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

Abstract

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.

Intelligent sensing for robotic re-manufacturing in aerospace — An industry 4.0 design based prototype

“…The High-Speed DAQ system (Section III-D) has been used to evaluate welding operations in real-time for the ATLAS Experiment in CERN [4], [5], [6], and for nuclear applications at Nuclear AMRC [7]. The DAQ system is not only able to successfully detect welding process flaws, but can also detect subtle deviation in flaws originating from different events.…”

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

“…Response to GTAW technique variation requires intelligence which has proven exceptionally challenging to develop for the complex profile (curved) and super alloy materials (heat input and distortion) now utilized by aerospace engine manufacturers. Experienced welding engineers dynamic inputs alter the weld deposition or AM characteristics (size, shape, depth and micro-structure) by varying parameters termed CLAMS (current, length, angle, manipulation and speed) [3,10]. They use their knowledge and apply their skills almost automatically by subtly fine tuning CLAMS to achieve the required result.…”

“…Measurements were performed using bespoke high speed sensors [10,12]. The measurement signals have been filtered in real-time with a low-pass filter and cutoff frequency of 10 Hz.…”

“…The aim of the trials was to firstly monitor the welding process in real-time, to be able to predict the quality of a manual weld, detecting errors and defects as they occur. The results of this research were presented [10] and this current research follows the transfer of the data collected during the trials to advance the robotic welding systems adaptability and performance. Figure 1a shows the welding voltage and current obtained while welding two stainless steel plates with 152 mm length at 1.25 mm/s welding speed.…”

Process monitoring and industrial informatics for online optimization of Welding Procedure Specifications (WPS) in Gas Tungsten Arc Welding (GTAW) – Industry 4.0 for robotic additive remanufacturing of aeroengine components

“…With these limitations in mind University of Sheffield developed a variant of their high-speed data acquisition system based on arc sensing, for real-time monitoring of the welding process. By measuring current and voltage at a very high sampling rate, the system has been experimentally proven to be able to detect variations in GTAW processes that correspond to defects and poor quality of welds [18,19].…”