Abstract-Emergingthrough an industry-academia collaboration between the University of Sheffield and VBC Instrument Engineering Ltd, a proposed robotic solution for remanufacturing of jet engine compressor blades is under ongoing development, producing the first tangible results for evaluation. Having successfully overcome concept adaptation, funding mechanisms, design processes, with research and development trials, the stage of concept optimization and end-user application has commenced. A variety of new challenges is emerging, with multiple parameters requiring control and intelligence. An interlinked collaboration between operational controllers, Quality Assurance (QA) and Quality Control (QC) systems, databases, safety and monitoring systems, is creating a complex network, transforming the traditional manual re-manufacturing method to an advanced intelligent modern smart-factory. Incorporating machine vision systems for characterization, inspection and fault detection, alongside advanced real-time sensor data acquisition for monitoring and evaluating the welding process, a huge amount of valuable industrial data is produced. Information regarding each individual blade is combined with data acquired from the system, embedding data analytics and the concept of "Internet of Things" (IoT) into the aerospace re-manufacturing industry. The aim of this paper is to give a first insight into the challenges of the development of an Industry 4.0 prototype system and an evaluation of first results of the operational prototype.

For the last decade, additive manufacturing (AM) has been revolutionising the aerospace industry, building and repairing various components for aircrafts and outer space vehicles. Despite the fact that AM is gaining rapid adoption by the industry, it is still considered a developing technology, with ongoing research in a variety of fields. Wire arc additive manufacturing (WAAM), a welding-based AM technology, is an active field of research as well, because it enables economical production of largescale metal components with relatively high deposition rates. In this article, the effects on the weldbead geometry and heat affected zone from high and low frequency pulsed current are explored on Gas Tungsten Arc Welding (GTAW). The materials used in this investigation were selected to be Ti-6-4 and Inconel 718, both highly used in the aerospace industry for their high strength-to-weight ratio and strength at elevated temperatures respectively. The design of the experiments followed a Taguchiinspired orthogonal array, altering, apart from the current modes and values, the torch travel speed driven by an industrial robotic arm as well as the wire-feeding rate.The results demonstrate the ability to control both the weld-bead dimensions and penetration depth, as well as the heat affected regions, by utilizing the dual pulsing combination of both high and low frequency pulsing. Alterations from wide beads with deep penetration to narrower beads with greater height-to-width ratios are demonstrated in a single manufacturing setup, enabling further development of the WAAM process.

This is a repository copy of Welding process monitoring applications and industry 4.0.

Abstract. Ensuring critical welded joint quality and repeatability is largely dependent on robust, well-designed Welding Procedure Specifications (WPS). Highly skilled manual welding engineers automatically recognise many imperfections, adjusting their responses according to inputs from vision, smell and sounds made during the welding process. Unfortunately, exceptional human ability does not guarantee performance when less predictable influences occur during welding processes. Human error and materials imperfections can result in defective welds for critical applications, commonly attributed to material surface impurities and contamination. Fault detection is problematic; the only finite method of weld testing is destructive testing which is not applicable to final product verification. Quality assurance and control is used to guarantee the welding process repeatability by production of a Procedure Qualification Record. This oftenlengthy approval process restricts welding technology and materials application advancement. An alternative method of testing is the detection of flaws and defects in real-time to allow immediate process corrections. Development of real time welding evaluation instrumentation requires welding process parameters measurements combined with high-speed data processing. This real time monitoring and evaluation produces a weld defect fingerprint used to determine quality. We aim to highlight variations found in welding process quality using realtime monitoring and assess if it is within the acceptable standards for nuclear applications. To achieve this, we first must understand the human welding engineer using data taken from a series of manual weld trials. The trials use a common welding operation found in nuclear reactor pressure vessels. Reference data comparisons are made using identical trials with robotic welding equipment. Trial comparison results indicate that real time evaluation of welding processes detects flaws in weld quality. We then demonstrate how applications of welding process parameters are exceptionally effective methods for the control of robotic welding applications.