The UK is home to several major air commercial and transport hubs. As a result, there is a high demand for Maintenance, Repair, and Overhaul (MRO) services to ensure that fleets of aircraft are in airworthy conditions. MRO services currently involve heavy manual labor. This creates bottlenecks, low repeatability, and low productivity. Presented in this paper is an investigation to create an automation cell for the fan-blade reconditioning component of MRO. The design and prototype of the automation cell is presented. Furthermore, a digital twin of the grinding process is developed and used as a tool to explore the required grinding force parameters needed to effectively remove surface material. An integration of a 6-DoF industrial robot with an end-effector grinder and a computer vision system was undertaken. The computer vision system was used for the digitization of the fan-blade surface as well as tracking and guidance of material removal. Our findings reveal that our proposed system can perform material removal, track the state of the fan blade during the reconditioning process and do so within a closed-loop automated robotic work cell.

Titanium alloys are commonly heat-treated to meet specific design requirements. In an effort to possibly create a better composite, the influence of heat treatments on the damage evolution and strength of a SiC/Ti-15-3 metal matrix composite (MMC) was studied. Heat treatments of 450°C and 700°C for 24 hours were performed on axial and transverse unidirectional specimens. These specimens, in addition to specimens in the as-received condition, were tested under nonproportional loading paths and then microstructurally analyzed to determine the induced damage. The axial composite with the 450°C heat treatment showed the highest elastic modulus and the lowest stiffness reduction than the other heat treatment conditions. The transverse composite in the as-received condition showed the highest room temperature elastic modulus and the lowest stiffness reduction compared with other heat treatment conditions. Typical damage modes of Ti MMC’s, such as fiber/matrix debonding and matrix microcracking, were seen in all heat treatments. A micromechanics model based on the Mori-Tanaka averaging scheme was implemented to simulate the effects of micro-cracking induced damage on composite stiffness reduction. [S0094-4289(00)01101-4]

Within aerospace and automotive manufacturing, the majority of quality assurance is through inspection or tests at various steps during manufacturing and assembly. Such tests do not tend to capture or make use of process data for in-process inspection and certification at the point of manufacture. Inspection of the product during manufacturing can potentially detect defects, thus allowing consistent product quality and reducing scrappage. However, a review of the literature has revealed a lack of any significant research in the area of inspection during the manufacturing of terminations. This work utilises infrared thermal imaging and machine learning techniques for inspection of the enamel removal process on Litz wire, typically used for aerospace and automotive applications. Infrared thermal imaging was utilised to inspect bundles of Litz wire containing those with and without enamel. The temperature profiles of the wires with or without enamel were recorded and then machine learning techniques were utilised for automated inspection of enamel removal. The feasibility of various classifier models for identifying the remaining enamel on a set of enamelled copper wires was evaluated. A comparison of the performance of classifier models in terms of classification accuracy is presented. The best model for enamel classification accuracy was the Gaussian Mixture Model with expectation maximisation; it achieved a training accuracy of 85% and enamel classification accuracy of 100% with the fastest evaluation time of 1.05 s. The support vector classification model achieved both the training and enamel classification accuracy of more than 82%; however, it suffered the drawback of a higher evaluation time of 134 s.