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WAAM has been proven a promising alternative to fabricate medium and large scale metal parts with a high depositing rate and automation level. However, the production quality may deteriorate due to the poor deposited layer surface quality. In this paper, a laser sensor based surface roughness measuring method was developed for WAAM. To improve the surface integrity of deposited layers by WAAM, different machine learning models, including ANFIS, ELM and SVR, were developed to predict the surface roughness. Furthermore, the ANFIS model was optimized by GA and PSO algorithms. Full factorial experiments were conducted to obtain the training data, and the K-fold Cross-validation strategy was applied to train and validate machine learning models. The comparison results indicate that GA–ANFIS has superiority in predicting surface roughness. The RMSE, $$ R^{2} $$ , MAE and MAPE for GA–ANFIS were 0.0694, 0.93516, 0.0574, 14.15% respectively. This study could also provide inspiration and guidance for surface roughness modelling in multipass arc welding and cladding.

Wire arc additive manufacturing (WAAM) is an emerging technology in the manufacturing industry, which uses a welding arc as an energy source to fuse metal wire and deposit layer by layer. In order to promote its manufacture precision, stability, and repeatability, it’s crucial to develop a feedback control strategy for WAAM. This research implements vision-based feedback control for the layer width during the WAAM process. A WAAM system is developed using a robot and CMT welder with a visual sensing system. The dynamics of the layer width in WAAM process is modeled experimentally. An ARX dynamic model is built. Based on this model, a model predictive control (MPC) strategy is derived to regulate the WAAM process. Feedback control experiments were conducted to verify the tracking and robustness performance of the proposed MPC algorithm.

Wire arc additive manufacturing technology (WAAM) has become a very promising alternative to high-value large metal components in many manufacturing industries. Due to its long process cycle time and arc-based deposition, defect monitoring, process stability and control are critical for the WAAM system to be used in the industry. Although major progress has been made in process development, path slicing and programming, and material analysis, a comprehensive process monitoring, and control system are yet to be developed. This paper aims to provide an in-depth review of sensing and control design suitable for a WAAM system, including technologies developed for the generic Arc Welding process, the Wire Arc Additive Manufacturing process and laser Additive Manufacturing. Particular focus is given to the integration of sensor-based feedback control, and how they could be implemented into the WAAM process to improve its accuracy, reliability, and efficiency. The paper concludes by proposing a framework for sensor-based monitoring and control system for the GMAW based WAAM process. This framework provides a blueprint for the monitoring and control strategies during the WAAM process and aims to identify and reduce defects using information fusion techniques.

As an advanced and highly efficient welding method, Keyhole Tungsten Inert Gas (keyhole TIG) welding has drawn wide interests from the manufacturing industry. In order to improve its manufacturing quality and automation level, it’s necessary to develop an online monitoring system for the keyhole TIG welding process. This study developed a visual monitoring system, which utilized an HDR welding camera to monitor the welding pool and keyhole during keyhole TIG welding process. A state of the art Convolutional neural network (Resnet) was developed to recognize different welding states, including good weld, incomplete penetration, burn through, misalignment and undercut. In order to improve the diversity of training dataset, image augmentation was performed. To optimize the training process, a metric learning strategy of center loss was introduced. Furthermore, visualization methods, including guided Grad-CAM, feature map and t-SNE were applied to understand and explain the effectiveness of deep learning process. This study will lay a solid foundation for the development of on-line monitoring system of keyhole TIG.

Geometric properties of material deposited by the wire arc additive manufacturing (WAAM) process often deviates from desired setpoints. To improve the accuracy and repeatability of the WAAM process, an effective control strategy to maintain desired deposition geometry that operates robustly under various welding conditions is required. In this work, a control strategy utilizing multi-input multi-output (MIMO) model-predictive control (MPC) is presented. This approach, based on linear autoregressive (ARX) modelling, aims to improve the accuracy and flexibility of deposited bead geometry in the WAAM process. The MPC controller updates welding parameters between successive layers by minimizing a cost function based on sequences of input variables. Measurements of deposited bead geometry are made by laser scanner and input to the linear ARX model, which then makes future bead geometry predictions. Weighting coefficients of the ARX model are trained iteratively throughout the manufacturing process. Experimental results show that the derived control strategy can reduce fluctuations in a part's height by 400% and maintain the fluctuation within an acceptable range. In addition, the fluctuations in bead width along a single weld seam was also improved by more than 50%.

In more recent times, research on various aspects of the Wire Arc Additive Manufacturing (WAAM) process has been conducted, and efforts into monitoring the WAAM process for defect identification have increased. Rapid and reliable monitoring of the WAAM process is a key development for the technology as a whole, as it will enable components produced by the process to be qualified to relevant standards and hence be deemed fit for use in applications such as those found in the aerospace or naval sectors. Intelligent algorithms provide inbuilt advantages in processing and analysing data, especially for the large data sets generated during the long manufacturing cycles. Interdisciplinary engineering (IDE) furnishes a concept integrating computer science and industrial system manufacturing engineering together to treat large amounts of process monitoring data. In this work, a WAAM process monitoring and defect detection system integrating intelligent algorithms is presented. The system monitors welding arc current and voltage signals produced by the WAAM process and makes use of a support vector machine (SVM) learning method to identify disturbances to the welding signal which indicate the presence of potential defects. The incremental machine learning models developed in this work are trained via statistical feature analysis of the welding signals and a novel quality metric that improves detection rates is also presented. The incremental learning approach provides an efficient means of detecting welding-based defects, as it does not require large quantities of data to be trained to an operational level (addressing a major drawback of other machine learning methods). A case study is presented to validate the developed system, results show that it was able to detect a set of defects with a success rate greater than 90% F1-score.

An approach based on millisecond pulsed laser is proposed for thermal fatigue property evaluation in this paper. Cyclic thermal stresses and strains within millisecond interval are induced by complex and transient temperature gradients with pulsed laser heating. The influence of laser parameters on surface temperature is studied. The combination of low pulse repetition rate and high pulse energy produces small temperature oscillation, while high pulse repetition rate and low pulse energy introduces large temperature shock. The possibility of application is confirmed by two thermal fatigue tests of compacted graphite iron with different laser controlled modes. The developed approach is able to fulfill the preset temperature cycles and simulate thermal fatigue failure of engine components.

As a new emerging technology, wire arc additive manufacturing (WAAM) has attracted extensive interests from both academia and industry during recent years. WAAM uses welding arc as an energy source to fuse metal wire and deposit layer by layer, which provides the advantages of freeform deposition. In order to improve its manufacture precision, stability and repeatability, it is necessary to develop sensing and control strategy for WAAM process. This research develops a passive visual sensing system for a robotic WAAM system. A new deep learning technique (Mask R-CNN) is proposed to detect and segment the melt pool area, and the width of melt pool can be measured based on the coordinate of the bounding rectangle. The pseudo-random ternary (PRT) signals were used to stimulate the WAAM process, and the corresponding width can be measured by the Mask R-CNN. Based on the width data and corresponding PRT input, a dynamic model of adaptive neuro-fuzzy inference system was built for the WAAM process.

As one of obvious electrolyte design technologies of Li ion batteries (LIBs) to meet durable high-rate capability at elevated temperatures for battery electric vehicles, this study assesses the superiority of gel polymer electrolyte (GPE) based on experimental results supporting its working mechanism. Our previous study indicated that degradation of cathode electrode under high-rate cycling at elevated temperature was a major cause of the decrease in performance of LIBs, and the single full cells (SFCs) with a GPE designed from dipentaerythritol hexaacrylate and methyl ether methacrylate was re-verified to have superior 3.0C cycling performance at 80 °C. The superiority of the GPE is studied from comparing mid-voltages of discharge profiles of the SFCs, observing the cross-sectional morphology of the electrodes by field emission scanning electron microscopy, assessing the interacting force among the electrolyte components by thermogravimetric analysis, and examining each resistance component of the SFCs by electrochemical impedance spectroscopy. Gel polymerization of liquid electrolyte results in a significant increase of durable high-rate capability of LIBs due to the mechanisms of not only its buffering effect on solvating process of Li+ ions being extracted from the active materials during high rate operation, but also higher thermal stability of electrolyte components, lower susceptibility of the ionic conductivity of the electrolyte to a temperature change, and lower energy barrier to breakup of solvated structures during conducting of Li+ ions in gel polymer matrix.

During high-rate cycling of Li-ion batteries (LIBs) at elevated temperatures, the detachment of the cathode materials from their Al substrate is a major cause of the deterioration in the performance of LIBs. This detachment is suppressed by the addition of an electrolyte additive, poly(ethylene glycol) methyl ether methacrylate, which can act as a buffer zone to prevent the abrupt mass transport of electrolyte within the cathode and as a swing to transport Li+ ions dissociating from the active materials of the cathode. Owing to the dual effects of this type of monomer, an acrylate monomer with one side ether chain, the cathode materials are maintained without detachment from the Al substrate, even under severe cycling conditions. This idea can be applied to LIBs for a series of electric vehicles, which require superior high-rate performance at elevated temperatures.