This study explores the uncharted territory of beam shaping through a novel deformable mirror system in directed energy deposition laser wire, an emerging area in Additive Manufacturing. While beam shaping has shown substantial benefits in laser processes like welding and powder bed fusion, its potential in this specific domain remains unexploited. The research investigates the influence of three near-elliptical Gaussian beam shapes on melt pool and bead geometries during deposition with stainless-steel wire. The study reveals three distinct processing modes achievable at the same total power through beam shaping, with significant modifications observed in melt pool and bead structures. Reduced bead geometry variation and enhanced process stability were achieved with the beam shape with major axis along the wire feeding direction, and with highest average power density and intermediate peak power density. The findings underscore the potential of beam shaping to enhance robustness and increase energy utilization and productivity in this process.  

This study presents a vision-based closed-loop tracking system designed specifcally for robotic laser beam welding of curved and closed square butt joints. The proposed system is compared against 11 existing solutions reported in the literature, which employ various sensor principles for the same application. The system employs a non-contact, non-intrusive machine vision approach, seamlessly integrated into the laser beam welding head to mitigate challenges associated with sensor forerun. Key features include an of-axis LED illumination, an optical flter, and a movable actuator, facilitating real-time image processing and closed-loop control during the welding process. Experimental validation was conducted on stainless-steel plates with complex closed square butt joints. The system achieved a mean absolute joint-to-beam ofset of 0.14 mm across four test cases, with a maximum ofset of 0.85 mm, demonstrating its robustness and precision. Comparative analysis underscores the proposed method’s advantages, showcasing its potential for industrial applications in laser beam welding of geometrically challenging joints.

Defect detection on the computed tomography (CT) images plays an important role in the development of metallic additive manufacturing (AM). Although some deep learning techniques have been adopted in the CT image-based defect detectionproblem, it is still a challenging task to accurately detect small-size defects in the presence of undesirable noises.

In this paper,a novel defect detection method, namely, the depth-connected region-based convolutional neural network (DC-RCNN), is proposed to detect small defects and reduce the influence of noises. In particular, a saliency-guided region proposal method is first developed to generate small-size region proposals with the aim to accommodate the small defects. Then, the main architecture of DC-RCNN is proposed to extract and connect the consistent features across multiple frames, thereby reducing the influence of randomly distributed noises. Moreover, the transfer learning technique is utilized to improve the generalization ability of the proposed DC-RCNN. In order to verify the effectiveness and superiority, the proposed method is applied to the real-world AM data for defect detection.

The experimental validations show that the proposed DC-RCNN is able to detect the small-size defects under noises and outperforms the original RCNN method in terms of detection accuracy and running time.

Laser Beam Welding (LBW) requires precise control to ensure high-quality welds. Accurate classification of joint gap widths and detection of tack welds are crucial for optimizing the process and enhancing product reliability.

This study investigates the application of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to classify instant joint gap widths and detect the presence of tack welds during welding. The goal is to facilitate adaptive joint gap bridging in robotized and autogenous butt joint welding. Sequences of images resembling a time series were captured during welding of prepared workpieces with varying joint gap widths along the joint line.

The results demonstrate that CNNs significantly outperform RNNs, achieving over 99 percent classification accuracy in both validation and test datasets, and 96 percent accuracy under conditions of substantial noise. These findings underscore the potential of CNNs in enhancing the precision and adaptability of welding automation. However, challenges remain in generalizing the CNN model to diverse and noisy operational environments.

Accurate temperature estimation is crucial in metal additive manufacturing ensuring component quality and process efficiency. This study introduces the use of Convolutional Neural Networks (CNNs), namely MobileNet and ResNet, to predict temperatures directly from melt pool images without the need for extensive preprocessing. Through comparative analysis, MobileNet demonstrated superior performance over ResNet, achieving a mean absolute error of 0.0562 and a correlation coefficient of 0.9900. These findings underscore the effectiveness of CNNs in real-time temperature prediction tasks within Laser-Directed Energy Deposition with wire (DED-LB/w), highlighting significant advancements and setting the stage for further technological enhancements.

Hot-wire directed energy deposition using a laser beam (DED-LB/w) is a method of metal additive manufacturing (AM) that has benefits of high material utilization and deposition rate, but parts manufactured by DED-LB/w suffer from a substantial heat input and undesired surface finish. Hence, regulating the process parameters and monitoring the process signatures to control the final quality during the deposition is crucial to ensure the quality of the final part. This paper explores the dynamic modeling of the DED-LB/w process and introduces a parameter-signature-quality modeling and control approach to enhance the quality of modeling and control of part qualities that cannot be measured in situ. The study investigates different process parameters that influence the melt pool width (signature) and bead width (quality) in single and multi-layer beads. The proposed modeling approach utilizes a parameter-signature model as F1 and a signature-quality model as F2. Linear and nonlinear modeling approaches are compared to describe a dynamic relationship between process parameters and a process signature, the melt pool width (F1). A fully connected artificial neural network is employed to model and predict the final part quality, i.e., bead width, based on melt pool signatures (F2). Finally, the effectiveness and usefulness of the proposed parameter-signature-quality modeling is tested and verified by integrating the parameter-signature (F1) and signature-quality (F2) models in the closed-loop control of the width of the part. Compared with the control loop with only F1, the proposed method shows clear advantages and bears potential to be applied to control other part qualities that cannot be directly measured or monitored in situ.

In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the initial cluster centroids of the FCM algorithm. The superiority of the proposed ASRPPSO is demonstrated over five existing PSO algorithms on a series of benchmark functions. To illustrate its application potential, the proposed ASRPPSO-based FCM algorithm is exploited in the outlier detection problem for analyzing the real-world industrial data collected from a wire arc additive manufacturing pilot line in Sweden. Experimental results demonstrate that the proposed ASRPPSO-based FCM algorithm out performs the standard FCM algorithm in detecting outliers of real-world industrial data.

Note to Practitioners

Electric arc (which is governed by the current and arc voltage) plays a significant role in monitoring the operating status of the wire arc additive manufacturing (WAAM) process. The nominal periodic current and voltage may occasionally change abruptly due to anomalies (such asarc instability, unstable metal transfer, geometrical deviations, and surface contaminations), which would affect the quality of the fabricated component. This paper focuses on detecting possible anomalies by analyzing the current and voltage during the WAAM process. A novel clustering-based outlier detection method is proposed for anomaly detection where abnormal and normal instances are categorized into two separate clusters. A new particle swarm optimization algorithm is put forward to optimize the initial cluster centroid so as to improve the detection accuracy. The proposed outlier detection method is applied to real-world data collected from a WAAM pilot line for detecting abnormal instances. Experimental results demonstrate the effectiveness of the proposed outlier detection method. The proposed outlier detection method can be applied to other industrial applications including electrical engineering, mechanical engineering and medical engineering. In the future, we aim to develop an online outlier detection system based on the proposed method for real-time for anomaly detection and defect prediction.

In autogenous laser butt welding the variability of the joint gap can cause problems in terms of weld seam quality. A suitable strategy to alleviate this is to dynamically shape the laser beam instead of a circular-shaped beam with typical Gaussian or top hat distributions. Currently available systems cannot reach sufficient performance due to both the real time control system for the shape variation and the limited laser power currently manageable. In the present work, the possibility of bridging the joint gap during welding using a deformable mirror to elongate the focused laser beam from circular to transversal elliptical shape was investigated. The effect of the beam shaping on the geometry of the weld pool and of the weld cross sections was analysed, for different values of the gap in comparison with a circular Gaussian beam. It was demonstrated that the adoption of a transversal elliptical laser beam makes the welding process more stable, especially for large gaps (i.e. larger than the circular beam radius). Thanks to the beam shaping, the extension of the fused zone (in terms of the cross section area, height and width) resulted to be less sensitive to the gap's dimension; in addition, the extension of the heat affected zone and the presence of undercuts were evidently reduced.

In this study, laser metal fusion with feedstock wire is addressed. We investigated how various process parameters affect the fraction of beam energy that is absorbed by the wire and the workpiece and the metal transfer from the feedstock wire to the melt pool. To perform this research, a thermo-fluid dynamic model with tracking of free surface deformation was developed to include the feeding of a solid wire and predict its melting. The fraction of beam energy absorbed by the metal was modeled as a function of local surface curvature and temperature, accounting for multiple Fresnel reflections and absorptions. The model was applied to Titanium alloy (Ti-6Al-4V) with a 1.07 μm laser and a process in conduction mode. Experiments at various wire feeding rates were conducted to evaluate the model’s ability to predict the process and a good agreement was obtained. The different parameters studied were the beam angular position, the wire angular position, the wire feed rate, and the beam-wire offset. The analysis of the simulation results gave a detailed physical understanding of the laser energy use. It highlighted that thermocapillary and Rayleigh-Plateau instabilities can contribute to the transition from continuous to drop metal transfer mode. Damping these instabilities might thus allow using a wider process window.

To be a successful competitor among other technologies, metallic laser-directed energy depositionusing a laser beam would benefit from the support of intelligent automation making the processrobust, repeatable, and cost-efficient. This calls for technology leaps towards robust and accuratedetection and estimation of the conditions during processing and control schemes for robustperformance. This chapter discusses how developments in sensor technology and model-basedsignal processing can contribute to advancements in in-process monitoring of directed energydeposition using a laser beam and how developments in model-based feedforward- and feedbackcontrol can support automation. The focus is on how machine vision, optical emission spectroscopy,thermal sensing, and electrical process signals can support monitoring, control and better processunderstanding. These approaches are industrially relevant and have a high potential to support amore sustainable manufacturing.