Walking excavators are all-terrain multifunctional excavators that are often utilized in operations on complex unstructured terrain. Due to structural complexity and the diversity of the terrain on which these excavators operate, ride comfort is a crucial and challenging factor. To address these issues, this paper proposes a nested posture planning strategy for unmanned walking excavators (UWEs) based on multi-objective optimization. First, a 7-degree-of-freedom kinematic model of the chassis is established based on closed-loop vector equations, and a machinery–terrain coupling dynamical model is established based on the Lagrange method. Subsequently, the radial basis function (RBF) is employed to characterize the motion trajectories of the supporting hydraulic cylinders. Furthermore, a nonlinear trajectory planning model that integrates multiple objectives, multiple constraints, and terrain information is constructed to minimize attitude deviations and energy consumption associated with active adjustments during operation. To accelerate model solving, a two-stage nested optimization strategy is proposed. Finally, a high-fidelity mathematical-physics environment and real-world experiments are constructed to investigate the performance of the proposed method. The results demonstrate that the UWE can successfully traverse challenging terrain with excellent chassis posture in several scenarios.

Reliability testing is essential for detecting early failures in computer numerical control (CNC) machine tools and enhancing their operational reliability. However, traditional ex-factory run-in tests require prolonged cutting of raw materials to simulate real-world conditions, leading to high costs, time consumption, and environmental impact. To address these challenges, this paper proposes a novel simulated cutting force loading device based on a parallel mechanism. The kinematics and dynamics of the device are thoroughly analyzed, and a unique force allocation method for redundant controlled variables is developed to improve the smoothness of pneumatic servo output by exploiting the characteristics of pneumatic actuation. Based on real-time kinematic and dynamic calculations, a force feedforward proportional integral derivative controller is designed. Loading experiments on a simulated spindle demonstrate the device’s ability to accurately apply static and low-to-medium-frequency dynamic loads to non-rotating spindles. Furthermore, experiments conducted on the rotating spindle of a CNC machine tool show that the proposed device can effectively simulate cutting forces, offering a cost-effective and environmentally friendly alternative to conventional cutting-based reliability tests.

In general, the tool center point (TCP) accuracy of machine tools can be enhanced by minimizing geometric error (GE) and tracking error (TE). However, five-axis machining for sculptured surfaces has led to increased dynamic error (DE), driven by vibrations and deformations under the real-time influence of machine dynamics and motion parameters. These characteristics in DE align closely with the core concept of digital twins, which involve real-time interactions between physical objects and their virtual models to map system state changes. Hence, a TCP trajectory prediction model (TTPM) of five-axis machine tools (FAMTs) is proposed to achieve precise trajectory prediction based on a digital twin, integrating DE with GE and TE. Firstly, a TCP dynamic error model (TDEM) is established to estimate DE considering multi-axis coupling and varying structural dynamics in FAMTs. Simultaneously, a forward kinematic model (FKM) is constructed using screw theory to account for GE and TE. Then, by integrating the TDEM and FKM, the proposed TTPM predicts TCP trajectories considering DE, GE, and TE. Finally, the TTPM is verified through the R-test. The results reveal that the proposed model exhibits an average deviation of 3.80 μm and a maximum deviation of 6.53 μm in high-speed and high-acceleration trajectories, resulting in a 14.81% improvement in root mean square error and a 22.13% enhancement in trajectory error prediction accuracy on average. The proposed model achieves high prediction accuracy with low computational cost and can be integrated into digital twin systems.