3-DUSSS: 3-Dimensional Ultrasonic Self Supervised Segmentation

This study introduces a novel self-supervised learning approach for volumetric segmentation of defect indications captured by phased array ultrasonic testing data from Carbon Fiber Reinforced Polymers (CFRPs). By employing this self-supervised method, defect segmentation is achieved automatically without the need for labelled training data or examples of defects. The approach has been tested using artificially induced defects, including back-drilled holes and Polytetrafluoroethylene (PTFE) inserts, to mimic different defect responses. Additionally, it has been evaluated on stepped geometries with varying thickness, demonstrating impressive generalization across various test scenarios. Minimal preprocessing requirements are needed, with no removal of geometric features or Time-Compensated Gain (TCG) necessary for applying the methodology. The model's performance was evaluated for defect detection, in-plane and through-thickness localisation, as well as defect sizing. All defects were consistently detected with thresholding and different processing steps able to remove false positive indications for a 100% detection accuracy. Defect sizing aligns with the industrial standard 6 dB amplitude drop method, with a Mean Absolute Error (MAE) of 1.41 mm. In-plane and through-thickness localisation yielded comparable results, with MAEs of 0.37 and 0.26 mm, respectively. Visualisations are provided to illustrate how this approach can be utilised to generate digital twins of components.

A quantitative investigation for deployment of mobile collaborative robots in high-value manufacturing

Component inspection is often the bottleneck in high-value manufacturing, driving industries like aerospace toward automated inspection technologies. Current systems often employ fixed arm robots, but they lack the flexibility in adapting to new components or orientations Advanced mobile robotic platforms with updated sensor technologies and algorithms have improved localization and path planning capabilities, making them ideal for bringing inspection processes directly to parts. However, mobile platforms introduce challenges in localization and maneuverability, leading to potential errors. Their positional uncertainty is higher than fixed systems due to the lack of a fixed calibrated location, posing challenges for position-sensitive inspection sensors. Therefore, it's essential to assess the positional accuracy and repeatability of mobile manipulator platforms. The KUKA KMR iiwa was chosen for its collaborative features, robust build, and scalability within the KUKA product range. The accuracy and repeatability of the mobile platform were evaluated through a series of tests to evaluate the performance of its integrated feature mapping, the effect of various speeds on positional accuracy, and the efficiency of the omnidirectional wheels for a range of translation orientations. Experimental evaluation revealed that enabling feature mapping substantially improves the KUKA KMR iiwa's performance, with accuracy gains and error reductions exceeding 90%. Repeatability errors were under 7 mm with mapping activated and around 2.5 mm in practical scenarios, demonstrating that mobile manipulators, incorporating both the manipulator and platform, can fulfil the precise requirements of industries with high precision needs. Providing a highly diverse alternative to traditional fixed-base industrial manipulators.