Exploring Infrared Sensoring for Real Time Welding Defects Monitoring in GTAW

This paper presents an evaluation of an infrared sensor for monitoring the welding pool temperature in a Gas Tungsten Arc Welding (GTAW) process. The purpose of the study is to develop a real time system control. It is known that the arc welding pool temperature is related to the weld penetration depth; therefore, by monitoring the temperature, the arc pool temperature and penetration depth are also monitored. Various experiments were performed; in some of them the current was varied and the temperature changes were registered, in others, defects were induced throughout the path of the weld bead for a fixed current. These simulated defects resulted in abrupt changes in the average temperature values, thus providing an indication of the presence of a defect. The data has been registered with an acquisition card. To identify defects in the samples under infrared emissions, the timing series were analyzed through graphics and statistic methods. The selection of this technique demonstrates the potential for infrared emission as a welding monitoring parameter sensor

Model-free adaptive iterative learning control of melt pool width in wire arc additive manufacturing

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Wire arc additive manufacturing (WAAM) is a Direct Energy Deposition (DED) technology, which utilize electrical arc as heat source to deposit metal material bead by bead to make up the final component. However, issues like the lack of assurance in accuracy, repeatability and stability hinder the further application in industry. Therefore, a Model Free Adaptive Iterative Learning Control (MFAILC) algorithm was developed to be applied in WAAM process in this study. The dynamic process of WAAM is modelled by adaptive neuro fuzzy inference system (ANFIS). Based on this ANFIS model, simulations are performed to demonstrate the effectiveness of MFAILC algorithm. Furthermore, experiments are conducted to investigate the tracking performance and robustness of the MFAILC controller. This work will help to improve the forming accuracy and automatic level of WAAM

In-process phased array ultrasonic weld pool monitoring

In recent years, there have been increasing economic and industrial drivers for the development of real-time non-destructive evaluation directly at the point of manufacture. Real-time inspection and monitoring of welding processes can help to reduce fabrication costs by detecting defects as they occur, enabling more efficient and cost-effective builds. This paper shows, for the first time, the use of phased array ultrasonics to monitor and analyse the molten weld pool during deposition of multi-pass gas tungsten arc welds. The received ultrasonic signals are shown to contain information related to key physical transitions occurring within the welding process, namely the melting and solidification of the weldment. Furthermore, the technique used here is shown to be effective for determining weld quality in real-time with significant signal changes occurring when defects such as Lack of Root Penetration are present. The accurate focusing and steering capabilities offered by phased arrays are used to successfully isolate the molten weld pool from the surrounding solidified weldment during deposition of multiple layers of a multi-pass weld

Development of an acoustic emission monitoring system for crack detection during arc welding

Condition monitoring techniques are employed to monitor the structural integrity of a structure or the performance of a process. They are used to evaluate the structural integrity including damage initiation and propagation in engineering components. Early damage detection, maintenance and repairs can prevent structural failures, reduce maintenance and replacement costs, and guarantee that the structure runs securely during its service life. Acoustic emission (AE) technology is one of the condition monitoring methods widely employed in the industry. AE is an attractive option for condition monitoring purposes, the number of industrial applications where is used is rising. AE signals are elastic stress waves created by the fast release of energy from local sources occurring inside of materials, e.g. crack initiating and propagating. The AE technique includes recording this phenomenon with piezoelectric sensors, which is mounted on the surface of a structure. The signals are subsequently analysed in order to extract useful information about the nature of the AE source. AE has a high sensitivity to crack propagation and able to locate AE activity sources. It is a passive approach. It listens to the elastic stress waves releasing from material and able to operate in real-time monitoring to detect both cracks initiating and propagating. In this study, the use of AE technology to detect and monitor the possible occurrence of cracking during the arc welding process has been investigated. Real-time monitoring of the automated welding operation can help increase productivity and reliability while reducing cost. Monitoring of welding processes using AE technology remains a challenge, especially in the field of real-time data analysis, since a large amount of data is generated during monitoring. Also, during the welding process, many interferences can occur, causing difficulties in the identifications of the signals related to cracking events. A significant issue in the practical use of the AE technique is the existence of independent sources of a signal other than those related to cracking. These spurious AE signals make the discovering of the signals from the cracking activity difficult. Therefore, it is essential to discriminate the signal to identify the signal source. The need for practical data analysis is related to the three main objectives of monitoring, which is where this study has focused on. Firstly, the assessment of the noise levels and the characteristics of the signal from different materials and processes, secondly, the identification of signals arising from cracking and thirdly, the study of the feasibility of online monitoring using the AE features acquired in the initial study. Experimental work was carried out under controlled laboratory conditions for the acquisition of AE signals during arc welding processing. AE signals have been used for the assessment of noise levels as well as to identify the characteristics of the signals arising from different materials and processes. The features of the AE signals arising from cracking and other possible signal sources from the welding process and environment have also collected under laboratory conditions and analysed. In addition to the above mentioned aspects of the study, two novel signal processing methods based on signal correlation have been developed for efficiently evaluating data acquired from AE sensors. The major contributions of this research can be summarised as follows. The study of noise levels and filtering of different arc welding processes and materials is one of the areas where the original contribution is identified with respect to current knowledge. Another key contribution of the present study is the developing of a model for achieving source discrimination. The crack-related signals and other signals arising from the background are compared with each other. Two methods that have the potential to be used in a real-time monitoring system have been considered based on cross-correlation and pattern recognition. The present thesis has contributed to the improvement of the effectiveness of the AE technique for the detection of the possible occurrence of cracking during arc welding

Infrared sensor-based temperature control for domestic induction cooktops

In this paper, a precise real-time temperature control system based on infrared (IR) thermometry for domestic induction cooking is presented. The temperature in the vessel constitutes the control variable of the closed-loop power control system implemented in a commercial induction cooker. A proportional-integral controller is applied to establish the output power level in order to reach the target temperature. An optical system and a signal conditioning circuit have been implemented. For the signal processing a microprocessor with 12-bit ADC and a sampling rate of 1 Ksps has been used. The analysis of the contributions to the infrared radiation permits the definition of a procedure to estimate the temperature of the vessel with a maximum temperature error of 5 °C in the range between 60 and 250 °C for a known cookware emissivity. A simple and necessary calibration procedure with a black-body sample is presented

Bead geometry modeling on uneven base metal surface by fuzzy systems for multi-pass welding

This paper presents a modeling method of weld bead profiles deposited on uneven base metal surfaces and its application in multi-pass welding. The robotized multi-pass tungsten inert gas welding requires precise positioning of the weld beads to avoid welding defects and achieve the desirable welding join since the weld bead shapes depend on the surface of the previously deposited beads. The proposed model consists of fuzzy systems to estimate the coefficients of the profile function. The characteristic points of the trapezoidal membership functions in the rule bases are tuned by the Bacterial Memetic Algorithm during supervised training. The fuzzy systems are structured as multiple-input-single-output systems, where the inputs are the welding process variables and the coefficients of the shape functions of the segments underlying the modeled bead; the outputs are the coefficients of the bead shape function. Each segment surface is approximated by a second-order polynomial function defined in the weld bead’s local coordinate system. The model is developed from empirical data collected from single and multi-pass welding. The performance of the proposed model is compared with a multiple linear regression model. During the experimental validation, first, the individual beads are evaluated by comparing the estimated coefficients of the profile function and other bead characteristics (bead area, width, contact angles, and position of the toe points) with the measurements, and the estimations of a multiple linear regression model. Second, the sequential placement of the weld beads is evaluated while filling a straight Vgroove by comparing the estimated bead characteristics with the measurements and calculating the accumulated error of the filled groove cross-section. The results show that the proposed model provides a good estimation of the bead shapes during deposition on uneven base metal surfaces and outperforms the regression model with low error in both validation cases. Furthermore, it is experimentally validated that the derived bead characteristics provide a suitable measure to identify locations sensitive to welding defects

Remanufacturing and Advanced Machining Processes for New Materials and Components

"Remanufacturing and Advanced Machining Processes for Materials and Components presents current and emerging techniques for machining of new materials and restoration of components, as well as surface engineering methods aimed at prolonging the life of industrial systems. It examines contemporary machining processes for new materials, methods of protection and restoration of components, and smart machining processes. • Details a variety of advanced machining processes, new materials joining techniques, and methods to increase machining accuracy • Presents innovative methods for protection and restoration of components primarily from the perspective of remanufacturing and protective surface engineering • Discusses smart machining processes, including computer-integrated manufacturing and rapid prototyping, and smart materials • Provides a comprehensive summary of state-of-the-art in every section and a description of manufacturing methods • Describes the applications in recovery and enhancing purposes and identifies contemporary trends in industrial practice, emphasizing resource savings and performance prolongation for components and engineering systems The book is aimed at a range of readers, including graduate-level students, researchers, and engineers in mechanical, materials, and manufacturing engineering, especially those focused on resource savings, renovation, and failure prevention of components in engineering systems.

Remanufacturing and Advanced Machining Processes for New Materials and Components

"Remanufacturing and Advanced Machining Processes for Materials and Components presents current and emerging techniques for machining of new materials and restoration of components, as well as surface engineering methods aimed at prolonging the life of industrial systems. It examines contemporary machining processes for new materials, methods of protection and restoration of components, and smart machining processes. • Details a variety of advanced machining processes, new materials joining techniques, and methods to increase machining accuracy • Presents innovative methods for protection and restoration of components primarily from the perspective of remanufacturing and protective surface engineering • Discusses smart machining processes, including computer-integrated manufacturing and rapid prototyping, and smart materials • Provides a comprehensive summary of state-of-the-art in every section and a description of manufacturing methods • Describes the applications in recovery and enhancing purposes and identifies contemporary trends in industrial practice, emphasizing resource savings and performance prolongation for components and engineering systems The book is aimed at a range of readers, including graduate-level students, researchers, and engineers in mechanical, materials, and manufacturing engineering, especially those focused on resource savings, renovation, and failure prevention of components in engineering systems.

Remanufacturing and Advanced Machining Processes for New Materials and Components

Remanufacturing and Advanced Machining Processes for Materials and Components presents current and emerging techniques for machining of new materials and restoration of components, as well as surface engineering methods aimed at prolonging the life of industrial systems. It examines contemporary machining processes for new materials, methods of protection and restoration of components, and smart machining processes. • Details a variety of advanced machining processes, new materials joining techniques, and methods to increase machining accuracy • Presents innovative methods for protection and restoration of components primarily from the perspective of remanufacturing and protective surface engineering • Discusses smart machining processes, including computer-integrated manufacturing and rapid prototyping, and smart materials • Provides a comprehensive summary of state-of-the-art in every section and a description of manufacturing methods • Describes the applications in recovery and enhancing purposes and identifies contemporary trends in industrial practice, emphasizing resource savings and performance prolongation for components and engineering systems The book is aimed at a range of readers, including graduate-level students, researchers, and engineers in mechanical, materials, and manufacturing engineering, especially those focused on resource savings, renovation, and failure prevention of components in engineering systems