The current state of research of wire arc additive manufacturing (WAAM): a review

Wire arc additive manufacturing is currently rising as the main focus of research groups around the world. This is directly visible in the huge number of new papers published in recent years concerning a lot of different topics. This review is intended to give a proper summary of the international state of research in the area of wire arc additive manufacturing. The addressed topics in this review include but are not limited to materials (e.g., steels, aluminum, copper and titanium), the processes and methods of WAAM, process surveillance and the path planning and modeling of WAAM. The consolidation of the findings of various authors into a unified picture is a core aspect of this review. Furthermore, it intends to identify areas in which work is missing and how different topics can be synergetically combined. A critical evaluation of the presented research with a focus on commonly known mechanisms in welding research and without a focus on additive manufacturing will complete the review

Machine Learning Based Defect Detection in Robotic Wire Arc Additive Manufacturing

In the last ten years, research interests in various aspects of the Wire Arc Additive Manufacturing (WAAM) processes have grown exponentially. More recently, efforts to integrate an automatic quality assurance system for the WAAM process are increasing. No reliable online monitoring system for the WAAM process is a key gap to be filled for the commercial application of the technology, as it will enable the components produced by the process to be qualified for the relevant standards and hence be fit for use in critical applications in the aerospace or naval sectors. However, most of the existing monitoring methods only detect or solve issues from a specific sensor, no monitoring system integrated with different sensors or data sources is developed in WAAM in the last three years. In addition, complex principles and calculations of conventional algorithms make it hard to be applied in the manufacturing of WAAM as the character of a long manufacturing cycle. Intelligent algorithms provide in-built advantages in processing and analysing data, especially for large datasets generated during the long manufacturing cycles. In this research, in order to establish an intelligent WAAM defect detection system, two intelligent WAAM defect detection modules are developed successfully. The first module takes welding arc current / voltage signals during the deposition process as inputs and uses algorithms such as support vector machine (SVM) and incremental SVM to identify disturbances and continuously learn new defects. The incremental learning module achieved more than a 90% f1-score on new defects. The second module takes CCD images as inputs and uses object detection algorithms to predict the unfused defect during the WAAM manufacturing process with above 72% mAP. This research paves the path for developing an intelligent WAAM online monitoring system in the future. Together with process modelling, simulation and feedback control, it reveals the future opportunity for a digital twin system

Real-time measurement of temperature and volume of the weld pool in wire-arc additive manufacturing

The quality of the material deposition and contour accuracy produced by WAAM (Wire Arc Additive Manufacturing) depends on process parameters such as the temperature and the volume of the weld pool. The control of the welding process can be substantially improved by obtaining real-time signals of the weld-pool temperature and volume. In this paper, we present a new optical sensor that can measure the quantities through the welding arc. We have developed a ratio pyrometer with narrowband filters that suppresses the radiation from the plasma. We present our new sensor in this paper and demonstrate its capability to measure the weld-pool temperature pyrometrically and the weld-pool area by digital image processing as a real-time signal through the arc. In addition, we estimate the weld-pool volume from its area and the known material inflow

Robotizing the conventional and Hot-Forging Wire Arc Additive Manufacturing processes for producing 3D parts with complex geometries

Wire Arc Additive Manufacturing (WAAM) is an Additive Manufacturing (AM) process which has high deposition rates at reduced costs, being suitable to produce large size compo-nents. Hot-Forging WAAM (HF-WAAM) is a WAAM variant which uses an oscillating hammer to forge the material as it is deposited, improving mechanical properties and the microstruc-ture of the produced parts. This study aimed to use and validate the WAAM and HF-WAAM to robotize the pro-duction of compact metallic and complex geometry parts. Thus, a welding torch capable of performing forging was redesign, developed and assembled in a 6 degree-of-freedom (6-DoF) manipulator robot. 316LSi stainless steel parts were produced using WAAM and HF-WAAM processes. During their production, the vibration signal of the robot was acquired and then processed and compared. The AM robotic system demonstrated to be suitable to build these parts, since the tool tip speed and tool tip to substrate distance are controlled, and the tool path optimized. It was also observed that vibration did not negatively affect the built parts quality.O Wire Arc Additive Manufacturing (WAAM) é um processo de Manufatura Aditiva (MA) que apresenta elevadas taxas de deposição a custos reduzidos sendo adequado para produzir peças de grandes dimensões. O Hot-Forging WAAM (HF-WAAM) é uma variante do WAAM que usa um martelo oscilante para forjar o material à medida que este vai sendo depositado, melhorando as propriedades mecânicas e a microestrutura das peças produzidas. Este trabalho tem como objetivo usar e validar o WAAM e HF-WAAM para robotizar a produção de peças metálicas com geometria complexa. Para isto, uma tocha de soldadura com capacidade de realizar forjamento foi redesenhada, fabricada, e montada num robô manipu-lador de 6 graus de liberdade (6-DoF). Foram produzidas peças em aço inoxidável 316LSi uti-lizando os processos de WAAM e HF-WAAM. Durante a sua produção, o sinal de vibração do robô foi adquirido e posteriormente processado e comparado. O sistema robótico de MA demonstrou ser adequado para produzir peças quando a velocidade da ponta da ferramenta e a distância da ponta da ferramenta ao substrato estavam controladas e o percurso da ferramenta otimizado. Também se observou que a vibração não afetou negativamente a qualidade das peças produzidas

AUTOMATIC ERROR DETECTION AND CORRECTION IN LASER METAL WIRE DEPOSITION - AN ADDITIVE MANUFACTURING TECHNOLOGY

Additive manufacturing (AM) technology involves building three-dimensional objects by adding material layer-upon-layer under computer control. Metal additive manufacturing offers new possibilities, not only in design, but also in the choice of materials. However, the additive process remains at a lower maturity level compared to the conventional subtractive processes such as milling, drilling and machining among others. Scientifically, there is a safety concern relating to the accuracy of the AM process, how printed products will perform over time and the consistency of their quality. Process accuracy and eventual part quality is compromised due to errors introduced by each of the building steps in the process.Laser metal deposition with wire (LMD-w) is an additive manufacturing technology that involves feeding metal wire through a nozzle and melting the wire with a high-power laser. The technology is being largely researched for use in the aerospace industry to fabricate large aircraft components. With efficient process control, i.e. sensing, processing, and feedback correction of errors, the LMD-w technology has the potential to change the course of manufacturing. However, a prominent limitation in LMD-w is the difficulty in controlling the process.This work proposes a method for detecting surface geometry errors in a deposited layer in the LMD-w process via laser height scanning and high-speed image processing. The controlled process is simplified into a linear system. The aim is to develop an effective sensing and correction module that automatically detects irregularities in each layer before proceeding to subsequent layers, which will reduce part porosity and improve inter-layer bond integrity

Multimodal sensor fusion for real-time location-dependent defect detection in laser-directed energy deposition

Real-time defect detection is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM). Traditional in-situ monitoring approach utilizes a single sensor (i.e., acoustic, visual, or thermal sensor) to capture the complex process dynamic behaviors, which is insufficient for defect detection with high accuracy and robustness. This paper proposes a novel multimodal sensor fusion method for real-time location-dependent defect detection in the robotic L-DED process. The multimodal fusion sources include a microphone sensor capturing the laser-material interaction sound and a visible spectrum CCD camera capturing the coaxial melt pool images. A hybrid convolutional neural network (CNN) is proposed to fuse acoustic and visual data. The key novelty in this study is that the traditional manual feature extraction procedures are no longer required, and the raw melt pool images and acoustic signals are fused directly by the hybrid CNN model, which achieved the highest defect prediction accuracy (98.5 %) without the thermal sensing modality. Moreover, unlike previous region-based quality prediction, the proposed hybrid CNN can detect the onset of defect occurrences. The defect prediction outcomes are synchronized and registered with in-situ acquired robot tool-center-point (TCP) data, which enables localized defect identification. The proposed multimodal sensor fusion method offers a robust solution for in-situ defect detection.Comment: 8 pages, 10 figures. This paper has been accepted to be published in the proceedings of IDETC-CIE 202

Open-Source TIG-Based Metal 3D-Printing

Metal 3-D printing has been relegated to high-cost proprietary high-resolution systems and low-resolution low-cost metal inert gas (MIG) systems. In order to provide a path to high-resolution, low-cost, metal 3-D printing, this manuscript proposes a new open source metal 3-D printer design based around a low-cost tungsten inert gas (TIG) welder coupled to a commercial open source self replicating rapid prototyper. Optimal printing parameters for the machine are acquired using a novel computational intelligence software. TIG has many advantages over MIG, such as having a low heat input, clean beads, and the potential for both high-resolution prints as well as insitu alloying of complex geometries. The design can be adapted to most RepRap-class systems and has a basic yet powerful free and open source software (FOSS) package for the characterization of the 3-D printer. This system can be used for fabricating custom metal scientific components and tools, near net-shape structural metal component rapid prototyping, adapting and depositing on existing metal structures, and is deployable for in-field prototyping for appropriate technology applications