automated process cells, delivery of surface form measurements, delivery of NDT measurements and handling the large data sets produced by these measurement techniques in real time). Although our initial motivation is therefore aerospace manufacturing, it is clear that the autonomous techniques developed translate directly into the automotive Remanufacturing environment. Here part transport, recognition and orientation are just as relevant as for aerospace, but with a lower emphasis on NDE. Real time adaptive robot path planning and obstacle avoidance must be seamlessly integrated into the measurement tasks - the ability for in process loop modification of path and measurement strategy is an essential characteristic for automating the inspection procedure. Building the requisite intelligence into the data compression, processing, display and archiving are all core priorities.
Finally we note that the autonomous approaches developed in the proposal will translate well into the arena of asset inspection. We have chosen to demonstrate this initially by focussing on collaboration with the National Nuclear Laboratory and Sellafield sites (both existing partners through EPSRC and Innovate UK funded projects). Demonstrating our range of robotic delivery and control platforms at the new Workington facilities is our strategy, and realistic due to numbers of the team having already deployed semi-autonomous inspection vehicles on site at Sellafield after proving through Workington.
Of course the academic team will pursue the conventional impact and dissemination activities through academic journal and conference publications. The proposed international collaborations will allow the research to have strong exposure in the EU robotics arena, leading to new knowledge exchange and joint research activities funded through the current Horizon 2020 framework.