Minli Zheng

Milling chatter is a dynamic instability that deteriorates surface integrity, accelerates tool degradation, and limits productivity. For discrete-edge end milling, the intermittent edge geometry makes the measured vibration response strongly nonstationary, and chatter-related components are easily masked by spindle harmonics and broadband noise. This article develops an Improved Pelican Optimization Algorithm-Variational Mode Decomposition (IPOA-VMD) framework for chatter detection. IPOA searches for the VMD mode number and penalty factor directly from the measured signal, thereby avoiding empirical parameter assignment. After decomposition, the chatter-sensitive mode is selected according to frequency- and energy-based screening criteria, and its energy–entropy–center–frequency feature is then used to discriminate the machining state. Simulation and milling tests demonstrate that the proposed framework achieves accurate modal separation, enhances chatter-related feature representation, and provides reliable chatter identification under the investigated discrete-edge end-milling conditions.

The flute of a solid end mill directly determines parameters such as the rake angle, core diameter, and flute opening angle, thereby affecting the cutting performance of the tool. To address the complexity of spatial analytical models and the possibility that direct radial projection may neglect helical phase compensation, the grinding-wheel–blank process system was investigated. Grinding-wheel and blank models were established in a local normal plane, the normal-plane section was obtained according to the envelope principle, and the radial-section curve was then obtained through inverse coordinate transformation. A model for solving the flute parameters was subsequently established, and UG secondary-development technology was used to develop flute-grinding software for solid end mills. To verify the effectiveness of the proposed method, grinding experiments were conducted under identical operating conditions using a 1A1 grinding wheel and a GU25UF carbide blank. A radial-projection baseline (RP), in which only the helical phase compensation was removed, was compared with the proposed normal-plane envelope–inverse helical transformation method (NP) through a controlled geometric ablation comparison based on the same source geometry. The results showed that the core diameter, rake angle, and flute opening angle obtained by the NP method were 4.038 mm, −11.24°, and 89.68°, respectively, with errors of 2.70%, 5.55%, and 3.05% relative to the physical measurements. Compared with the RP method, the NP method reduced the rake-angle and flute-opening-angle errors by 74.90% and 81.18%, respectively, and the geometric offset at the flute-opening endpoint was 1.27 mm. The helix-angle sensitivity analysis further showed that the RP projection error increased markedly with increasing helix angle. The proposed method reduces the geometric error caused by flute-section projection; the maximum relative error among the three parameters was 5.55%, demonstrating good engineering applicability.

This paper addresses the finite-time sliding mode control problem for fractional Brownian motion (fBm)-driven Itô stochastic systems with actuator efficiency degradation, unknown matched nonlinearities, and partially unknown transition probabilities. A projection-based adaptive sliding mode observer is proposed to estimate unmeasurable states and unknown actuator fault factors. A non-switching sliding surface is constructed to avoid chattering caused by mode switching. Using a composite Lyapunov function and the fractional Itô formula, linear matrix inequality conditions are derived to ensure stochastic stability. A finite-time sliding mode controller is designed that drives the system onto the sliding surface within a prescribed time, with an explicit upper bound on the reaching time. Numerical simulations verify that the proposed method achieves finite-time convergence and yields bounded estimation errors, which demonstrates its effectiveness and robustness.

High-viscosity sodium alginate solutions (4.5% by mass, apparent viscosity 1 × 10⁴–2 × 10⁴ cP) are widely used in the preparation of hydrogels, wet spinning, and biomedical materials. Residual bubbles can cause internal voids in hydrogels, mechanical heterogeneity, fiber breakage during spinning, and reduced strength, and can severely affect the cell compatibility and clinical safety of biomaterials. Due to the difficulty of bubble migration, coalescence, and rupture in high-viscosity systems, traditional vacuum-standing degassing takes up to 24 h and is extremely inefficient, severely limiting the quality of subsequent processing. To address this issue, this study proposes a novel vacuum-assisted centrifugal recirculating degassing method for highly viscous sodium alginate solutions and aims to establish a kinetic framework for describing its overall degassing behavior. Using the number density of bubbles larger than 0.5 mm in diameter as an evaluation metric, we conducted vacuum-standing control experiments and univariate experiments with different screen mesh apertures (5, 1.5, 0.3, and 0.07 mm). We experimentally verified a continuous kinetic model of bubble number decay based on vacuum bubble expansion, centrifugally enhanced migration, and removal probability during the cycle. The results indicate that the bubble removal effect of 40 min of vacuum–centrifugal cyclic degassing is equivalent to that of 4 h of vacuum static settling, representing a 450% increase in degassing efficiency. There is an optimal range for a screen aperture, with the best degassing effect observed at 0.3 mm, achieving a bubble removal rate of 83.69%. The established kinetic model exhibits good fitting accuracy (RMSE = 0.17, MAPE = 5.9%) and can accurately predict degassing efficiency under different process conditions. This study provides a quantifiable, modelable, and optimizable process scheme for rapid degassing of high-viscosity sodium alginate solutions, and offers a theoretical reference for the development of degassing technologies for high-viscosity polysaccharide fluids.

With the rapid development of Industry 4.0 and the increasing digitalization of manufacturing processes, tool condition monitoring has become a critical technology for ensuring machining quality and efficiency. Compared with traditional Conventional end mills, discrete-edge end mills exhibit significant advantages in chip breaking, vibration reduction, and heat dissipation due to their unique intermittent cutting mechanism. However, this structural characteristic also causes the milling signals generated during the cutting process to exhibit stronger non-stationarity, nonlinearity, and complex transient impact characteristics. To address the above issues, this study proposes a novel complexity analysis method for vibration signals of discrete-edge end mills by integrating the proposed CRSN architecture with multiscale fuzzy entropy (MFE), aiming to achieve a comprehensive and accurate representation of the latent state information embedded in the signals. Furthermore, a dedicated signal processing approach, termed VI-CNN, is developed for discrete-edge end mills milling signals, providing a reliable basis for high-precision tool condition monitoring. Experimental results demonstrate that the proposed CRSN network can effectively extract representative features from discrete-edge end mills vibration signals, thereby validating the intrinsic complexity of the milling process. In addition, the proposed VI-CNN signal processing method is capable of reducing signal complexity while preserving key structural and physically meaningful features.

This study addresses the challenge of designing an adaptive sliding mode controller for a class of nonlinear Markov jump systems. These systems are characterized by unmeasurable states, partially unknown transition probabilities, and uncertainties arising from matched external disturbances and modeling inaccuracies. In control design and analysis, the nonlinear Markov system in which both the linear term and specific information about the upper bound in the external disturbance term are unknown. To enable descending equivalent sliding mode motion to regulate the dithering phenomenon in a controlled system, an integral sliding surface is established to achieve chattering suppression via descending equivalent sliding motion. A key theoretical contribution is the rigorous proof that the proposed control law ensures both finite-time reachability of the sliding surface and mean-square stability of the closed-loop trajectories. Comparative simulation results demonstrate that the proposed approach achieves a state estimation RMSE of 0.175, which is 48.0% lower than conventional sliding mode control (0.337) and 3.3% lower than observer-based sliding mode control without fault compensation (0.181). The controller reduces control chattering by 75.2% compared to conventional SMC (total variation from 64.4 to 16.0), achieves sliding surface reachability within 0.42s, and maintains effective fault estimation with an average RMSE of 0.138 for time-varying actuator efficiency factors. These quantitative improvements validate the effectiveness of the proposed fault-tolerant mechanism.

Discrete-edge end mills offer superior performance in machining difficult-to-cut materials by discretizing cutting loads via chip-splitting grooves. However, the nonlinear coupling between groove geometry and three-dimensional force dynamics remains poorly understood. This study establishes a physics-constrained parametric design framework for discrete-edge tools. First, a mechanistic cutting force model, incorporating a novel Boolean effective chip-slot function, is developed to rigorously capture the transient engagement caused by geometric discontinuities. The model is validated against milling experiments on AL7075, confirming its capability to predict the characteristic large-amplitude force fluctuations inherent to discrete cutting. Subsequently, a multiobjective optimization strategy is proposed, bounded by structural stiffness constraints derived from cantilever beam theory. Analysis reveals that the groove width ( λ ₂ ) significantly modulates the feed force ( F _x ) through a shearing-dominant mechanism, while the phase offset ( ps ) governs dynamic stability. Global optimization results demonstrate that the proposed geometric configuration yields a 26% improvement in the comprehensive performance index and a 28.6% reduction in average cutting forces, providing a robust theoretical basis for the precision design of high-performance cutting tools.

In machining systems, chatter is a harmful dynamic instability phenomenon that can cause high-frequency oscillations between the tool and the workpiece, exacerbate tool wear, and affect machining quality. To achieve efficient and accurate vibration monitoring, this study proposes an integrated optimized variational mode decomposition (VMD) and multi-scale permutation entropy (MPE) milling vibration signal analysis method—ITSLWOA-VMD-MPE. This method combines the Improved Whale Optimization Algorithm (ITSLWOA) to achieve adaptive signal decomposition and utilizes MPE for chatter state recognition. Firstly, Tent chaotic mapping, sine adaptive weighting, and Levy flight optimization WOA are introduced to enhance its search efficiency and global exploration capability. Secondly, ITSLWOA is used to adaptively optimize VMD parameters and accurately extract vibration signals during the milling process. Finally, combining MPE to quantify the evolution of chatter states can improve the accuracy of chatter identification. Simulation and experimental results show that this method can efficiently and accurately decompose milling signals, enhance the ability to extract chatter features, and effectively improve the robustness of chatter recognition.