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.

Minimum quantity lubrication (MQL) machining has gained widespread attention in both academic and industrial research fields as a beneficial technology for improving machining performance and sustainability, due to its low cost and environmental protection. Nevertheless, there is still room for improvement in MQL, such as the lack of research and analysis on the development history of MQL, key authors, and research hotspots. This may be one of the reasons limiting the development of MQL. Based on this, this paper proposes a new bibliometric analysis of MQL research, with the aim of describing current research trends and visualizing the development history and emerging trends of MQL to support researchers in conducting in-depth studies. First, a bibliometric analysis was conducted on 1842 publications related to MQL in the Web of Science (WoS) Core Collection database from 2008 to 2023. Secondly, bibliometric analysis software such as VOSviewer and bibliometrix were used to visualize the annual growth of publications, distribution of research fields, regional distribution, distribution of research institutions, author distribution, highly cited articles, and keywords. The results show that from 19 publications annually in 2008 to 292 publications annually in 2023, there has been a 15-fold increase, with India (550 publications) being the country with the most publications and China (19420 citations) having the highest number of citations. Furthermore, an analysis was conducted on the research hotspot directions represented by keyword classification, summarizing the current research achievements. This paper reveals the development trend, global cooperation pattern, basic knowledge, research hotspots, and emerging frontiers of MQL.

Bone tissue is more accurately removed by micro-grinding, which is more conducive to postoperative recovery and healing. However, the anisotropy of the bone material is not taken into account in current research on bone tissue micro-grinding, and experiments to optimize the parameters of the bone grinding process are lacking, leaving a gap in reference for the selection of parameters in clinical surgery. Based on this, the bone micro-grinding force is first analyzed by considering the anisotropy of bone tissue, and a single-factor experiment investigates the effects of machining parameters on the micro-grinding process under the three directions of vertical, cross, and parallel to the feed direction of the grinding head and the bone unit. Orthogonal experiments are then used for optimization to evaluate the effect of each machining parameter on the machining quality based on the magnitude of micro-grinding force in the x- and y-directions and the surface morphology of the machined bone tissue. The results show that the minimum F_x and F_y and the best surface quality are obtained at a feed rate of 100 mm/min, a grinding head rotational speed of 16000 r/min, and a grinding depth of 15 μm. Finally, the results of the orthogonal experiments are analyzed by signal-to-noise ratio and analysis of variance, and the results show that the grinding depth factor has the greatest effect on F_x and F_y in the vertical direction, the grinding head rotational speed factor has the greatest effect on F_x and F_y in the cross direction, the grinding depth in the parallel direction has the greatest effect on F_x, and the grinding head rotational speed has the greatest effect on F_y. The aim of this study is to provide theoretical guidance and technical support to improve the processing quality of biological bone micro-grinding.

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.