Additive manufacturing (AM) becomes a critical technology that increases the speed and flexibility of production and reduces the lead time for high-mix, low-volume manufacturing. One of the major bottlenecks in further increasing its productivity lies around its post-processing procedures. This work focuses on tackling a critical and inevitable step in powder-bed additive manufacturing processes, i.e., powder cleaning or de-caking. Critical concerns can be raised with human involvement when performing this task manually. Since then, a robot-driven automatic powder cleaning system could be an alternative to reducing time consumption and increasing safety for AM operators. However, since the color and surface texture of the powder residuals and the sintered parts are similar from a computer vision perspective, it can be hard for robots to plan their cleaning path. This study proposes a machine learning framework which incorporates image segmentation and eye tracking to de-cake the parts printed by a powder bed additive manufacturing process. The proposed framework intends to partially incorporate human biological behaviors to increase the performance of an image segmentation algorithm to assist the path planning for the robot de-caking system. The proposed framework is verified and evaluated by comparing it with the state-of-the-art image segmentation algorithms.

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