In the world of engineering equipment, customized parts possess enormous size spans and intricate designs. Current research is insufficient in terms of adsorption score and time consumption. This work proposes a new method based on neural network acceleration and the group optimization algorithm to address these problems. First, the network model and synthesized part image are combined to classify the part’s shape. Then, the improved GWO algorithm will search the adsorption pose for parts that are categorized as irregular kinds. Finally, in order to accelerate the search process, the 1024 vectors of part images and the parallel optimal poses are saved for subsequent retrieval. Numerous experiments demonstrate that the accuracy of the network model is close to 100%, and its judge time is 285 times lower than the traditional method. In addition, compared with the current SOTA research, our adsorption score improves 4.68%, which fully demonstrates the advantage of our method in industrial production. This is the first report that a deep neural network is used to accelerate the adsorption of industrial parts well.
Feng et al. (Thu,) studied this question.
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