Ensemble of convolutional neural networks based on an evolutionary algorithm applied to an industrial welding process

This paper presents an approach for image classification based on an ensemble of convolutional neural networks and the application to a real case study of an industrial welding process. The ensemble consists of five convolutional neural networks, whose outputs are combined through a voting policy. In order to select appropriate network parameters (i.e., the number of convolutional layers and layers hyperparameters) and voting policy, an efficient search process was carried out by using an evolutionary algorithm. The proposed method is applied and validated in a case study focused on detecting misalignment of metal sheets to be joined through submerged arc welding process. After selecting the most convenient setup, the ensemble outperforms other seven strategies considered in a comparison in several metrics, while maintaining an adequate computational cost. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces an image classification approach based on an ensemble of convolutional neural networks and its application in a real industrial welding case study. Through an efficient search process, the method outperforms other strategies in detecting misalignment of metal sheets with maintained computational cost.