How to improve layer depalletizing productivity with AI?
In collaboration with DGS Processing Solutions and Viroteq.ai, the DGS depalletizer has been developed to cope with an unlimited variety of boxes. Our AI technology makes it possible to precisely detect pallet stacking patterns which enables DGS to efficiently automate depalletizing processes of boxes with different dimensions and stacking patterns with high capacity.
The use of AI and computer vision technology in the field of depalletizing can significantly enhance the efficiency and accuracy of the process. Depalletizing is the process of removing products from a pallet and sorting them based on their characteristics. It can be a challenging task when the boxes are unknown or have inconsistent shapes and sizes. However, with the use of AI and computer vision, depalletizing unknown boxes can be made easier and more efficient.
Our AI algorithms can analyze the visual data obtained from cameras and sensors to identify the type, size, and position of boxes on a pallet. This information can then be used to plan the best approach for removing the boxes from the pallet. The AI algorithms are trained to recognize various types of boxes and their orientations, making it possible to quickly identify and sort them.
Our computer vision technology can also help in identifying any defects or damages in the boxes, which is important in ensuring product quality. The technology can detect even minor defects that might be missed by human workers, thereby preventing any defective products from being shipped.
One of the significant benefits of using AI and computer vision technology in depalletizing is that it reduces the need for manual labor for these kind of flexible de-stacking processes. The process can be fully automated, resulting in faster and more efficient operations. This reduces the risk of human error and increases overall productivity.
Moreover, our AI algorithms can adapt to changing environments and adjust to new types of boxes or materials. This is particularly useful in the case of depalletizing unknown boxes where the characteristics of the boxes are not known beforehand. The algorithms can quickly learn from the visual data obtained from the cameras and sensors and adapt to new situations.