Monitoring Of Welding Process Research Articles

Quality monitoring of hybrid welding processes: A comprehensive review

Hybrid welding processes have gained significant attention due to their high efficiency and exceptional welding properties. However, there are still significant technological challenges in achieving consistent quality and suppressing welding defects. To overcome this challenge, researchers have focused on the integration of visual analysis techniques, numerical simulation techniques, and advanced technologies such as artificial intelligence/machine learning (AI/ML) and digital twins. This comprehensive review synthesizes current knowledge on quality monitoring in hybrid welding, encompassing an overview of hybrid welding processes, quality assurance, monitoring techniques, key performance indicators, and advancements in monitoring techniques. Furthermore, the review highlights the integration of sensor data with AI/ML algorithms and digital twin technologies, enhancing the capabilities of quality monitoring systems. Notably, the review emphasizes the incorporation of artificial intelligence (AI) and digital twin technologies into quality monitoring frameworks. Artificial intelligence/Machine learning enables real-time analysis of welding parameters and defect detection, while digital twins offer virtual representations of physical welding processes, facilitating predictive maintenance and optimization. The findings underscore the crucial role of sensor technology, AI/ML, and digital twin integration in enhancing defect detection accuracy, improving welded joint quality, and control in hybrid welding. In addition to improving the quality of welded joints, this integration paves the way for further developments in welding technology.

Inline Monitoring of continous Ultrasonic Welding Processes of Thermoplastic Composites via a custom polyCMUT based Ultrasound Array

Ultrasonic welding (UW) of thermoplastic composites (TCs) is an emerging technology in the field of composite joining techniques in the aerospace sector. Through a mechanical oscillator, ultrasound at a frequency of 20kHz is induced into the material via a welding horn, where microscopic friction and damping effects melt the thermoplastic. Under further pressure the weld area cools down, permanently joining both parts together. Like all joining processes in the aerospace industry the resulting joints need to be tested for their quality and structural integrity. The traditional testing method using water-coupled ultrasound includes extra steps. This process could be considerably improved by assessing the quality of the weld directly after or even during the welding process, allowing for immediate rework or discard of the parts in question. Ultrasound is still the best solution for this quality assessment, being inexpensive, well understood, and able to create B-Mode images, allowing a look into the cross-section of the weld. However, there are several major problems: To increase the system complexity as little as possible it is necessary to attach the ultrasound unit next to the welding equipment, and as close to the welding horn and compactor as possible to save space and keep the end- effector manoeuvrable. This brings problems for classic piezoelectric ultrasonic arrays: The low welding frequency and its resonance modes reach into the lower resonance modes of the piezoelectric sensors leading to immense noise, hiding any potential echo from the welding zone. Classic piezoelectric crystals are also very brittle and can suffer damage from sustained exposure to this violent environment. The authors present a novel solution: a custom-made polymer-based capacitive micromachined ultrasonic transducer array (polyCMUT). polyCMUTs are tiny drums with two electrodes. One on the bottom and the other suspended over a cavity sandwiched between two layers of polymers. By applying a DC-bias an electrical field is created and the membrane is set under tension. If then an AC voltage is applied, the strength of the electric field decreases, allowing the membrane to snap back into its original position. If done at the resonance frequency of the membrane, a strong ultrasonic signal is created. To receive this signal the polyCMUT is charged with a DC-bias, allowing it to receive the echo of the transmitted signal by measuring the changing capacitance. Not only is the polymer robust and inexpensive to fabricate, the general architecture of CMUTs also allows a design where the first mode of resonance is the actual mode the CMUT is operating in. By designing for a resonance frequency over 5 MHz all noise from the initial welding process is ignored, leading to a working pulse echo imaging system. The array is then mounted onto a PEEK block attached to the compactor unit of the welding end-effector. This publication is intended to present initial results, the design process of the custom array and the tests leading there.

Defect monitoring of high-power laser-arc hybrid welding process based on an improved channel attention convolutional neural network

Defect monitoring of high-power laser-arc hybrid welding process based on an improved channel attention convolutional neural network

Prediction and optimization method for welding quality of components in ship construction

Welding process, as one of the crucial industrial technologies in ship construction, accounts for approximately 70% of the workload and costs account for approximately 40% of the total cost. The existing welding quality prediction methods have hypothetical premises and subjective factors, which cannot meet the dynamic control requirements of intelligent welding for processing quality. Aiming at the low efficiency of quality prediction problems poor timeliness and unpredictability of quality control in ship assembly-welding process, a data and model driven welding quality prediction method is proposed. Firstly, the influence factors of welding quality are analyzed and the correlation mechanism between process parameters and quality is determined. According to the analysis results, a stable and reliable data collection architecture is established. The elements of welding process monitoring are also determined based on the feature dimensionality reduction method. To improve the accuracy of welding quality prediction, the prediction model is constructed by fusing the adaptive simulated annealing, the particle swarm optimization, and the back propagation neural network algorithms. Finally, the effectiveness of the prediction method is verified through 74 sets of plate welding experiments, the prediction accuracy reaches over 90%.

Airtight welding technology for aircraft composite mold panel

In this paper, the airtightness problem caused by welding defects in the manufacturing process of composite mold is analyzed. The mechanism analysis of the weld defects of composite mold is conducted. According to the mechanism of weld failure, the welding process monitoring, weld metallographic analysis, weld X-ray inspection, panel airtightness detection, and other methods are used to evaluate the welding quality of different welding forms in the welding process of composite mold panels. The measures to optimize the welding quality of composite mold panels are put forward. Finally, through the actual verification, it is proved that the optimized air-tightness welding method is stable and reliable.

On-line defect recognition of MIG lap welding for stainless steel sheet based on weld image and CMT voltage: Feature fusion and attention weights visualization

At present, sensor recognition technology has become a research hotspot in welding process monitoring. However, most of the existing defect recognition methods are based on single-sensor information, which hinders the further improvement of recognition accuracy and weakens the anti-interference ability under complex conditions. To overcome the shortcomings of single-sensor recognition technology, multi-source information fusion technology can exploit the advantages of different types of sensors to achieve more comprehensive and accurate monitoring of the welding process. However, given the process of fusion of different information, the feature mining mechanism of the deep learning model is still under-explained, and the direct complementarity of the different information remains unclear. In this paper, we propose a hybrid model that uses the fusion of molten pool image and voltage signals to quickly and accurately identify three types of defects in lap MIG welding: burn-through, weld bias, and uneven width. Firstly, a synchronized trigger device is used to acquire molten pool images and weld voltage information, and the voltage data is sliced and matched with the molten pool image data. Secondly, the Short-Time Fourier Transform (STFT) is applied to explore the frequency variation components in the voltage data, and the resulting time-frequency matrix is transformed into a time spectrogram. Finally, the molten pool image and the time spectrogram are simultaneously fed into the hybrid CNN model for online monitoring of welding defect states. The test results show that the average accuracy of the hybrid CNN model in detecting five different welding states reaches 98.87 %, which is significantly higher than three other models based on single sensor information. The visualization of the confusion matrix and Grad-CAM shows that the hybrid CNN model based on multi-source heterogeneous information has a higher accuracy for detecting the transition states that are difficult to detect in welding, and has a more reasonable mechanism for allocating attention, so it can achieve a more accurate and balanced recognition effect in the whole welding manufacturing process. These studies can provide guidance for quality monitoring of welding processes based on multi-source sensor technology, which can be extended to the field of arc additive monitoring.

Two-stage quality monitoring of a laser welding process using machine learning

Abstract In production, quality monitoring is essential to detect defective elements. State-of-the-art approaches are single-sensor systems (SSS) and multi-sensor systems (MSS). Yet, these approaches might not be suitable: Nowadays, one component may comprise several hundred meters of the weld seam, necessitating high-speed welding to produce enough components. To detect as many defects as possible in time, fast yet precise monitoring is required. However, information captured by SSS might not be sufficient and MSS suffer from long inference times. Therefore, we present a confidence-based cascaded system (CS). The key idea of the CS is that not all data are analyzed to obtain the quality weld, but only selected ones. As evidenced by our results, all CS outperform SSS in terms of accuracy and inference time. Further, compared to MSS, the CS has hardware advantages.

Real-Time AI driven Interpretation of Ultrasonic Data from Resistance Spot Weld Process Monitoring For Adaptive Welding

Adaptive resistance spot welding systems typically rely on real-time analysis of dynamic resistance curves and other indirect measurements to estimate weld progress and guide adaptive weld control algorithms. Though efficient, these approaches are not always reliable, and consequently there is a need for improved feedback systems to drive adaptive welding algorithms. As an alternative, an advanced in-line integrated ultrasonic monitoring system is proposed, with real-time weld process characterization driven by artificial intelligence (AI) to create actionable feedback for the weld controller. Such a system would require real-time ultrasonic data interpretation, and for this a solution using deep learning was investigated. The proposed solution monitors the ultrasonic data for key process events and estimates the vertical size of the weld nugget proportional to the stack size throughout the welding process. This study shows that adaptive welding using ultrasonic process monitoring backed by AI-based data interpretation has immense potential. This research highlights the importance of nondestructive evaluation (NDE) in the zero-defect manufacturing paradigm.

Real-Time Penetration Recognition Based on Voltage Signal in K-TIG Welding

This paper focuses on discerning the melting state of keyhole tungsten inert gas (K-TIG) welding, a promising welding technique still considered a novelty. In order to achieve this goal, a sensing system is established to collect voltage signals during the welding process of 304 stainless steel. Analysis of the collected signals reveals that the characteristic signal power spectrum between 36 kHz and 37 kHz is closely related to whether the welding has complete joint penetration. The experimental findings confirm that the spectral power density of the characteristic signal varies significantly with the alteration of welding quality. Moreover, it is observed that when the welding melting state remains unchanged, the change in welding parameters is not noteworthy. When adjusting welding parameters to maintain stable feature signals during the welding process, the corresponding penetration state of the welding does change. Therefore, it is possible to ensure the stability of welding quality by altering welding parameters in real time. These research results provide valuable insights into the real-time quality monitoring of the K-TIG welding process.

Laser Welding Penetration Monitoring Based on Time-Frequency Characterization of Acoustic Emission and CNN-LSTM Hybrid Network

In-process penetration monitoring of the pulsed laser welding process remains a great challenge for achieving uniform and reproducible products due to the highly complex nature of the keyhole dynamics within the intense laser-metal interactions. The main purpose of this study is to investigate the feasibility of acoustic emission (AE) measurement for penetration monitoring based on acoustic wave characteristics and deep learning. Firstly, a series of laser welding experiments on aluminum alloys were conducted using high-speed photography and AE techniques. This allowed us to in-situ visualize the complete keyhole dynamics and elucidate the generation mechanism of acoustic waves originating from pressure fluctuations at the keyhole wall. Then, an adaptive time-frequency technique namely VMD (Variational Mode Decomposition) was proposed to characterize the acoustic energy distribution among the nine subsignals with low-frequency and high-frequency components under different welding penetrations. Lastly, a novel hybrid model combing CNN (Convolutional Neural Network) and LSTM (Long Short Term Memory) was designed to deeply mine the spatial and temporal acoustic features from the extracted frequency components. Extensive experiments demonstrate that our proposed approach yields a remarkable classification performance with a test accuracy of 99.8% and a standard deviation of 0.21, which obtains a high recognition rate. This work is a new paradigm in the digitization and intelligence of the laser welding process and contributes to an alternative way of developing an efficient end-to-end penetration monitoring system.

Application research of Welding process monitoring system based on LAN

In the field of production and manufacturing, the monitoring of welding information and analysis of post-welding signal is an important technical means to ensure the overall quality of work. Monitoring the information parameters in the welding process and comprehensively mastering the construction process of welding process can not only quickly obtain the working status of the staff, but also take the collected signal as the basis for quality evaluation. Based on the research status of welding process monitoring system in the new era, this paper discusses the design of monitoring system for real-time acquisition of welding process information, and makes clear the application principle of signal analysis, management module and quality evaluation system. The final results show that the overall system design can not only manage the real-time data, but also optimize the welding technology and improve the quality of welding work.

Identification of defective weld and quality monitoring in manual metal arc welding process using surface level image features

The current research work is the demonstration of the development of a monitoring methodology for the identification of defective weld using surface-level features. A novel grey level-based indicator has been proposed in this research work that has the potential to identify defective welds. The proposed methodology has been validated with a well-established 2D wavelet transform framework to prove the capacity of the proposed methodology. In comparison to the wavelet transform the proposed methodology has been found to be more successful and computationally efficient with less a priori knowledge and limited pre-processing steps making it more effective. In this framework extracted image features along with the process parameters are used as the input to the support vector machine-based regression model for the prediction of the ultimate tensile strength of the welds. It is observed that the model yields an average absolute percentage error of 2.83% compared to the model developed only with process parameters that yield an error of 5.09%. The improvement in the prediction capability of the model leads to the impression that the proposed methodology is effective. The methodology can be used in human free automation system for monitoring of manual metal arc welding process.

Parameter monitoring and control of the friction stir welding process

A control algorithm and the implementation example of a corresponding control system for a friction welding machine designed for experimental work on the friction stir welding technology of large-sized products from aluminum alloys are described. Brief characteristics of the experimental-industrial installation «Malachite» are given. A method to control the welding mode for the process automation according to the main drive power is found. The emergency events separation for automatic friction stir welding is introduced. The application of optical communication between equipment for mode control and unification of panel solutions for friction stir welding is tested. Keywords friction stir welding, aluminum alloys, welded assemblies from aluminum alloys, welding process automation, pure welding technologies, optical communication

Acoustic methods in real-time welding process monitoring: Application and future potential advancement

The rapid advancement of the welding technology has simultaneously increased the demand for the online monitoring system in order to control the process. Among the methods that could be possibly used to assess the weld condition, an air-borne acoustic method grasps the attention from scholars due to its ability to provide a simple, non-contact, and low-cost measurement system. However, it is still lack of resources involving this subject in an attempt to deeply understand the emitted sound behaviour during welding especially when dealing with a complete deviation of a process parameter, welding types, workpiece material as well as the noise from the surrounding. This paper reviews the application of the acoustic method in monitoring the welding process. Specifically, this review emphasized the source of both structure-borne and air-borne acoustic during the welding process and the significance of applying the acoustic method in more detail. By focusing on the liquid state welding process, the scope of discussion converged on the arc and laser welding process. In the last part of this review, the potential future advancement of this method is pointed out before the overall conclusion is made.

Monitoring of resistance spot welding process

Resistance spot welding is a process with high productivity and high level of automation. This rises a number of tasks related to development of quality evaluation and process monitoring systems operating in real-time mode which would allow to detect non-compliant joints during the process run of shortly after it is finished. The more complex task is to make such system as much universal as possible, consisting of relatively simple equipment and with a possibility of full automation of evaluation process. Research was focused on electrical welding parameters which determine the thermal cycle of the welding process as well as the state of metal in the welding zone and its plasticity. Experiments were performed for work pieces with different pre-welding state of surface. Developed method also allows to monitor the state of working surfaces of electrodes and to detect splashes with a relatively high accuracy.

Monitoring of 304 austenitic stainless-steel laser-MIG hybrid welding process based on EMD-SVM

Real-time monitoring of laser-metal inert gas (MIG) hybrid welding plays a significant role in modern automatic production. In this paper, an innovative prediction model is proposed for laser-MIG hybrid welding process monitoring and weld defect detection. Specifically, the status of laser-MIG hybrid welding was monitored using a high-speed imaging system that could observe the visual information from the top and bottom of the weldment simultaneously. Then, the morphological features were extracted by image process algorithm. Furthermore, the empirical mode decomposition (EMD) was employed to analyze the time series of visual features, and the decomposed component intrinsic mode functions (IMFs) and residue were inputted to the support vector machine (SVM) classification model. Finally, the EMD-SVM model was employed to evaluate the welding status of four weld formations, including sound weld, root humping, incomplete penetration and burn through. Experimental results demonstrate the superiority of the proposed method in terms of accuracy and robustness compared with traditional methods such as the backpropagation (BP) neural network, SVM, wavelet packet decomposition (WPD)-BP, WPD-SVM, and EMD-BP. This proposed method provides a novel and accurate approach to perform laser-MIG welding process monitoring and online defects detection.

Innovations in Monitoring, Control and Design of Laser and Laser-Arc Hybrid Welding Processes

With the rapid development of high power laser, laser welding has been widely used in many fields including manufacturing, metallurgy, automobile, biomedicine, electronics, aerospace etc. Because of its outstanding advantages, such as high energy density, small weld size, easy automation. Combining the two heat sources of laser and arc for welding can achieve excellent results due to the synergistic effect. Laser welding is a complicated physical and chemical metallurgical process, involving the laser beam and molten pool, keyholes and materials melting, evaporation and multiple physical process. Process monitoring and quality control are important content of research and development in the field of laser welding, which is the premise to obtain fine weld with high quality. Numerical simulation technology can describe many complex physical phenomena in welding process, which is very important to predict weld forming and quality and clarify the underline mechanism. In this paper, the research progress of process monitoring, quality control and autonomous intelligent design of laser and laser-arc hybrid welding based on numerical simulation were reviewed, and the research hotspots and development trends of laser welding in the future are predicted.

Monitoring of high-speed laser welding process based on vapor plume

The increase of welding speed can improve the productivity of laser welding. The behavior of vapor plume is an important indicator reflecting the state of the laser welding process. However, the behavior of vapor plume in high-speed laser welding (whose welding speed is higher than 10 m/min) has not been studied adequately. In this research, images of the vapor plume in high-speed laser welding of SUS304 austenitic stainless steel are captured through high-speed imaging for the monitoring of the welding process. Characteristics of the vapor plume are extracted with image processing, and the influences of laser power and welding speed on these characteristics are analyzed. The relationships between the behavior of vapor plume and melt pool are discussed, and three ejection regimes of the vapor plume are proposed. In order to predict the occurrence of humping, which is a typical defect in high-speed laser welding, samples are built by applying a time sliding window and then a classification model is built with the random forest. In addition, the importance of features in the classification is analyzed. The test results show that the proposed method can predict the humping defect in high-speed laser welding accurately.

Detection of arc characteristics and weld forming quality of aluminum alloy DP-MIG welding using AE signal through resonance demodulation

Acoustic emission (AE) signals are generated during aluminum alloy double-pulse melt inert-gas (DP-MIG) welding, which contains physical information related to arc stability and weld forming quality. In this paper, the AE signal test platform of aluminum alloy DP-MIG welding is constructed. An adaptive resonance demodulation method for AE signals in unknown frequency bands is proposed, which is applied for the AE signals of the six groups different welding process parameters. The time-domain waveform and frequency spectrum of the acquired AE envelope signals are consistent with that of the arc current. Hilbert-Huang transform (HHT) is performed on the envelope signal to obtain a time–frequency diagram. The signal energy changes periodically along the time scale, and the frequency distribution ranges from 0 to 2000 Hz. The segmental entropy of the AE signal is calculated to evaluate the welding seam forming quality. The calculated entropy values of 0.6, 0.9 and 1 mean that the welding seam is formed well, the values of 1.6 and 1.9 mean that the welding seam is poor. The research provides a new and effective method for realizing the quality monitoring of aluminum alloy DP-MIG welding process.

Process Monitoring in Ultrasonic Metal Welding of Lithium Batteries by Power Signals

Abstract Ultrasonic metal welding is one of the key technologies in manufacturing lithium batteries, and the welding quality directly determines the battery performance. Therefore, an online welding process monitoring system is critical in identifying abnormal welding processes, detecting defects, and improving battery quality. Traditionally, the peak welding power is used to indicate abnormal process signals in welding process monitoring systems. However, since various factors have complex impacts on the electric power signals of ultrasonic welding processes, the peak power is inadequate to detect different types of welding defects. Therefore, a signal pattern matching method is proposed in this study, which is based on the electric power signal during the entire welding process and thus is capable of identifying abnormal welding processes in various conditions. The proposed method adopts isometric transformation and homogenization as signal pretreatment methods, and Euclidean distance is used to calculate the similarity metric for signal matching. The effectiveness and robustness of the proposed method are experimentally validated under different abnormal welding conditions.