Underpinning UK High-Value Manufacturing: Development of a Robotic Re-manufacturing System

Impact and its measure of outcome is a given performance indicator within academia. Impact metrics and the associated understanding play a large part of how academic research is judged and ultimately funded. Natural progression of successful scientific research into industry is now an essential tool for academia. This paper describes what began over ten years ago as a concept to automate a bespoke welding system, highlighting its evolution from the research laboratories of The University of Sheffield to become a platform technology for aerospace remanufacturing developed though industry-academia collaboration. The design process, funding mechanisms, research and development trials and interaction between robotic technology and experienced welding engineers has made possible the construction of a robotic aerospace turbofan jet engine blade re-manufacturing system. This is a joint collaborative research and development project carried out by VBC Instrument Engineering Limited (UK) and The University of Sheffield (UK) who are funded by the UK governments’ innovation agency, Innovate-UK with the Aerospace Technology Institute, the Science and Facilities Technology Council (STFC) and the Engineering and Physical Sciences Research Council (EPSRC)

Intelligent Sensing for Robotic Re-Manufacturing in Aerospace - An Industry 4.0 Design Based Prototype

Emerging through 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 ìInternet of Thingsî (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

Process monitoring and industrial informatics for on-line optimization of Welding Procedure Specifications (WPS) in Gas Tungsten Arc Welding (GTAW) – Industry 4.0 for robotic additive re-manufacturing of aeroengine components

Industry 4.0, the scheme that drives the fourth industrial revolution, has been, since its conception, reshaping the manufacturing industry. To advance current industrial chains into the smart factories of the future, cyber-physical systems are monitored and communicate with each other to ensure transparent interoperability, giving birth to the emerging field of industrial informatics. To enable the repair and recycling of high value jet engine compressor blades, additive manufacturing is utilized. The complex geometries and asymmetrical wear of the blades require robotic welding systems to be trained by experienced human welding engineers in order to be able to adapt to differing components. Demonstrated in this paper, process monitoring and industrial informatics are introduced to the adaption of Welding Procedure Specifications (WPS) utilized by a developing robotic system for the additive re-manufacturing of aeroengine components. Using a novel variant of Gas Tungsten Arc Welding (GTAW), the robotic welding system under development is a product of an industry-academia collaboration between the Enabling Sciences for Intelligent Manufacturing Group (ESIM) of the University of Sheffield and VBC Instrument Engineering Ltd

Transfer Analysis of Human Engineering Skills for Adaptive Robotic Additive Manufacturing in the Aerospace Repair and Overhaul Industry

The desire for smart “lights out factories” which can autonomously produce components for high value manufacturing industries is described by the Industry 4.0 solution. This manufacturing methodology is appropriate for newly designed components, which take advantage of modern materials, robotic and automation processes, but not necessarily applicable to overhaul and repair. The aerospace overhaul and repair industry remains heavily dependent on human engineering skills to develop repair and re-manufacturing techniques for complex components of high value. Development of any advanced, intelligent multi-agent robotic additive re-manufacturing system requires correct interrogation of metallic materials thermal properties, system control and output. Advanced programming of robots, data interpretation from associated sensory and feedback systems are required to mirror human input. Using process analysis to determine stimuli, replacement of human sensory receptors with electronic sensors, vision systems and high-speed data acquisition and control systems allows for the intelligent fine tuning of multiple heat input parameters to deposit the additive material at any one time. The interaction of these key components combined with novel robotic technology and experienced welding engineers has made possible the construction of a disruptive robotic re-manufacturing technology. This paper demonstrates the design process and analyses the outputs sourced from observation and the recording of highly skilled human engineers when conducting manual remanufacturing and repair techniques. This data is then mined for the transferable control input parameters required to replicate and improve human performance. This industry-academia research intensive collaboration between VBC Instrument Engineering Limited (UK) and The University of Sheffield has received project funding from the Engineering and Physical Sciences Research Council (EPSRC, 2006–2010), the Science and Facilities Technology Council (STFC, 2011–2013) and Innovate-UK with the Aerospace Technology Institute (2014–2018)

A feasibility study comparing two commercial TIG welding machines for deep penetration

Developing deep penetration TIG welding to produce welds of equal quality to the industrial standard practise of laser-based welding techniques has the potential to lower production complexity and cost. The detrimental effects of the necessary higher currents required to increase penetration depth in conventional TIG welding have been shown to be circumvented through K-TIG and A-TIG techniques. However, prior experimental work on weld pool dynamics in conventional TIG welding in higher current regions has been sparse as TIG welding enhanced through novel techniques provides the best quality welds. This paper is an early feasibility study for novel deep penetration welding techniques motivated by observations made during research done at The University of Sheffield where novel activity in the weld pool was identified during TIG welding with a VBC IE500DHC between 300A – 1000A. This current range is labelled the ‘Red Region’. Understanding the weld pool dynamics in the ‘Red Region’ allows the potential exploration of novel techniques for deep penetration TIG welding. Addressing this, the paper compares the quality of welds produced between 100A and 200A on 316 Stainless Steel by two industrially leading welding machines; the Miller Dynasty 350 and the VBCie 500DHC

Advanced Real-Time Weld Monitoring Evaluation Demonstrated with Comparisons of Manual and Robotic TIG Welding Used in Critical Nuclear Industry Fabrication

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 often-lengthy 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 real-time 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

Upgrade to the Birmingham Irradiation Facility

The Birmingham Irradiation Facility was developed in 2013 at the University of Birmingham using the Medical Physics MC40 cyclotron. It can achieve High Luminosity LHC (HL-LHC) fluences of 1015 (1 MeV neutron equivalent (neq)) cm-2 in 80 s with proton beam currents of 1 μA and so can evaluate effectively the performance and durability of detector technologies and new components to be used for the HL-LHC. Irradiations of silicon sensors and passive materials can be carried out in a temperature controlled cold box which moves continuously through the homogenous beamspot. This movement is provided by a pre-configured XY-axis Cartesian robot scanning system. In 2014 the cooling system and cold box were upgraded from a recirculating glycol chiller system to a liquid nitrogen evaporative system. The new cooling system achieves a stable temperature of -50 °C in 30 min and aims to maintain sub-0 °C temperatures on the sensors during irradiations. This paper reviews the design, development, commissioning and performance of the new cooling system

Development of a vision system for TIG welding - a work-in-progress study

The development of a Vision System for TIG Welding has the potential to help realize a real time process monitoring system for joining tasks which require automated welding. A key application of this technique is in the Nuclear Industry; where industrial components require several passes (layers of welding) to achieve robust joints. Through monitoring a welding process such as this in real time, material and time waste could be drastically reduced as faults could be instantly identified. A TIG welding arc is a very intense source of both light and heat, making the creation of a vision system for it challenging. Higher currents result in; brighter TIG welding arcs, higher energy input and deeper and wider weld pools. Nuclear industry applications require deep penetration welding but bright TIG welding arcs can overwhelm the intensity of an auxiliary illumination laser reducing the image clarity of an observing camera system. Thus, a balance between a wide weld bead with clear features applicable to deep penetration but without a brightness level which overwhelms that of the laser must be found. This paper is a Work-in-Progress study of a vision system for TIG welding using an automated TIG welding system and a camera with a laser illumination system. Welding was performed using a Miller Dynasty 350 at 100A with a 3B class laser used to illuminate the weld pool

Evaluating the Radiation Tolerance of a Robotic Finger

In 2024, The Large Hadron Collider (LHC) at CERN will be upgraded to increase its luminosity by a factor of 10 (HL-LHC). The ATLAS inner detector (ITk) will be upgraded at the same time. It has suffered the most radiation damage, as it is the section closest to the beamline, and the particle collisions. Due to the risk of excessive radiation doses, human intervention to decommission the inner detector will be restricted. Robotic systems are being developed to carry out the decommissioning and limit radiation exposure to personnel. In this paper, we present a study of the radiation tolerance of a robotic finger assessed in the Birmingham Cyclotron facility. The finger was part of the Shadow Grasper from Shadow Robot Company, which uses a set of Maxon DC motors

Robotic additive manufacturing system featuring wire deposition by electric arc for high-value manufacturing

Increasing demand from the high-value manufacturing industries of quality, productivity, efficiency and security aligns with the ambition and driving need for novel automated robotic systems. This paper describes the motivation, design and implementation phases of the SERFOW project (Smart Enabling Robotics driving Free Form Welding). SERFOW is an automated additive manufacturing arc and wire tungsten inert gas (TIG) welding prototype to support industrial manufacturing requirements of the nuclear, aerospace and automotive industry sectors. Key innovations are found in the integration of a 3D vision system with a robotic manipulator to perform automatic free-form fusion welding for the multiple layer additive material build-up required to expand Additive Manufacturing (AM) with minimum human intervention. Welding trials were performed on samples made of Super Duplex stainless steel alloy. Metallographic observations were performed to analyze the porosity distribution and penetration on the material after welding. Also, temperature, feritescope and tensile measurements were performed. The results showed that the welding and AM process performed with the SERFOW cell are within an acceptable quality tolerance range according to the ISO 5817 and the ASME A789 welding standards