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. This paper presents the development and deployment of a novel multi-robot system for automated welding and in-process NDE. Full external positional control is achieved in real time allowing for on-the-fly motion correction, based on multi-sensory input. The inspection capabilities of the system are demonstrated at three different stages of the manufacturing process: after all welding passes are complete; between individual welding passes; and during live-arc welding deposition. The specific advantages and challenges of each approach are outlined, and the defect detection capability is demonstrated through inspection of artificially induced defects. The developed system offers an early defect detection opportunity compared to current inspection methods, drastically reducing the delay between defect formation and discovery. This approach would enable in-process weld repair, leading to higher production efficiency, reduced rework rates and lower production costs.

Abstract-Lameness is a significant problem for performance horses and farmed animals, with severe impact on animal welfare and treatment costs. Lameness is commonly diagnosed through subjective scoring methods performed by trained veterinary clinicians, but automatic methods using suitable sensors would improve efficiency and reliability. In this paper, we propose the use of radar micro-Doppler signatures for contactless and automatic identification of lameness, and present preliminary results for dairy cows, sheep, and horses. These proof-of-concept results are promising, with classification accuracy above 85% for dairy cows, around 92% for horses, and close to 99% for sheep.

This paper presents the first initial results of using radar raw I & Q data and range profiles combined with Long Short Term Memory layers to classify human activities. Although tested only on simple classification problems, this is an innovative approach that enables to bypass the conventional usage of Doppler-time patterns (spectrograms) as inputs of the LSTM layers, and adopt instead sequences of range profiles or even raw complex data as inputs. A maximum 99.56% accuracy and a mean accuracy of 97.67% was achieved by treating the radar data as these time sequences, in an effective scheme using a deep learning approach that did not require the pre-processing of the radar data to generate spectrograms and treat them as images. The prediction time needed for a given input testing sample is also reported, showing a promising path for real-time implementation once the LSTM network is properly trained.

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. This work presents a synchronized multi-robot WAAM and NDE cell aiming to achieve (1) defect detection in-process, (2) enable possible in-process repair and (3) prevent costly scrappage or rework of completed defective builds. The deployment of the NDE during a deposition process is achieved through real-time position control of robots based on sensor input. A novel high-temperature capable, dry-coupled phased array ultrasound transducer (PAUT) roller-probe device is used for the NDE inspection. The dry-coupled sensor is tailored for coupling with an as-built high-temperature WAAM surface at an applied force and speed. The demonstration of the novel ultrasound in-process defect detection approach, presented in this paper, was performed on a titanium WAAM straight sample containing an intentionally embedded tungsten tube reflectors with an internal diameter of 1.0 mm. The ultrasound data were acquired after a pre-specified layer, in-process, employing the Full Matrix Capture (FMC) technique for subsequent post-processing using the adaptive Total Focusing Method (TFM) imaging algorithm assisted by a surface reconstruction algorithm based on the Synthetic Aperture Focusing Technique (SAFT). The presented results show a sufficient signal-to-noise ratio. Therefore, a potential for early defect detection is achieved, directly strengthening the benefits of the AM process by enabling a possible in-process repair.

Welds are currently only inspected after all the passes are complete and after allowing sufficient time for any hydrogen cracking to develop, typically over several days. Any defects introduced between passes are therefore unreported until fully buried, greatly complicating rework and also delaying early corrections to the weld process parameters. In-process inspection can provide early intervention but involves many challenges, including operation at high temperatures with significant gradients affecting acoustic velocities and, hence, beam directions. Reflections from the incomplete parts of the weld would also be flagged as lack-of-fusion defects, requiring the region of interest (ROI) to adapt as the weld is built up. The collaborative SIMPLE (SIngle Manufacturing PLatform Environment) project addresses these challenges by incorporating robotic inspection within a robotic tungsten inert gas (TIG) welding cell. This has been accomplished initially with commercial off-the-shelf ultrasonic phased arrays, but is flexible enough to adapt to future developments with solutions suitable for higher temperatures. The welding and inspection robots operate autonomously. The former can introduce deliberate defects to validate the latter, which uses 5 MHz 64-element phased arrays on high-temperature wedges to generate sector scans after each weld pass. The results are presented, confirming that the challenges have been addressed and demonstrating the feasibility of this approach.