In this paper, a contour error control strategy based on spatial iterative learning control (sILC) is developed for repetitive processing of five-axis computer numerical control (CNC) machine tools. A curve approximation method with an adaptive moving window is developed to achieve accurate tool position and orientation contour error estimation, and a five-axis contour error control strategy based on sILC is proposed. The compensation method is derived using an sILC algorithm to modify the geometric reference path instead of modifying the controller. The experimental results show that the proposed control strategy reduces contour errors of five-axis CNC machine tools, and it outperforms the traditional tracking error control.

Thermal errors significantly affect the accurate performance of computer numerical control (CNC) machine tools. In this paper, an improved robust thermal error prediction approach is proposed for CNC machine tools based on the adaptive Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms. Specifically, the adaptive LASSO method enjoys the oracle property of selecting temperature-sensitive variables. After the temperature-sensitive variable selection, the XGBoost algorithm is further adopted to model and predict thermal errors. Since the XGBoost algorithm is decision tree based, it has natural advantages to address the multicollinearity and provide interpretable results. Furthermore, based on the experimental data from the Vcenter-55 type 3-axis vertical machining center, the proposed algorithm is compared with benchmark methods to demonstrate its superior performance on prediction accuracy with 7.05 $\mathsf{\mu }\mathrm{m}$ (over 14.5% improvement), robustness with 5.61 $\mathsf{\mu }\mathrm{m}$ (over 12.9% improvement), worst-case scenario predictions with 16.49 $\mathsf{\mu }\mathrm{m}$ (over 25.0% improvement), and percentage errors with 13.33% (over 10.7% improvement). Finally, the real-world applicability of the proposed model is verified through thermal error compensation experiments.

For multi-phase separation, developing high-performance cyclone separators is highly demanded. In this study, different divergent or convergent insertion pipes bFelow the vortex finder are employed to improve the separation performance. The impacts of insertion pipe geometrical configurations on the flow characteristics, pressure drop, and separation efficiency are systematically analyzed. The results reveal that the employed insertion pipe can significantly affect the static pressure distribution inside the cyclone. A longer insertion pipe length and larger tilt angle result in a wider central low-pressure area and smaller overall static pressure. The divergent insertion pipes increase pressure loss, while the convergent insertion pipes augment pressure loss. A larger insertion pipe length significantly impacts the axial velocity distribution. For divergent insertion pipes, the maximum central axial velocity increases with increasing tilt angles and then decreases. For small particles (d_p ≤ 5 μm), a convergent insertion pipe exhibits a better separation performance. When the particle diameter d_p ≥ 5 μm, divergent insertion pipes with larger angles and lengths can significantly decrease the pressure drop while guaranteeing satisfied collection efficiency. When L = 75 mm and θ = 60°, the pressure drop is reduced by 7.8%. This study may contribute to rationally designing high performance cyclone separators and also paves ways for improving the existing cyclone separators.

Purpose: – This paper aims to use the crankshaft-bearing system of a four-cylinder internal combustion engine as the studying object, and develop a multi-objective optimization design of the crankshaft-bearing. In the current optimization design of engine crankshaft-bearing, only the crankshaft-bearing was considered as the studying object. However, the corresponding relations of major structure dimensions exist between the crankshaft and the crankshaft-bearing in internal combustion engine, and there are the interaction effects between the crankshaft and the crankshaft-bearing during the operation of internal combustion engine. Design/methodology/approach: – The crankshaft mass and the total frictional power loss of crankshaft-bearing s are selected as the objective functions in the optimization design of crankshaft-bearing. The Particle Swarm Optimization algorithm based on the idea of decreasing strategy of inertia weight with the exponential type is used in the optimization calculation. Findings: – The total frictional power loss of crankshaft-bearing and the crankshaft mass are decreased, respectively, by 26.2 and 5.3 per cent by the multi-objective optimization design of crankshaft-bearing, which are more reasonable than the ones of single-objective optimization design in which only the crankshaft-bearing is considered as the studying object. Originality/value: – The crankshaft-bearing system of a four-cylinder internal combustion engine is taken as the studying object, and the multi-objective optimization design of crankshaft-bearing based on the crankshaft-bearing system is developed. The results of this paper are helpful to the design of the crankshaft-bearing for engine. There is universal significance to research the multi-objective optimization design of crankshaft-bearing based on the crankshaft-bearing system. The research method of the multi-objective optimization design of crankshaft-bearing based on the crankshaft-bearing system can be used to the optimization design of the bearing in the shaft-bearing system of ordinary machinery.