Driven by energy shortages and climate concerns, the electric vehicles are popular around the world with their energy-saving and environmentally friendly advantages. As electric vehicle batteries (EVBs) mainly use lithium batteries, and the batteries’ performance decreases with the increase of charging times, a large number of batteries are entering the end-of-life (EoL) stage. Recycling and reuse of EVBs are effective ways to reduce environmental pollution and promote resources utilization and is now a top priority. Building a recycling network is the foundation of battery recycling. However, there are few studies on battery recycling networks and the construction of recycling networks is expensive, which impedes the sustainable development of electric vehicles. Based on this, recycling network design is critical for EVB recycling. This paper first analyzes three strategies to deal with used batteries: remanufacturing, reuse, and recycling materials. Secondly, an EVB recycling network model is developed with the objective of minimizing the total cost and carbon emissions. The model solves the problem of siting the centers in the network and the vehicle routing in the recycling process. Finally, the model was applied to GEM (a Chinese company dedicated to circular economy) and validated using a greedy algorithm. In addition, the results show that logistics costs and operating costs account for the majority of the recycling network total expense, at 48.45% and 31%, respectively. Therefore, if companies want to further reduce the cost of EVB recycling, they should reduce logistics costs and operating costs. In summary, this paper provides a decision-making approach for EVB recycling enterprises to carry out recycling and reuse, and offers advice on how to promote the sustainable economic and environmental development of the electric vehicle battery industry.

The rapid increasing number of automobile products has brought great convenience to people’s living, but it has also caused serious environmental issues, waste of resources and energy shortage during its whole lifecycle. Corporate Environmental Responsibility (CER) refers to the company’s responsibility to avoid damage to the natural environment derived from its corporate social responsibility (CSR), and it plays an important role in solving resource and environmental problems. However, due to various internal and external reasons, it is difficult for the automobile manufacturing industry to find the key drivers for the implementation of CER. This research proposes a model framework that uses the fuzzy decision-making test and evaluation laboratory (fuzzy DEMATEL) method to analyze the drivers of CER from the perspective of the triple bottom line (TBL) of economy, environment and society. Firstly, the common drivers of CER are collected using literature review and questionnaire survey methods. Secondly, the key drivers are analyzed by using the fuzzy DEMATEL. Finally, the proposed approach was verified through a case study. The research results show that some effective measures to implement CER can be provided for the government, the automobile manufacturing industry and the public to promote sustainable development of Chinese Auto Manufacturing Industry (CAMI).

Modeling and estimating the energy consumption of computer numerical control (CNC) milling systems have been recognized as essential ways to realize lean energy consumption management and improve energy efficiency performance. As the preparatory phase, considerable time and energy are consumed in the tool setting process. However, research on the tool setting process mainly focuses on accuracy and operational efficiency, and the energy consumption is usually ignored or simplified. Accurately estimating the energy consumption of the tool setting process is thus indispensable for reducing the energy consumption of CNC milling systems and improving their energy efficiency. To bridge this gap, an energy consumption estimation method for the tool setting process in CNC milling based on the modular arrangement of predetermined time standards (MODAPTS) is presented. It includes three steps: (i) operations decomposition and determination of the MODAPTS codes for the tool setting process, (ii) power modeling of the basic action elements of the machine tool, and (iii) energy consumption modeling of the tool setting process. Finally, a case study was conducted to illustrate the practicability of the proposed method via energy consumption modeling of the tool setting process using an XH714D CNC machine center with a square workpiece, in which the estimation values of the operating time and the energy consumption for the tool setting process were 210.786 s and 140,681.68 J, respectively. The proposed method can increase the transparency of energy consumption and help establish labor-hour quotas and energy consumption allowances in the tool setting process.

The uncertainty failure of the used part leads the complexity selection of remanufacturing processes. The different remanufacturing process combinations among used parts of used products also make the formulation of tolerance schemes more difficult. It is hard to guarantee the optimality of process-tolerance schemes by traditional serial production modes in general, in which tolerance design is followed by process formulation. In order to generate the optimal remanufacturing scheme of process and tolerance for used products, an optimization method to integrate designs of process-tolerance (IDP-T) based on fault features was presented. In this work, the failure description set of used parts was constructed by combining the attribute characteristics and failure characteristics. Case-based reasoning (CBR) was first utilized to generate the feasible remanufacturing process plans of used parts. Then, based on the feasible process plans, the factors of cost, quality loss, closed-loop accuracy and machining ability of remanufacturing were comprehensively considered to construct the optimization model of IDP-T. The Beetle Antennae Search algorithm (BAS) was used for the optimal alternative selection. Finally, a used gearbox was taken as an example to illustrate the validity and practicality of the proposed method. The results showed that the proposed method was effective in the optimization of IDP-T for remanufacturing.

Modeling and assessing the sustainability of machining systems has been considered to be a crucial approach to improving the environmental performance of machining processes. As the most common machining system, the computer numerical control (CNC) milling system is a typical man–machine cooperative system where the activities of the machine tool and operator generate material and energy consumption. However, the energy consumption of the operator in the CNC milling system has often been ignored in most existing research. Therefore, existing methods fail to provide a comprehensive understanding of the sustainability of the CNC milling system. To fill this gap, an exergy loss assessment method is proposed to investigate the sustainability of the CNC milling system, where the energy consumption of the operator, the energy consumption of the machine tool, and material consumption are taken into consideration. The key performance indexes of the energy consumption of the operator, the energy consumption of the machine tool, the exergy loss, and the specific exergy loss (SEL) are analyzed and modeled. To demonstrate the feasibility of the proposed method, a case study was carried out on a three-axis machining center (XH714D), in which the energy consumption of the operator, the energy consumption of the machine tool, the exergy loss of energy consumption, the exergy loss of chips, the exergy loss of compressed air, the exergy loss of cutting tool wear, the exergy loss of cooling liquid dissipation, and the SEL were found to be 169,750 J, 758,211 J, 603,131 J, 2,031,404 J, 22,023 J, 301,868 J, 2673 J, and 88.04 J/mm³, respectively. The proposed method is effective to assess the sustainability of the CNC milling system, and the established exergy loss models build a good basis for exergy efficiency optimization.

Machine tools, as an indispensable equipment in the manufacturing industry, are widely used in industrial production. The harsh and complex working environment can easily cause the failure of machine tools during operation, and there is an urgent requirement to improve the fault diagnosis ability of machine tools. Through the identification of the operating state (OS) of the machine tools, defining the time point of machine tool failure and the working energy-consuming unit can be assessed. In this way, the fault diagnosis time of the machine tool is shortened and the fault diagnosis ability is improved. Aiming at the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional OS recognition methods, a deep learning method based on data-driven machine tool OS recognition is proposed. Various power data (such as signals or images) of CNC machine tools can be used to recognize the OS of the machine tool, followed by an intuitive judgement regarding whether the energy-consuming units included in the OS are faulty. First, the power data are collected, and the data are preprocessed by noise reduction and cropping using the data preprocessing method of wavelet transform (WT). Then, an AlexNet Convolutional Neural Network (ACNN) is built to identify the OS of the machine tool. In addition, a parameter adaptive adjustment mechanism of the ACNN is studied to improve identification performance. Finally, a case study is presented to verify the effectiveness of the proposed approach. To illustrate the superiority of this method, the approach was compared with traditional classification methods, and the results reveal the superiority in the recognition accuracy and computing speed of this AI technology. Moreover, the technique uses power data as a dataset, and also demonstrates good progress in portability and anti-interference.