Maximizing the residual value of retired products and reducing process consumption and resource waste are vital for Generalized Growth-oriented Remanufacturing Services (GGRMS). Under the GGRMS, the traditional product-oriented remanufacturing methods to be changed: the products in GGRMS should be divided into multiple parts for maximizing residual value of different parts. However, this increases the difficulty of resource matching for service activities. To improve the efficiency of resource matching, we first used rough-fuzzy number and structural entropy weighting method to perform a coupling analysis on all service activities in the generalized growth scheme set, and to merge redundant service activities. We then considered the interests of both the service providers and integrators and added flexible impact factors to establish a service resource optimization configuration model, and solved it with the Non-Dominated Sorting Genetic Algorithm (NSGA-II). Finally, we, using a retired manual gearbox an experiment, optimized the service resource allocation for its generalized growth scheme set. The experimental results shown that the overall matching efficiency was increased by 74.56% after merging redundant service activities, showing that the proposed method is suitable for the resource allocation of the generalized growth for complex single mechanical products, and can offer guidelines to the development of RMS.

Mineral image classification technology based on machine vision is an efficient system for ore sorting. With the development of artificial intelligence and computer technology, the deep learning-based mineral image classification system is gradually applied to ore sorting. However, there is a bottleneck in improving classification accuracy, and the feature extraction ability of the CNNs model is relatively limited for multi-category mineral image classification tasks. Therefore, four visual attention blocks are designed and embedded in the existing CNNs model, and new mineral image classification models based on the visual attention mechanism and CNNs are proposed. Then, referring to the building strategies of the different depth ResNet, we build various CNNs model embedding with attention blocks for mineral image classification and visualize the models by Grad-CAM to observe the change in classification weight distributions and classification weight values. Finally, by using the confusion matrices, this experiment systematically evaluates the classification performance of the proposed models and analyzes the misjudgment rate.