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.

Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing and parallel neural network is proposed. Firstly, the relationship between CNC program and energy consumption of CNC machine tool is analyzed. Based on the structural characteristics of the CNC program, an automatic parsing algorithm for the CNC program is proposed. Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed. Finally, the proposed method is compared with the literature to verify the superiority of the proposed method in terms of prediction efficiency and accuracy, and the practicability of the method is verified through the case study. The proposed method lays the foundation for efficient and low-consumption process planning and energy efficiency improvement of machine tools and is conducive to the sustainable development of the environment.

Remanufacturing has become an important way to realize sustainable development strategy. For remanufacturing closed‐loop supply chain under different circumstances, many factors are considered, such as consumers’ different preferences for the purchase and payment of remanufactured products and the quality of recycled products. In this study, three models are presented for supply chain system, including a manufacturer lead and a retailer recycle. While nongovernment participation and non‐supply‐chain coordination are considered in model I, model II has government participation but non‐supply‐chain coordination, and model III has both government participation and supply chain coordination. The equilibrium decisions are derived for each model using the game‐theoretic approach. The study found that consumers’ preferences affect the pricing decisions of remanufactured products. When consumers’ willingness to buy and pay for remanufactured products is close to that of new products, the total profit of the supply chain will significantly increase; the quality of recycled products will also influence the pricing decisions of new/remanufactured products, which in turn will affect the market demand for products. Government participation is conducive to encouraging manufacturer, retailer, and consumers to actively participate in the recycling and remanufacturing activities of used products. Supply chain coordination contracts can further enhance the role of the consumer market and promote the implementation of government policies. Under certain conditions, revenue sharing contract coordination can significantly increase the total profit of the supply chain system. The research conclusions have certain reference value for the coordination among the nodes of the remanufacturing supply chain and the formulation of policies with government participation.

Recycling and remanufacturing logistics network affects the scale and efficiency of sustainable development of the manufacturing industry. This paper designs a multi-level closed-loop supply chain network with supplier, manufacturer, recycling centers, preprocessing centers and processing centers. An improved nonlinear grey Bernoulli-Markov model is developed to predict the recycled quantity. The capacity of recycling center and preprocessing center, the demand of manufacturer and the inventory of preprocessing center are formulated as constraints. A dynamic multi-objective model, which is based on scalable logistics facilities, takes into account the minimization of system operating costs and minimization of time costs related to the out-of-stock and inventory in each operating cycle. This model realizes the dynamic selection of the scale of facilities. Objective weighted genetic algorithm is adopted to transform multi-objective optimization problem into a single-objective. A scrap automobile products calculations are analyzed to verify the effectiveness and practicability of this model.