20CrMnTi is applied in the manufacturing industry for hardenability and ductility. However, surface strengthening and precision machining are separated, which affects manufacturing efficiency, extra pollution, and energy consumption. An integrated grinding carburization is proposed, and a material fluent model with experiment is established, considering the mechanical–thermal and carbon diffusion hardening. The surface material removal mechanism varies from ductility to brittleness due to stronger mechanical–thermal coupling. Results denote that the hardness is enhanced by 3.5 times than that of the matrix. The surface material with a thinner and finer structure is beneficial for roughness and residual stress. This study presents not only a surface-integrated modeling method but also an efficient strategy for extreme manufacturing.

Ultra-high-strength steels have been widely utilized in the aviation industry due to their superior mechanical and physical properties. However, the intense friction occurring at the tool–workpiece interface can result in significant tool wear, impacting both the machining efficiency and surface quality. Therefore, a deep understanding of the tribological behavior at the tool–workpiece interface is crucial for extending tool life. The current study proposed a novel open tribo-system to simulate the intermittent contact conditions between the tool and the workpiece during the milling process. An improved friction model was established to calculate the real friction coefficient under intermittent contact conditions. Tribological comparative experiments on ultra-high-strength steel and cemented carbide were conducted under various cooling conditions: dry, high-pressure air cooling (HPAC), air atomization of cutting fluid (AACF), and ultrasonic atomization of cutting fluid (UACF). The influences of various cooling conditions on the tribological characteristics were investigated, with a focus on the depth of the wear mark, height of the pile-up, measured force, adhesion friction coefficient, and wear morphology. The results reveal that the depth of the wear mark, height of the pile-up, and measured forces play pivotal roles in determining the adhesion friction coefficient. The synergistic action of airflow and droplets results in the formation of a liquid film, improving the friction at the interface between the ball and the workpiece. Compared with the AACF condition, the UACF condition results in a 7.7% reduction in the adhesion friction coefficient due to its excellent film-forming ability stemming from its small droplet size and uniform droplet size distribution. Abrasive wear, adhesive wear, and oxidative wear are the primary wear types for cemented carbide, regardless of the cooling conditions. The effective cooling and lubrication capability provided by the uniform liquid film in UACF contributes to improving the wear resistance of cemented carbide, offering valuable insights for mitigating tool wear.

This study is concerned with the instability analysis and improvement of a high-pressure and high-flow air pressure-reducing regulator (HPHFPRR). The outlet pressure of HPHFPRR abruptly exceeds the initial set pressure value under high-pressure and high-flow conditions. To address this instability issue of HPHFPRR during practical usage, this study conducted simulations to analyze the transient flow field characteristics under varying valve opening degrees, unlike most studies that focused on unsteady turbulent behaviors at fixed valve openings. The investigation examined variations in the internal flow field and air force acting on the valve spool at different degrees of valve opening for HPHFPRR. Moreover, a dynamic model was established to deeply analyze the motion process and forces experienced by the valve spool and piston in HPHFPRR, and an optimized spool structure was experimentally verified. Results demonstrated that the gas flow force acting on the valve spool was responsible for sudden outlet pressure rise in HPHFPRR. After design optimizations, the maximum gas flow force on the newly designed valve spool decreased by 32.7% during HPHFPRR opening, effectively resolving abnormal instability issues under high-pressure conditions encountered in projects.

Layer jamming structures (LJSs) are variable-stiffness elements used in soft robotics to enhance load-bearing capacities. The complexity of interactions within these structures has impeded the development of a comprehensive model, often making LJS design reliant on empirical experience. Existing models primarily focus on straight beams under specific conditions, thus exhibiting limited applicability. To address these challenges, we developed a novel model that specifically investigates the bending deformation characteristics of LJS beams. Through finite element analysis, we analyzed the stress distribution and stress increments during bending and established a relationship between stress and external loads. From this foundation, we derived a governing deformation equation that is applicable to all LJS beams and can address the issue of large deformation under complex loading conditions through an iterative algorithm. Our model was validated experimentally and proven to be highly accurate in predicting the effects of layer thickness, hydrostatic pressure, and cyclic loading. This research substantially advances the understanding of LJS mechanical behavior, laying the groundwork for the development of sophisticated applications and structural designs in this rapidly evolving field.

Sustainable production depends on the optimization of manufacturing processes. The assessment of carbon emissions in manufacturing is crucial for achieving sustainability. However, a comprehensive systematic framework to reflect the carbon emission regularity of manufacturing processes is currently lacking. This study focuses on the modeling and evaluation of carbon emissions by considering machining processes and multiple factors. First, carbon emission models for machining processes, such as turning, milling, and drilling, are systematically summarized by considering power consumption. Second, the influence of system parameters on carbon emissions is analyzed. Results show that cutting depth exerts a substantial effect on carbon emissions, and material removal rate has minimal influence. Last, the emission reduction mechanism and performance of novel sustainable machining processes are examined to contribute to carbon emission reduction. This study helps in systematically understanding carbon emissions in manufacturing processes, providing support for the further development of sustainable manufacturing.

Plunge milling, which is recognized for its efficiency, has gradually been adopted for the roughing of integral impellers in recent years. However, several challenges persist, such as redundant tool paths, difficulties in managing the Sudden Increase of Radial Depth (SIRD), and excessive residual material in the adjusted tool path by plunge milling, which collectively constrain the efficiency of the plunge milling process. To address these challenges, this study proposes a five-axis plunge milling method with double-row slotting tailored for integral impellers. This method segments the machining area based on the width of the impeller runner and utilizes various tools with different diameters to enhance machining efficiency. The plunge milling path is optimized to facilitate efficient material removal while maintaining cutting stability, which addresses issues related to cutting overload and chip accumulation. In addition, an efficient method for SIRD identification and exclusion is proposed to minimize residual material. Simulation and practical machining results confirm that the proposed plunge milling method with double-row slotting significantly improves machining efficiency, which achieves enhancements of over 1.5 times compared with traditional methods.

The harsh working environment of unmanned mining electric shovels (UMESs) and the considerable inertia changes during the excavation process in the front-end mechanism pose major challenges to excavation trajectory tracking. In this study, an adaptive Hamilton–Jacobi inequality (HJI)-based robust control method for UMES excavation systems with uncertainty was proposed for trajectory tracking control in intelligent mining. First, the excavation system dynamic model was analyzed using the Lagrangian method, and an excavation resistance prediction model and a material quality prediction model were constructed. The optimal excavation trajectory was described. Then, the HJI theorem was used to design an adaptive controller based on the dynamic model of the UMES, and a generalized regression neural network was introduced to fit the interference term in the control object to ensure the convergence of the control system. Subsequently, a Lyapunov function was constructed to demonstrate the stability of the control system to ensure the reliability of the excavation system. Finally, the method proposed in this study was verified under two different working conditions involving a typical material surface and a real material surface. The numerical simulation results demonstrated that the planned position and velocity were effectively tracked in both working conditions. Furthermore, it maintains an improved tracking effect under different uncertain disturbances, thus verifying the feasibility and robustness of the control system designed in this study.

To automate heavy-duty hydraulic manipulators in construction applications, trajectory learning from demonstration is increasingly in demand. However, it faces difficulties in motion noise owing to factors such as size scaling and oscillation tendency. A smooth trajectory learning method is established to overcome this problem by segmenting the demonstration and extracting the subgoals for motion noise cancellation. The imperfect demonstration trajectory is segmented by clustering the end-effector’s velocity in the task space with locally weighted noise cancellation to reduce the impact of velocity fluctuations. A sequentially hierarchical Dirichlet process algorithm with temporal encoding is designed to extract the intended subgoals and filter inefficient operations. Then, the learned trajectory is reconstructed, combined with dynamic motion primitives (DMP). The comparison test results indicate that the proposed method can learn a relevant trajectory that reflects the real intention of the user from an imperfect demonstration. Taking DMP and Sparse Sampling as comparisons, two cases of automatic trajectory tracking tasks are performed, which shows that the average position error with respect to the reference can be reduced because inefficient operations or movements are effectively filtered.