Realisation of a multi-sensor framework for process monitoring of the wire arc additive manufacturing in producing Ti-6Al-4V parts

Wire arc additive manufacturing (WAAM) is arc welding-based additive manufacture which is providing a major opportunity for the aerospace industry to reduce buy-to-fly ratios from 20:1 with forging and machining to 5:1 with WAAM. The WAAM method can build a wide range of near net shapes from a variety of high-grade (metallic) materials at high deposition speeds without the need for costly moulds. However, current WAAM methods and technologies are unable to produce parts reliably and with consistent structural material properties and required dimensional accuracy. This is due to the complexity of the process and the lack of process control strategies. This article makes a brief review on monitoring methods that have been used in WAAM or similar processes. The authors then identify the requirements for a WAAM monitoring system based on the common attributes of the process. Finally, a novel multi-sensor framework is realised which monitors the system voltage/current, part profile and environmental oxygen level. The authors provide a new signal process technique to acquire accurate voltage and current signal without random noises thereby significantly improving the quality of WAAM manufacturing

MULTI-BEAD AND MULTI-LAYER PRINTING GEOMETRIC DEFECT IDENTIFICATION USING SINGLE BEAD TRAINED MODELS

In Wire Arc Additive Manufacturing (WAAM), a geometric defect is a defect that creates voids in the final printed part due to incomplete fusion between two non-uniform overlapping bead segments. Such a defect posesthe onset of a severe problem during multi-bead prints. In our earlier work, a methodology has been developed to construct machine learning (ML)-based modelsto identify geometrically defective bead segments using acoustic signals. In this paper, we investigate the performance of these single-bead segments trained defect detection model scalability for identifying voids during multi-bead prints. A comparative study of the performance of a variety of ML models is explored based on Inconel 718 material printing. The results show that the single bead segments-based defect identification model can effectively identify defective and non-defective segments in both single-layer multi-bead printing and multi-layer multibead printing.Mechanical Engineerin

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

Influence Of Process Parameter On The Height Deviation Of Weld Bead In Wire Arc Additive Manufacturing

Significant attention towards Wire and Arc Additive Manufacturing (WAAM) has gradually increased. WAAM technology has been proven in building three-dimensional (3D) metal parts efficiently and economically. The combination of wire and arc welding is promising, especially in depositing metals with higher deposition rate, and low cost of raw materials, as well as manufacturing of a large-scale product. However, there are several process parameters should be optimized to ensure no internal defect, acceptable surface finish and consequently to have a good quality of final parts when using WAAM. Therefore, an experimental design using the Taguchi method was used to determine the effect of welding current, welding voltage, and travel speed on the responses, including the deviation in height were investigated. The results revealed that travel speed was the dominant factor affecting the waviness surface structure on top of the 3D metal parts. Besides, the contribution rate for each factor to the deviation in height was also determined

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

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