Many high-performance metal parts users extend the service of these damaged parts by employing repair technology. Hybrid manufacturing, which includes additive manufacturing (AM) and subtractive manufacturing, provides greater build capability, better accuracy, and surface finish for component repair. However, most repair processes still rely on manual operations, which are not satisfactory in terms of time, cost, reliability, and accuracy. This dissertation aims to improve the application of hybrid manufacturing for repairing metallic components by addressing the following three research topics. The first research topic is to investigate and develop an efficient best-fit and shape adaption algorithm for automating 3D models\u27 the alignment and defect reconstruction. A multi-feature fitting algorithm and cross-section comparison method are developed. The second research topic is to develop a smooth toolpath generation method for laser metal deposition to improve the deposition quality for metallic component fabrication and repair. Smooth connections or transitions in toolpath planning are achieved to provide a constant feedrate and controllable deposition idle time for each single deposition pass. The third research topic is to develop an automated repair process could efficiently obtain the spatial information of a worn component for defect detection, alignment, and 3D scanning with the integration of stereo vision and laser displacement sensor. This dissertation investigated and developed key technologies to improve the efficiency, repair quality, precision, and automation for the repair of metallic components using hybrid manufacturing. Moreover, the research results of this dissertation can benefit a wide range of industries, such as additive manufacturing, manufacturing and measurement automation, and part inspection --Abstract, page iv
