Integrating dynamic land surface processes and machine learning into a hydrological modeling framework: application to the Yellow River Basin

Development of damage energy based process signature for machined surface quality in CFRP cutting

Abstract Gallium oxide (β-Ga2O3), as a representative fourth-generation semiconductor material, exhibits exceptional physical properties. However, it’s challenging to achieve high-quality wafer surfaces of β-Ga2O3 crystal due to its inherent cleavability. To provide theoretical guidance for the surface machining of such cleavable materials, this study systematically investigated the micro/nano-mechanical properties of β-Ga2O3 crystal by integrating first-principles calculations with nanoindentation/micro-scratch testing. First-principles calculations were employed to analyze the atomic structure and cleavage behavior of β-Ga2O3, revealing that the [GaO6] octahedron is more prone to disruption than the [GaO4] tetrahedron, which explains why cleavage delamination occurs on the (100)A and (100)B planes. Combining the elastic parameters obtained from first-principles calculations with nanoindentation experimental results, the following values were obtained: shear modulus (G) of GPa, bulk modulus (B) of GPa, elastic modulus (E) of GPa, hardness (H) of GPa, and a Poisson’s ratio (v) of . Based on nanoindentation energy analysis, linear elastic fracture mechanics (LEFM) theory, and three distinct equivalent strain energy density (ESEL) theories, the fracture toughness (Kc) of β-Ga2O3 crystal was calculated to be approximately 1.87 MPa·m1/2. The Kc calculation method derived from LEFM theory is particularly well-suited for exfoliation-prone brittle crystalline materials.

Anodic electrochemical dissolution behaviors of SUS304 stainless steel and its surface characteristics by scanning electrochemical machining

ABSTRACT Five-axis milling of free-form surfaces requires simultaneous optimization of toolpaths and continuously varying tool orientations. However, most commercial CAM workflows focus only on geometric simulation and neglect critical physical quantities such as cutting forces, leading to force spikes, tool deflection and surface inaccuracies. This study proposes an integrated optimization framework that combines automatic toolpath generation from STEP/B-Rep models with solid-model-based extraction of cutter–workpiece engagement (CWE). The CWE data are transformed into entry and exit immersion parameters and undeformed chip thickness to enable mechanistic cutting-force prediction. Tool orientations are then optimized using a curvature-aware parameterization method and a particle swarm optimization (PSO) algorithm. Numerical validation on representative free-form surfaces demonstrated that the proposed method reduced the maximum cutting force from 214 N to 170 N (a 20.6% reduction) compared with the original path. A secondary optimization stage incorporating polynomial-fitted force smoothing decreased force fluctuation amplitude by over 40%, resulting in both smoother tool-axis trajectories and improved machining stability. By integrating geometric modeling, physical simulation and metaheuristic optimization, the proposed PSO-based framework provides a quantitatively verified improvement in force-aware toolpath planning. The approach can be readily incorporated into existing CAD/CAM environments for efficient and reliable five-axis machining of complex free-form surfaces.

Surface finish is a critical quality characteristic in mechanical parts, influencing their functionality and durability. This study focuses on optimizing the cutting parameters to achieve the lowest surface roughness in the turning operation of A357 aluminum alloy. The effects of spindle speed, feed rate, and depth of cut on surface roughness were investigated experimentally using a tungsten carbide cutting tool. Multiple regression analysis (MRA) and analysis of variance (ANOVA) were employed to evaluate the significance of cutting parameters and to develop predictive models for surface roughness (Ra). The objective was to determine the optimal turning parameters and establish a correlation between surface roughness and the cutting variables. The results provide insights into the relationship between machining parameters and surface finish, offering a predictive framework for optimizing turning processes

Plastic deformation and pitting failures often occur in gas turbine bearings due to heavy loads, surface roughness and long service time at the interface between ball and raceway. The gas turbine bearing is a typical elliptical-contact friction pair, in this article, a numerical solution procedure is developed for the predictions of plastic deformation and relative fatigue life m based on the simulation of three-dimensional (3D) mixed plasto-elastohydrodynamic lubrication (PEHL) in elliptical contacts, whose results have been verified by comparing the predicted stress, film pressure and thickness with those from the semi-analytic method (SAM). The effects of machined surface roughness and the plastic deformation on the mixed PEHL lubrication characteristics, surface/subsurface stresses and relative fatigue life are studied. The results show that plastic deformation may significantly reduce the surface and subsurface stress concentrations caused by excessive elastic properties and micro asperity contact, and prolong the relative fatigue life for bearings.

Abstract This work examines the optimization of turning parameters across various lubrication environments, that is, mineral oil, biodiesel, and biodiesel augmented with multi‐walled carbon nanotubes (MWCNTs), utilizing Taguchi design and Response Surface Methodology (RSM). The influence of spindle speed, feed rate, and depth of cut on material removal rate (MRR) was methodically examined. Analysis of variance (ANOVA) revealed that spindle speed is the most significant metric under biodiesel lubrication, but depth of cut is predominant under biodiesel–MWCNT settings. Biodiesel and nano‐augmented biodiesel exhibited greater machining performance relative to mineral oil, due to enhanced viscosity, improved lubricity, and the creation of a tribo‐film at the tool–workpiece interface. These methods markedly diminished cutting forces, tool wear, and surface roughness. The optimization results indicated that the minimal material removal rate (MRR) occurred at a feed rate of 0.2 mm/rev, a spindle speed of 1036 rpm, and a depth of cut of 2.47 mm, with experimental validation closely aligning with anticipated values, thereby affirming the reliability of the generated model. Scanning electron microscopy validated these findings, revealing improved surface integrity, with biodiesel–MWCNT lubrication yielding the smoothest machined surfaces. The findings underscore the wider ramifications of utilizing nano‐augmented biodiesel lubricants, such as less reliance on petroleum‐derived cutting fluids, enhanced process sustainability, and conformity with eco‐friendly manufacturing standards. The study confirms biodiesel‐based nanolubricants as a realistic, high‐performance alternative for sustainable turning operations, optimizing productivity, surface quality, and environmental responsibility.

The mechanical joint surface is commonly simplified as smooth or Gaussian-distributed, while its actual non-Gaussian characteristics significantly affect fretting wear behavior. A non-Gaussian fretting wear model, characterized by the three-dimensional morphology of actual machined surfaces, is established to accurately reveal the fretting wear mechanism of joint interfaces. First, the fast Fourier transform is adopted to construct non-Gaussian surface models consistent with actual surface characteristics. Second, the non-Gaussian surface fretting wear model is developed through ABAQUS and the UMESHMOTION wear subroutine. Finally, the effects of kurtosis, skewness, standard deviation, material properties, and friction coefficient are explored. The conclusions illustrate that non-Gaussian surfaces demonstrating positive skewness and high kurtosis exhibit the most pronounced local stress concentration and maximum wear depth compared to smooth and Gaussian surfaces. Non-Gaussian surfaces exhibiting negative skewness and low kurtosis demonstrate more uniform stress distribution and better wear resistance under identical conditions. The wear depth in the non-Gaussian surface increases significantly with rising the standard deviation, elastic modulus of materials, and friction coefficients.

The micro-geometry of the cutting edge plays a crucial role in material flow ahead of the cutting edge and chip formation, primarily influencing chip formation mechanisms and the minimum cutting thickness. In the context of turning 304 stainless steel, however, existing research still lacks a unified quantitative framework linking "cutting edge micro-geometry-material separation behavior (separation point/minimum uncut chip thickness)-microstructural evolution of the machined surface." This gap hampers mechanistic optimization design aimed at enhancing machining quality. This study examines the turning of 304 stainless steel by integrating analytical modeling, finite element simulation, and experimental validation to develop a predictive model for minimum cutting thickness. It analyzes the effects of tool nose radius and asymmetric edge morphology, and a microstructure evolution prediction subroutine is developed based on dislocation density theory. The results indicate that the minimum cutting thickness exhibits a positive correlation with the tool nose radius, and their ratio remains stable within the range of 0.25 to 0.30. Under asymmetric edge conditions, the minimum cutting thickness initially increases and then decreases as the K-factor varies. The developed subroutine, based on the dislocation density model, enables accurate prediction of dislocation density, grain size, and microhardness in the machined surface layer. Among the factors considered, the tool nose radius demonstrates the most pronounced influence on microstructure evolution. This research provides theoretical support and a technical reference for optimizing cutting-edge design and enhancing the machining quality of 304 stainless steel.

Abstract For the robotic milling of large components with poor rigidity, it is easy to cause machining chatter. Severe chatter reduces the machining accuracy and surface quality of the part and affects the service life of the tool. Currently, the chatter suppression of robotic machining based on ultrasonic machining technology has become a hot topic. In this paper, a chatter active suppression method is proposed by adjusting the input of ultrasonic vibration energy to improve the robotic milling stability. Firstly, according to the optimal ratio of torsional vibration energy to longitudinal vibration energy, the ultrasonic knife handle is designed to improve robotic milling stability. Then, the chatter recognition method is applied to determine whether the robot exhibits chatter. After that, a model for the relationship between ultrasonic vibration energy and chatter energy is constructed for chatter active suppression. Finally, the experiment of robotic rotary ultrasonic milling brackets is carried out. The experimental results indicate that the machining quality meets the requirements of roughness and flatness. Compared with robotic common milling, the machining efficiency could be increased by five times.

Influence of cryogenic temperature on machining mechanisms and surface integrity of CF/PEEK composites based thermo-mechanical coupling analysis

Improving curved surface machining quality of blasting erosion arc milling by applying working fluid blockers

Abstract Surface form error (SFE) prediction in high-speed micromilling of thin-walled structures is challenging due to dynamic factors such as tool deflection, workpiece flexibility, and machining vibrations. The existing mechanistic model lacks the adaptability needed for diverse machining environments, as they are material and cutting conditions dependent, which limits its application. This study presents a novel validated framework that combines adaptive signal processing with stacking-type ensemble machine learning models to address these challenges. Premachining and postmachining laser scans capture surface deformation, and any temporal discrepancies between them are resolved using the dynamic time warping (DTW) technique to generate the machined surface envelope. This machined surface envelope has been used to obtain the SFE. Furthermore, machined surface analysis has been performed to compare the generated SFE envelop with experimentally obtained machined surface SFE, revealing a maximum prediction difference of less than 12%. A stacking-based ensemble approach has been proposed and validated to predict SFE by training the proposed models on the estimated SFE from different machining conditions. Experimental validation has also been carried out by performing low-immersion radial micromilling on thin-walled TC4 specimens, and the proposed framework achieved an R2 of 0.973 with less than 10% prediction error. The ensemble model with the highest R2 value has further been applied to develop a graphical user interface (GUI) that enables real-time SFE prediction for machining optimization.

With the rapid development of Head-Up Display (HUD) technology for vehicles, optical freeform mirrors, as its core optical components, are crucial for achieving system compactness and high imaging quality. However, their complex surface shapes and large-aperture characteristics pose significant challenges to ultra-precision manufacturing. This study presents a systematic optimization framework for the ultra-precision machining of HUD optical freeform mold cores, integrating surface design, tool path planning, vibration analysis, and process parameter optimization. Firstly, based on the XY polynomial freeform surface model, an off-axis three-mirror HUD system was designed, and the surface parameters and machining dimensions of the mold core were determined. For the Single-Point Diamond Turning (SPDT) Slow Tool Servo (STS) process, a hybrid trajectory planning method combining equidistant projection and cubic spline interpolation was proposed to ensure the smoothness and accuracy of the tool path. Through theoretical analysis and experimental verification, the selection criteria for tool parameters such as tool nose radius and effective cutting angle were clarified, and the mechanistic impact of Z-axis vibration on surface roughness and waviness was quantitatively revealed. Finally, through ultra-precision turning experiments and on-machine measurement, a high-precision freeform surface mold core was successfully fabricated. This validates the effectiveness and feasibility of the proposed process solution and provides technical support for the high-quality manufacturing of HUD optical elements.

The evaluation of machine tool characteristics and their impact on surface quality is challenging, often requiring disruptive traditional methods. This study introduces a novel, non-invasive approach using optical camera images for rapid and accurate assessment. Data robustness was ensured by acquiring initial images outside the machining chamber with consistent external illumination, focusing on detailed intensity profile analysis. Machined surfaces were processed using intensity profile extraction and Fast Fourier Transform (FFT). The dominant spatial wavelength (0.1833 mm) consistently showed excellent agreement (within 1.85%) with the theoretical feed per revolution (0.1800 mm). This robustly validates the method's ability to precisely capture primary kinematic tool marks. Temporal information, inferred from spatial frequencies, underwent subsequent FFT to identify periodic phenomena and harmonics. The comprehensive spatial and temporal FFT analyses offer detailed, quantitative surface characterizations. The clear distinctions in temporal harmonic patterns provide robust, frequency-domain signatures informing machining system performance and process integrity.

An integrated approach combining Response Surface Methodology (RSM), Machine Learning (ML-SVM) and TOPSIS optimization method is applied in this study to analyse the tribological behaviour of 3D printed patterns of almond shell-PMMA (polymethyl methacrylate) origami inspired composites and machinability. The process of taking advantage of fold-based geometrical patterns (such as Miura-ori or triangular tessellation) to enhance load distribution and energy absorption in 3D printed specimen is called origami-inspired. These patterns promote a certain degree of structural rigidity and cause weakened materials to deform under applied stress in a controlled manner in general to improve the mechanical strength and wear-resistance. Besides tribological performance factors such as wear rate and friction coefficient, the factors to be evaluated on the machinability properties of cutting force, surface roughness and material removal rate include spindle speed (3000−9000 rpm), feed rate (0.05–0.15 mm/rev), and depth of cut (0.2–0.6 mm). Although the machine learning algorithms were able to make predictive models concerning wear performance and machinability, RSM was addressed to plan the experiments and conclusion of the parameters interaction. TOPSIS method identified the parameters combination that will serve the best by balancing between tribological efficiency and machinability. The novelty aspect of the current work is the inclusion of agricultural waste (10 percent almond shells) to the polymer matrices and and the use of a hybrid optimization strategy on the ways to optimize its functional properties with respect to being used in wear and machining applications. Notable findings indicate that the most influential variable affecting machinability and tribological results is the feed rate; in the best case scenario there is achievement of surface roughness of 1.2 10−15 m and wear rate aside at 1.5 in 10−4 mm 3/Nm. The proposed model was able to give a workable and industrially friendly composite method with great efficiency and sustainability in terms of optimization performance and predictability (R2 > 0.95).

The stagnation point and dead metal zone in the cutting process directly or indirectly affect the chip formation and stress distribution, while the stress distribution in the machining process determines the plastic slip direction of the material. Aiming at the current insufficient research on the dead metal zone and stagnation point theory, this paper divides the cutting process into rounded edge contact stage and rounded edge-rake face contact, constructs a slip line field model with dead metal zone based on the stress distribution and pressure distribution of the two stages, calculates the slip line field through the Cauchy problem, and plots the slip line field through the secondary development port in SOLIDWORKS. The dead metal zone model is based on the stress distribution of the obtuse circular contact, and the stagnation point occurs at the critical condition of the elastic-plastic transition of the material, i.e., at the maximum shear stress of the process. The dead metal zone and stagnation point are examined based on simulation, and the slip line field model is verified experimentally. The results show that the dead metal zone model can be predicted more accurately when the tool rake angle is 15° or less, and the greatest influence on the stagnation point is the tool rake angle and the radius of the rounded edge of the tooltip, and the slip line field model containing the dead metal zone can more accurately reflect the plastic slip of the real cutting process. It can be seen that the dead metal zone model, stagnation point model, and slip line field model illustrate the cutting mechanism of the elastic and plastic phases of the cutting process, which lays a research foundation for the subsequent study of tool wear, chip formation, and machining surface quality.

The stagnation point and dead metal zone in the cutting process directly or indirectly affect the chip formation and stress distribution, while the stress distribution in the machining process determines the plastic slip direction of the material. Aiming at the current insufficient research on the dead metal zone and stagnation point theory, this paper divides the cutting process into rounded edge contact stage and rounded edge-rake face contact, constructs a slip line field model with dead metal zone based on the stress distribution and pressure distribution of the two stages, calculates the slip line field through the Cauchy problem, and plots the slip line field through the secondary development port in SOLIDWORKS. The dead metal zone model is based on the stress distribution of the obtuse circular contact, and the stagnation point occurs at the critical condition of the elastic-plastic transition of the material, i.e., at the maximum shear stress of the process. The dead metal zone and stagnation point are examined based on simulation, and the slip line field model is verified experimentally. The results show that the dead metal zone model can be predicted more accurately when the tool rake angle is 15° or less, and the greatest influence on the stagnation point is the tool rake angle and the radius of the rounded edge of the tooltip, and the slip line field model containing the dead metal zone can more accurately reflect the plastic slip of the real cutting process. It can be seen that the dead metal zone model, stagnation point model, and slip line field model illustrate the cutting mechanism of the elastic and plastic phases of the cutting process, which lays a research foundation for the subsequent study of tool wear, chip formation, and machining surface quality.

This article summarizes the progress made in vibration-damping boring bar technology over the past fifteen years, gaining insights from both patents and scholarly articles. Its main emphasis is on two types of equipment: active vibration-damping boring bars and passive vibration-- damping boring bars. During deep hole machining, the boring bar's high aspect ratio and inadequate rigidity make it prone to causing cutting vibrations. This leads to vibration marks on the machined surface, subpar roughness, tool failure, and poses a risk to personal safety. Either active or passive vibration reduction techniques are required to intervene and quell the vibrations produced by the boring bar during the cutting process, or to passively absorb or dissipate the vibration energy, thereby effectively suppressing boring bar vibration. The vibration-damping boring bar broadens the cutting stability frequency range by incorporating additional vibration absorbers into its design. Enhancing the stiffness of the absorber material improves its capacity to absorb and mitigate vibrations generated during machining. Alternatively, vibration reduction can be achieved by actively sensing the structural response and dynamically adjusting the damping coefficient in real time to suppress unwanted oscillations. Vibration reduction boring bars can employ either active or passive vibration reduction technology for various types of deep hole machining, effectively addressing product quality and safety concerns stemming from cutting vibrations during the process, and enhancing the cutting stability of the boring bars. These technologies not only extend the service life and enhance the cutting stability of the cutting tools during deep-hole boring operations but also effectively mitigate vibrations and other challenges encountered during machining. This ensures consistent stability and reliability of the boring bar, maintains the quality standards of the machined components, and enables a more precise, controllable cutting process, ultimately delivering a significant improvement in overall machining quality.