This study investigates how to minimise environmental cost and maximise economic benefits in the turning process with regard to high carbon emission characteristics in mechanical processing enterprises. Based on the processing task-based evaluation method, carbon emissions and cost are taken as the optimisation objectives. Based on the multi-objective optimisation algorithm concept, NSGA-II was selected to obtain the Pareto frontier. In addition to carbon emissions and cost, the effects of processing time are simultaneously considered, and the Pareto frontier is sorted using the TOPSIS method to determine the optimal combination of the cutting parameters. Contour and single factor analyses were carried out to analyse the influence of cutting parameters on the optimisation objectives. The concept of the processing interval with high economy and low environmental cost was proposed and the calculation method of the interval was presented, which is helpful for mechanical processing enterprises to select reasonable cutting parameters on the premise of meeting the environmental costs and economic benefits.

To address the poor cutting stability and deterioration of hole quality caused by the inherent trade-off between chip evacuation performance and drill-body stiffness in U-drilling, a multi-objective optimization framework was established. The design variables were the core thicknesses ${\mathrm{L}}_1$ and ${\mathrm{L}}_2$ of the inner and outer chip flutes, the inner and outer offset angles ${\mathrm{\theta }}_1$ and ${\mathrm{\theta }}_2$, and the inner and outer helix angles ${\mathrm{\beta }}_1$ and ${\mathrm{\beta }}_2$. The objectives were to maximize the chip evacuation force and minimize the drill-body strain (which serves as an equivalent indicator of maximizing drill-body stiffness). The chip evacuation force was rapidly evaluated using a mechanistic chip evacuation force model derived from mechanism-based analysis. The drill-body strain was efficiently predicted using a GA–BP neural-network surrogate model. An NSGA-II algorithm combined with the entropy-weighted TOPSIS method was employed to solve the optimization problem, yielding the optimal parameter combination for the U-drill chip-flute geometry. The results show that drilling experiments on 42CrMo under the optimal structural parameter combination reduced the cutting forces in the $x$, $y$, and $z$ directions by approximately 11.2%, 13.1%, and 11.8%, respectively. The root-mean-square acceleration in the $x$ and $y$-directions decreased by about 17.3% and 22.9%, respectively. These improvements effectively enhanced the hole-wall surface roughness and hole diameter accuracy, and further improved chip evacuation smoothness and cutting stability of the U-drill.

The evaluation of the energy performance of spindle systems with a proper metric is extremely important for energy-efficient machine tools. However, there is a lack of appropriate index to achieve this evaluation. To close this gap, the additional power requirement, which is defined as the discrepancy between the material removal power and the power gap of the spindle system from air-cutting state to cutting state, was proposed as a new indicator based on a detailed power flow analysis. The additional power requirement universally exists in machining and results from the efficiency and power losses of the system, which makes it a good energy performance indicator candidate. A theoretical model for the additional power requirement of spindle systems was built and verified in a lathe machine, through which two causes of additional power requirement were pointed: less than 100% efficiency throughout the system and efficiency change of the spindle motor under a dynamic load and a variable speed. The first cause has a positive effect on the additional power requirement, whereas the second one generally plays a negative role. Furthermore, the additional power requirement can be negative because of the negative effect, which is different from previous studies. The close relation between the additional power requirement and the energy performance of the spindle is demonstrated by an in-depth analysis. This work is an important building block for energy-efficient machine tools.

The runout of the cutting edge significantly affects the cutting force, wear, and the process stability. Generally, the postprocessor of five-axis tool grinding machine ignores the clamping error of blank. Hence, this paper developed a postprocessor method with compensation of tool clamping errors to minimize runout of cutting edge for tool grinding machine. This method includes the kinematic model and the measurement model of clamping error for the tool grinding machine. This kinematic model converts CL data into NC data and converts NC data into CL data. The measurement model for measuring and calculating the clamping error of blank. The clamping error was inputted into the kinematic model of machine to compensate the clamping error. The solution strategy of the kinematic model was built, and is suitable for five-axis tool grinding machine. A postprocessor was subsequently developed according to the algorithm presented. Finally, the effectiveness of postprocessor was confirmed by the grinding experiment.

Thin-walled components used in aerospace manufacturing are highly susceptible to machining-induced deformation due to their low structural stiffness and dynamic cutting instability. Although signal-based modeling approaches have been reported for machining process monitoring and performance evaluation, deformation prediction of thin-walled structures requires explicit consideration of structural flexibility. To address this challenge, a deformation error prediction framework integrating multi-source dynamic machining signals with static structural flexibility characteristics is proposed, enabling simultaneous representation of process dynamics and structural response. Kernel principal component analysis (KPCA) is employed to reduce the feature dimensionality, and the extracted low-dimensional features are subsequently used as inputs for a kernel-based support vector regression (KSVR) model to establish the prediction framework. The proposed method was validated through 25 milling experiments conducted on Al7075-T6 thin-walled workpieces, where deformation error was measured at predefined monitoring points under varying process conditions. The results indicate that the proposed model achieves high predictive accuracy for machining-induced deformation, with RMSE values below 13 μm and R² exceeding 0.89 on both validation and testing datasets, demonstrating strong agreement between predicted and experimental results. In addition, machining vibration amplitude exhibits a consistent correlation with deformation error, confirming that increased energy input and cutting instability significantly exacerbate thin-walled workpiece deformation.

Aiming at the problem of non-uniform and non-universal evaluation criteria of machine tools’ carbon emissions in the whole life-cycle analysis, an evaluation method of life-cycle carbon-emission analysis of machine tool based on declared unit was put forward by analyzing and summarizing the existing carbon emission evaluation models. A universal evaluation system for machine-tool life-cycle carbon-emission analysis was first established, and an appropriate declared unit was then selected according to industry characteristics and machine-tool types. Subsequently, an information-flow-based iERWC boundary division method was proposed to support data collection and carbon-emission calculation across five life-cycle stages. To better reflect carbon emissions in the phase of application, the life-cycle inventory incorporates the use of coated cutters, including the associated cutters’ consumption and replacement demands. Two heavy duty floor-milling and boring machine tools produced by Qiqihar No. 2 Machine Tool (Group) Co., Ltd. Were taken as examples to calculate and evaluate life-cycle carbon-emission analysis of machine tools, and the uncertainty analysis was carried out; the possible influencing factors were pointed out to ensure the comprehensive carbon-emission assessment of the whole life cycle.