Designs for remanufacturing (DfRem) consider the remanufacturability of the product in the early stages of product design, which can greatly increase the reusability of the products. However, product design schemes lack reasonable evaluation indicators for remanufacturability, and the decision-makers of the design scheme have subjective preferences and vague hesitation. These result in inaccurate decision making on DfRem schemes that will affect the successful implementation of product remanufacturing. In order to improve the accuracy of the DfRem scheme decision, a fuzzy decision-making method for green design for remanufacturability is proposed. Firstly, an evaluation indicator system for green design schemes was established that takes into account remanufacturability, reliability, cost, and the environment, and the entropy weighting method is used to quantify and weigh the design scheme evaluation indicators. Then, the hesitation fuzzy set is applied to construct the set of evaluations and the optimal design scheme is selected by applying the comprehensive evaluation method. Finally, the feasibility of the above method is verified by using the green design of an injection mold as an example, and the results show that the above method is able to make accurate and effective design scheme decisions. This method has been implemented in a prototype system using Visual Studio 2022 and Microsoft SQL Server 2022. The results show that the fuzzy decision-making system is accurate and effective for rapidly generating a rational green design scheme for remanufacturability.

With the rapid development of image recognition technology, freehand sketch recognition has attracted more and more attention. How to achieve good recognition effect in the absence of color and texture information is the key to the development of freehand sketch recognition. Traditional nonlearning classical models are highly dependent on manual selection features. To solve this problem, a neural network sketch recognition method based on DSCN structure is proposed in this paper. Firstly, the stroke sequence of the sketch is drawn; then, the feature is extracted according to the stroke sequence combined with neural network, and the extracted image features are used as the input of the model to construct the time relationship between different image features. Through the control experiment on TU‐Berlin dataset, the results show that, compared with the traditional nonlearning methods, HOG‐SVM, SIFT‐Fisher Vector, MKL‐SVM, and FV‐SP, the recognition accuracy of DSCN network is improved by 15.8%, 10.3%, 6.0%, and 2.9%, respectively. Compared with the classical deep learning model, Alex‐Net, the recognition accuracy is improved by 5.6%. The above results show that the DSCN network proposed in this paper has strong ability of feature extraction and nonlinear expression and can effectively improve the recognition accuracy of hand‐painted sketches after introducing the stroke order.

The design for the remanufacturing process (DFRP) is a key part of remanufacturing, which directly affects the cost, performance, and carbon emission of used product remanufacturing. However, used parts have various failure forms and defects, which make it hard to rapidly generate the remanufacturing process scheme for simultaneously satisfying remanufacturing requirements regarding cost, performance, and carbon emissions. This causes remanufactured products to lose their energy-saving and emission-reduction benefits. To this end, this paper proposes an integrated design method for the used product remanufacturing process based on the multi-objective optimization model. Firstly, an integrated DFRP framework is constructed, including design information acquisition, the virtual model construction of DFRP solutions, and the multi-objective optimization of the remanufacturing process scheme. Then, the design matrix, sensitivity analysis, and least squares are applied to construct the mapping models between performance, carbon emissions, cost, and remanufacturing process parameters. Meanwhile, a DFRP multi-objective optimization model with performance, carbon emission, and cost as the design objectives is established, and a teaching–learning based adaptive optimization algorithm is employed to solve the optimization model to acquire a DFRP solution satisfying the target information. Finally, the feasibility of the method is verified by the DFRP of the turbine blade as an example. The results show that the optimized remanufacturing process parameters reduce carbon emissions by 11.7% and remanufacturing cost by USD 0.052 compared with the original process parameters, and also improve the tensile strength of the turbine blades, which also indicates that the DFPR method can effectively achieve energy saving and emission reduction and ensure the performance of the remanufactured products. This can greatly reduce the carbon emission credits of the large-scale remanufacturing industry and promote the global industry’s sustainable development; meanwhile, this study is useful for remanufacturing companies and provides remanufacturing process design methodology support.