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.

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Maintenance, which is critical for safe, reliable, quality, and cost-effective service, plays a dominant role in the railway industry. Therefore, this paper examines the importance and applications of Robotic and Autonomous Systems (RAS) in railway maintenance. More than 70 research publications, which are either in practice or under investigation describing RAS developments in the railway maintenance, are analysed. It has been found that the majority of RAS developed are for rolling-stock maintenance, followed by railway track maintenance. Further, it has been found that there is growing interest and demand for robotics and autonomous systems in the railway maintenance sector, which is largely due to the increased competition, rapid expansion and ever-increasing expenses.

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Ultrasonic Non-Destructive Evaluation using Full Matrix Capture (FMC) and Total Focusing Method (TFM) is used for high resolution imaging as every pixel is in optimal focus. FMC excites one element in turn, so operates with lower transmitted energy compared to phased array beamforming. The energy at a reflector is further reduced by the broad directivity pattern of the single element. The large number of Tx/Rx A-scans that contribute to each pixel recover the Signal-to-Noise Ratio (SNR) in the final TFM image. Maintaining this in the presence of attenuating materials is a challenge because relevant information in each Ascan signal is buried in the thermal noise, and the TFM process assumes no quantization effects in the Analogue-to-Digital Converters (ADCs) in each receiver. In-process inspection during Additive Manufacturing (AM) requires ultrasonic array sensors that can tolerate high temperatures, scan over rough surfaces and leave no residue. Dry-coupled wheel probes are a solution, but the tire rubbers are often highly attenuating, causing a problem for FMC+TFM needed to adapt the focus through the rough surface. Common approaches to maintain the SNR are to drop the frequency or to average over multiple transmissions, but these compromise resolution and acquisition rate respectively. In this paper, the application of coded excitation to maintain the SNR in the presence of high signal attenuation is explored.