The use of hybrid manufacturing in the repair and maintenance of railway lines

The defect repair process within the UK rail industry has seen significant improvements over the past decade with the introduction of new measurement systems and defect detection systems. Although significant work has been made on the initial defect identification, little work has been done on the process after the initial defect has been detected, with the repair process utilised by Network Rail still being an extremely manual process. This manual process generates very little traceability and transparency during and after the process has been completed. When this is compounded with the high cost of repairing and maintaining the railway infrastructure, estimated to be between €30 000 and €100 000 per km per year for a modern European railway line, there is a clear opportunity to introduce some levels of automation within the rail repair process. This need has also be reiterated by Network Rail within their welding process and development report, where they state that automation of the welding and grinding processes within the repair procedures is of paramount importance. The purpose of this thesis is to discuss the use of an automated hybrid manufacturing system to maintain and repair railway lines. The project focusses on using an industrial robotic arm to repair surface defects found on the rail head. Work has been completed on utilising hybrid manufacturing using an industrial robotic arm in order to identify the location and size of a squat defect utilising various path generation and defect detection algorithms. On identifying the size and location of the defect the defective area is removed using adaptive grinding process and subsequent path generation algorithms create a weld preparation site in line with British Standard BS15594:2009. New material is then deposited using laser scan data to generate the deposition path for the robot arm and finally the same adaptive grinding system, utilised to remove the defect, is used to remove excess material in order to match the wear pattern either side of the previously defective area. The work presented in this thesis has shown the successful implementation of an autonomous rail repair system which is capable of locally identifying squat defects using a novel detection algorithm. Furthermore, this thesis has created novel path generation algorithms which can be drive the robotic arm and along with the tool in order to complete each stage of the repair process. Finally a Novel framework was been developed in line with the repair process which describes how each subsystem operates and further defines the order of operations which is required to complete the repair process.