The noise from other sources is inevitably mixed in the vibration information of CNC machine tools obtained using the sensors. In this work, a de-noising method based on joint analysis is proposed. The variational mode decomposition (VMD), correlation analysis (CA), and wavelet threshold (WT) denoising are used to denoise the original signal. First, VMD decomposes noisy signals into multiple intrinsic mode functions (IMFs). The penalty factor and decomposition level of VMD parameters are selected by the optimization algorithm by combining the whale optimization algorithm (WOA) and tabu search (TS). The minimum permutation entropy of IMF is used as the fitness function of the proposed fusion algorithm. Then, the IMF is divided into three categories by using the cross-correlation number. They include the pure components, signals containing noise, and complete noise components. Then, the WT method is used to further denoise the signals, and signal reconstruction is performed with the pure component to obtain the denoised signal. This joint analysis denoising method is named TS-WOA-VMD-CA-WT. The simulation results show that the fusion optimization algorithm proposed in this work has better performance as compared to the single optimization algorithm. It performs effectively when applied to the actual machine tool vibration signal denoising. Therefore, the proposed TS-WOA-VMD-CA-WT method is superior to other existing denoising techniques and has good generality, which is expected to be popularized and applied more widely.

Traditional product design evaluation processes are resource-intensive and time-consuming, resulting in unsustainably higher costs and longer lead times. Therefore, sustainable product design evaluation has become an increasingly crucial aspect of product design, focusing on creating a high-efficiency, high-reliability, and low-carbon-emission approach. In this study, we proposed an integrated approach that combines manual design evaluation based on the analytic hierarchy process (AHP) with an automatic design evaluation based on a ResNet-50 network in order to develop a sustainable design evaluation method. First, the evaluation level and indicators for the shape design of a tail-light were defined using the AHP. We followed this by establishing a determination matrix and weight coefficients for the design indicators to create a manual design evaluation model. Second, tail-light shape image datasets were manually annotated based on the evaluation indicators, and design datasets were constructed. The ResNet-50 algorithm was introduced to train the datasets, and the automatic evaluation model for product design was constructed through training and tuning. Finally, we validated the feasibility and effectiveness of the product design evaluation method, which was based on AHP and ResNet-50, by comparing the results obtained using both manual design and automatic design evaluations. The results showed that the proposed sustainable product design evaluation model provides an efficient and reliable method for evaluating product design, improves the decision-making process, and empowers the design and development process. The model enhances resource efficiency and economic sustainability.

CNC machine tools play an important role in manufacturing and are characterized by high total energy consumption and low energy efficiency. The energy consumption characteristics of the machine tool itself determine the total energy consumption and pollutant emission during its service life. Therefore, it is particularly important to design machine tools with energy consumption as the optimization target to analyze the composition of energy consumption and related characteristics, build a corresponding model based on reliability verification, guide the structural design and optimization according to the model, and ultimately use the evaluation system to evaluate and judge the overall energy consumption. In this paper, from four perspectives—the composition of the energy consumption of machine tools, modeling methods, design and optimization methods, and evaluation methods—with energy consumption optimization as the entry point, we analyze the research on CNC machine tools based on energy consumption around the world. The research results indicate that we should look forward to the role of energy consumption in the design of machine tools.

In recent years, Hybrid Cellular Automata Method (HCAM) has been successfully applied to solve structural topology optimization problems. However, there was no report on HCAM research of three-dimensional composite structure composed of multiple materials and multiple bodies. Therefore, in this paper, three-dimensional non-cube cells of irregular size (such as tetrahedral cells with adaptive changes inside length) and Finite Element Method (FEM) are introduced to extend HCAM, which is better and more flexibly to fit complex geometric shapes. Furthermore, a better structure configuration of multi-material and multi-body structure is obtained. The typical example study showed that the proposed topology optimization method could effectively remove the redundant materials of multi-material and multi-body structure, and the optimized structure configuration could still meet the requirements of the original condition after geometric reconstructed. Thus it provided a reference for the intelligent design of other products.

In order to obtain the thermal characteristics of the spindle–bearing integrated system of the computer numerical control (CNC) machine tools effectively, a mathematical model is established by employing the heat source method (HSM). The thermal characteristics of spindle–bearing system are identified by using the derived mathematical formula, and the presented model is validated by the finite element method (FEM) under four types of conditions corresponding to different heat intensities, heat transfer coefficients, geometrical model sizes, and heat source positions. Compared with the FEM, the presented model has better computational efficiency. The temperature fields of the two spindle systems of a CNC machine tool are predicted by using the present model. The predicted temperature field is compared with the measured data and results show that the maximum relative errors for the two systems are 0.41% and 8.38%, respectively. The proposed model has a potential to be applied in calculating temperature field and thermal deformation or other related engineering area.