High-frequency mechanical impact (HFMI) treatment is a well-documented post-weld treatment to improve the fatigue life of welds. Treatment of the weld toe must be performed by a skilled operator due to the curved and inconsistent nature of the weld toe to ensure an acceptable quality. However, the process is characterised by noise and vibrations; hence, manual treatment should be avoided for extended periods of time. This work proposes an automated system for applying robotised 3D scanning to perform post-weld treatment and quality inspection of linear welds. A 3D scan of the weld is applied to locally determine the gradient and curvature across the weld surface to locate the weld toe. Based on the weld toe position, an adaptive robotic treatment trajectory is generated that accurately follows the curvature of the weld toe and adapts tool orientation to the weld profile. The 3D scan is reiterated after the treatment, and the surface gradient and curvature are further applied to extract the quantitative measures of the treatment, such as weld toe radius, indentation depth, and groove deviation and width. The adaptive robotic treatment is compared experimentally to manual and linear robotic treatment. This is done by treating 600-mm weld toe of each treatment type and evaluating the quantitative measures using the developed system. The results showed that the developed system reduced the overall treatment variance by respectively 26.6% and 31.9%. Additionally, a mean weld toe deviation of 0.09 mm was achieved; thus, improving process stability yet minimising human involvement.
SUMMARY
This paper presents a novel solution for precision draping of prepreg composite fabrics onto double curved molds. Our contributions relate to system design, including hardware and software components, and to system integration. On the hardware side, design and implementation of a drape tool with up to 120 suction cups positioned individually by linear actuators are described. On the software side, design and implementation of the software architecture are presented, along with necessary algorithms within sensor technologies and mathematical modeling. The essential system’s components were verified individually, and the entire integrated system was successfully validated in the Proof-of-Concept experiments, performed on an experimental physical model of the system.
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