Purpose Modeling and control of bead geometry in wire and arc additive manufacturing is significant as it affects the whole manufacturing process. The purpose of this paper is to establish an efficient model to control the bead geometry with fewer experiments in wire and arc additive manufacturing (WAAM). Design/methodology/approach A multi-sensor system is established to monitor the process parameters and measure the bead geometry information. A dynamic parameters experimental method is proposed for rapid modeling without dozens of experiments. A deep learning method is used for bead modeling and control. To adaptively control the bead geometry in real-time, a closed-loop control system was developed based on the bead model and in situ monitoring. Findings A series of experiments were conducted to train, test and verify the feasibility of the method and system, and the results showed that the proposed method can build the bead model rapidly with high precision, and the closed-loop system can control the forming geometry adaptively. Originality/value The proposed modeling method is novel as the experiment number is reduced. The dynamic parameters experimental method is effective with high precision. The closed-loop control system can control the bead geometry in real-time. The forming accuracy is elevated.

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Purpose The modeling and optimization of a weld bead in the middle of the weld are often simple, as the forming process is dynamically balanced. However, the arc striking (AS) and arc extinguishing (AE) areas of weld beads are generally abnormal because the dynamic processes at these areas are unstable. The purpose of this paper is to investigate the abnormal areas of the weld bead with optimization modeling methods in wire and arc additive manufacturing (WAAM). Design/methodology/approach A burning-back method was proposed to fill the slanted plane in the AE area. To optimize the welding parameters and obtain the optimal design, a response surface methodology was proposed to build the relationships between the input parameters and response variables. Findings The proposed burning-back method could fill the slanted plane in the AE area. Second-order models of abnormal areas were developed and the optimization effects were analyzed. The experimental results indicated that the relationship models at both ends were applicable and preferable for the optimization of weld beads. Originality/value In this paper, a burning-back method was proposed to optimize the slanted plane in the AE area. Second-order models of abnormal areas were established. The methods and models were preferable in the optimization of the abnormal areas in WAAM.

Lack of monitoring and quality control of in situ forming process is a major bottleneck in Wire and Arc Additive Manufacturing (WAAM). Among various process control, the control of the geometric signatures is of high importance. This paper presents a method of in situ monitoring and closed-loop control of topography measurement. A laser-based areal topography measurement sensor is used to measure the surface geometric signatures of the formed layer. A topography control strategy is carried out by processing and analyzing the 3D surface profile data. To increase the computing speed and efficiency, a depth-image method is proposed to process the 3D surface point clouds data and extract the 3D contour signatures of the shaped layer. Experiments were designed to verify the feasibility and stability of the system and the experimental results indicate that the proposed method can adaptively deal with the surface topography quality and handle the concave and convex parts of the shaped layer.

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