The stability of the forming layer shape is a critical factor that impacts the final quality of sample morphology in wire and arc additive manufacturing (WAAM). This paper investigates the forming process and control methods to identify ways to optimize the process and improve the quality of the final product. The study aims to enhance the quality and precision of samples produced by the WAAM process by establishing a morphology control method based on image feedback. The focus is on real-time image acquisition using a CCD and simultaneous extraction of forming process parameter characteristics. Using a central composite experimental design, a prediction model is developed to estimate key process parameters and feature sizes of deposits, including deposition height and width. To further analyze the dynamic characteristics of feature sizes of deposited layers, step response identification is conducted using three process parameters as the system input: forming speed, arc current, and wire feeding speed. The experiments are designed to determine the weight of each process parameter and achieve a high level of response speed and precision. The findings indicate that the feature size of the deposited layer is most sensitive to changes in forming speed, followed by wire feeding speed, while arc current has the least impact on feature size. After verification, the monitoring of the deposition height and width was found to be in good agreement with the prediction model, with an accuracy of over 90%. The results of this study can be used for size measurement and optimization of large aviation aluminum alloy components using WAAM technology and to improve the quality of products produced using these processes.
Fused plus wire arc additive manufacturing is a low cost and high efficiency manufacturing technology for large scale metal parts. Aiming at the difficulty and low accuracy of component size information acquisition under strong arc light, this paper designs a laser bandpass composite filter system based on the intensity of the spectral characteristics. The component size features are extracted by image processing algorithms such as edge detection, threshold division, and line fitting. Through the adaptive control of the forming speed, the deposition width deviation is automatically corrected. The results show that under strong arc light, the feature size accuracy monitoring reaches 91.79%, and the clear image of deposition layer can be obtained effectively. This method can realize the effective forming with the deposition width of 5.5–10 mm and can accurately change the deposition layer width on demand. The dynamic control value error of the deposition feature size is less than 0.4 mm, that is, the control system can effectively ensure the component surface accuracy.
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