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Process energy consumption and environmental impact have been considered as important performance indicators for the sustainable electrical discharge machining (EDM) process. This paper presents a sustainable manufacturing technique known as magnetic field-assisted EDM (MF-EDM) to enhance the machine characteristics for the purpose of reducing the energy consumption and environmental hazards of the conventional EDM machining Ti6Al4V. Firstly, the principles of energy consumption, machining noise impact, and MF-EDM are respectively described. Thereafter, a set of experiments is carried out to investigate the effects of the main process parameters on the electrode wear rate ( E W R ), energy consumption ( S E C ), and environmental impacts (including carbon emission and machining noise) extensively, using the Taguchi method. The results indicate that the pulse on time (ranging from 100 to 200 μs) and magnetic field intensity (ranging from 0.05 to 0.10 T) are the two most significant factors affecting the MF-EDM sustainable manufacturing performance. Furthermore, the optimal process parameters were obtained by optimizing the MF-EDM process economically and environmentally using the modified non-dominated neighbor immune algorithm (M-NNIA) method. Compared to the minimum outputs of the experimental results, the optimal solutions of the E W R , S E C , and machining noise were significantly decreased, by 18.30%, 61.43%, and 20.95%, respectively. Therefore, it can be concluded from the above research that the proposed hybrid MF-EDM technique offers significant advantages and potential for applications in the sustainable manufacturing field.

Ultra-thin curved (2.5D, 3D) glass is widely used in the most recently developed smartphones, and the market demand for it is steadily increasing. The high-precision ultra-thin glass molding process (UTGMP) is very important in the fabrication of high-quality 2.5D/3D ultra-thin glass for smartphones; however, the UTGMP, with its high energy consumption and low pass rate, is not considered to be environment-friendly. Therefore, in this study, the sustainability of the molding process for 3D ultra-thin glass was comprehensively investigated. First, the ultra-thin 3D glass molding process is described, including a discussion of the glass molding apparatus, and the principle of glass thermos-formation is explained. Secondly, a thermo-mechanical coupling model is proposed to study the effects of loading heat and pressure on the distributions of temperature and internal stress of ultra-thin glass. The results demonstrate that significant amounts of stress were concentrated at the edges, and in holes and slots. A series of experiments were conducted to reveal the respective effect of various UTGMP parameters on the energy consumption, pass rate, and carbon emission level of the process. Consequently, the percentage of the main effect of loading temperature on energy consumption was found to be 23.65%, whereas that of the duration of applied pressure on the pass rate was determined to be 52.48%. The optimal process parameter combinations were determined by analyzing the above-mentioned research results and subsequently implementing them in a hybrid intelligent optimization algorithm that utilizes quantum-behaved particle swarm optimization (QPSO) and a back-propagation neural network (BPNN). The algorithm revealed that energy consumption and carbon emissions can be respectively decreased by 3.39% and 3.45% at a relatively high pass rate (i.e., over 70%). Eventually, the suggested combinations for the UTGMP will be able to achieve at least 60%–76% of the pass rate, as well as consume no more than 0.756 kW ⋅ h/pcs to meet sustainable manufacturing requirements. Thus, this study contributes to the effective application of a high-precision glass molding process for the sustainable manufacturing of ultra-thin curved glass.