Unsupervised machine learning for flaw detection in automated ultrasonic testing of carbon fibre reinforced plastic composites.

The use of Carbon Fibre Reinforced Plastic (CFRP) composite materials for critical components has significantly surged within the energy and aerospace industry. With this rapid increase in deployment, reliable post-manufacturing Non-Destructive Evaluation (NDE) is critical for verifying the mechanical integrity of manufactured components. To this end, an automated Ultrasonic Testing (UT) NDE process delivered by an industrial manipulator was developed, greatly increasing the measurement speed, repeatability, and locational precision, while increasing the throughput of data generated by the selected NDE modality.

Three-Dimensional Residual Neural Architecture Search for Ultrasonic Defect Detection.

This study presents a deep-learning (DL) methodology using 3-D convolutional neural networks (CNNs) to detect defects in carbon fiber-reinforced polymer (CFRP) composites through volumetric ultrasonic testing (UT) data. Acquiring large amounts of ultrasonic training data experimentally is expensive and time-consuming. To address this issue, a synthetic data generation method was extended to incorporate volumetric data.

Transforming Industrial Manipulators via Kinesthetic Guidance for Automated Inspection of Complex Geometries.

The increased demand for cost-efficient manufacturing and metrology inspection solutions for complex-shaped components in High-Value Manufacturing (HVM) sectors requires increased production throughput and precision. This drives the integration of automated robotic solutions. However, the current manipulators utilizing traditional programming approaches demand specialized robotic programming knowledge and make it challenging to generate complex paths and adapt easily to unique specifications per component, resulting in an inflexible and cumbersome teaching process.

Collaborative Robotic Wire + Arc Additive Manufacture and Sensor-Enabled In-Process Ultrasonic Non-Destructive Evaluation.

The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc Additive Manufacturing (WAAM). WAAM enables automated, time- and material-efficient manufacturing of metal parts. To strengthen these benefits, the demand for robotically deployed in-process Non-Destructive Evaluation (NDE) has risen, aiming to replace current manually deployed inspection techniques after completion of the part.

Sensor-Enabled Multi-Robot System for Automated Welding and In-Process Ultrasonic NDE.

The growth of the automated welding sector and emerging technological requirements of Industry 4.0 have driven demand and research into intelligent sensor-enabled robotic systems. The higher production rates of automated welding have increased the need for fast, robotically deployed Non-Destructive Evaluation (NDE), replacing current time-consuming manually deployed inspection.