This study investigates chip–texture interaction during low-speed dry orthogonal cutting of AISI 1015 steel using high-speed in-situ imaging and particle image velocimetry (PIV). Textured carbide tools with fixed parallel rake-face grooves are investigated by systematically varying cutting speed, uncut chip thickness, rake angle, and texture position relative to the cutting edge. For the fixed groove geometry investigated, the dimensionless ratio a/h, where a is the first-groove position and h is the uncut chip thickness, provides a geometric descriptor for organising the observed chip–texture interaction states. Larger a/h values are associated with preferential cutting, characterised by reduced tool–chip contact, increased shear angle, suppressed crack-driven segmentation, and stable chip flow. As groove engagement increases, an intermediate transitional interaction is observed before sustained chip intrusion establishes derivative cutting, characterised by persistent interface-driven deformation beneath the flowing chip. Progressive groove filling, groove-wall fracture, and transient built-up edge formation are observed as consequences of sustained interaction. Cutting speed and rake angle influence the severity of chip–texture engagement by modifying contact duration and chip confinement. Similar chip morphologies are found to arise from fundamentally different deformation mechanisms, demonstrating that post-machining chip observations alone are insufficient to identify the underlying interaction state. The study establishes an observation-based framework for interpreting chip–texture interaction based on the observed mechanics of chip engagement and interface-driven deformation under the investigated cutting conditions.
Uniform material removal on blisk blade surfaces is critical for surface integrity, but existing abrasive belt flap wheel (ABFW) grinding paths often lack sufficient directional variation, compromising surface quality. This paper proposes a toolpath planning method that co‑optimizes tool posture and adaptive trochoidal parameters based on conformal parameterization. The 3D surface is first conformally mapped onto a 2D domain, and a variable‑radius trochoidal path, whose radii are modeled by cubic B‑spline curves to ensure smoothness, is constructed on a zigzag guiding curve. The path is then optimized by a particle swarm optimization (PSO) algorithm, which iteratively co‑optimizes tool posture and the B‑spline control parameters under an objective that incorporates material removal uniformity, collision penalty, tool‑axis smoothness, and path coverage. A hierarchical collision detection strategy with broad‑phase filtering and narrow‑phase exact testing accelerates interference checking. Finally, comparative grinding experiments with four toolpaths were conducted on TC4 blades extracted from a blisk. The proposed method achieved the smallest material removal standard deviation (SD of 3.567 µm) and surface roughness (Ra 0.076 and Ra 0.105 in the Z and X directions, respectively), indicating that the co‑optimized zigzag‑based trochoidal path significantly improves surface quality.
The amplitude output capability of high-power ultrasonic metal welding transducers (HPUMW-UTs) is a key performance indicator. This study systematically investigates the effects of material matching on the impedance and vibration characteristics of HPUMW-UTs. Nine representative material-matching schemes are constructed from three commonly used materials: 304 stainless steel (304 SS), TC4, and 7075 Al. The results show that progressively replacing 304 SS with TC4 and then with 7075 Al in the front or back plates increases the electrical impedance, reduces the effective electromechanical coupling, narrows the bandwidth, and increases the load sensitivity of the impedance response. In contrast, the output amplitude generally increases as 304 SS is replaced by TC4 and then by 7075 Al. However, configurations with higher no-load amplitudes exhibit greater amplitude attenuation under external loading. The front plate material has a greater influence on impedance behavior and vibration amplitude than the back plate material. Experiments show that, under the same input power and the investigated 20 kHz HPUMW conditions, the TC4–TC4 configuration exhibits the most favorable amplitude output, reaching a maximum peak-to-peak amplitude of 158 μm. It also maintains superior amplitude stability as the external pressure increases from 0 to 500 N. These findings provide theoretical, Finite Element Analysis (FEA), and experimental guidance for material matching design in power UTs.
Ultrasonic-assisted hole expansion (UHE) enhances the fatigue performance of aerospace holed components by introducing beneficial compressive residual stress (RS). However, UHE-induced RS is still mainly evaluated through experiments or finite element simulations, which are costly and time-consuming, while mechanics-based analytical prediction methods remain limited, especially for high-strength titanium alloys. To address this gap, this study proposes an analytical model for predicting the RS distribution induced by UHE in TC21 titanium alloy. The model establishes a loading-unloading coupled elastoplastic framework that explicitly considers the reverse-yielding zone, plastic zone, and elastic zone around the hole. Based on elastoplastic theory and Lame’s equations, the Voce hardening model is used to describe strain hardening during loading, while a kinematic hardening model is introduced to capture reverse yielding during unloading and springback. This stage-specific constitutive treatment improves the analytical representation of path-dependent deformation behavior. An energy-based formulation is further employed to determine the plastic-zone radius and stress transition conditions, enabling analytical prediction of both radial and circumferential RS distributions. The proposed model is validated using a three-dimensional finite element model (FEM) and UHE experiments with a self-built setup. Under the baseline processing condition δ = 0.54, the predicted RS agrees well with the FEM results and experimental measurements, with deviations within 18% at different depths. Parametric analysis under different interference amounts further confirms the robustness and applicability of the model.
Natural frequencies of large-scale machine tools exhibit strong position dependence across the workspace. Accurate real-time prediction of position-dependent natural frequencies is essential for avoiding resonance and optimizing cutting parameters. However, existing methods face high testing costs, overlooked measurement uncertainties, and difficulty in achieving both accuracy and real-time performance under simulation uncertainty. This study proposes a probabilistic Key Conditional Quotient (KCQ) method that integrates a small amount of noisy vibration data with finite element simulations under parameter uncertainty to enable accurate real-time prediction. Quotient-form expressions for the conditional probability density function (PDF), conditional mean, and conditional variance are derived, transforming the prediction task into the computation of conditional statistical quantities. To solve this efficiently, a non-equal-weight generalized quasi-Monte Carlo (GQMC) method and Gaussian smoothing of the Dirac delta function are employed. An offline–online coupled KCQ framework is developed to support millisecond-level real-time performance. Validation on the JR-2203Q gantry machine tool, as a digital-twin-oriented application scenario, demonstrates prediction errors below 3% and computation times under 5 ms, confirming the method’s effectiveness in both predictive accuracy and computational speed.
The generation of helical groove CAD models is a key step in tool grinding simulation, and its efficiency and accuracy are often limited by the complex geometry of grinding wheels and the limitations of existing boundary extraction methods. To address this challenge, this paper proposes an efficient helical groove modeling framework based on a constraint-enhanced radial boundary extraction method. This framework enables rapid construction of helical groove models when the grinding wheel geometry and grinding trajectory are known in advance. First, based on the geometric characteristics of the helical groove cross-section, the constraint-enhanced radial boundary extraction method (CERBE) is proposed to extract structured ring-wise boundary primitives from unordered projected point clouds. Second, a curvature-weighted arc-length sampling strategy is adopted to discretize and optimize the grinding-wheel generatrix, thereby improving the overall structural quality of the grinding point cloud. Finally, the extracted boundary primitives are ordered and converted into CAD sections, and solid modeling is performed using the OpenCascade geometry kernel to generate kernel-valid helical groove CAD models. Additional validation, including parameter sensitivity, sparse and degraded point-cloud tests, and comparisons with convex-hull-based extraction, an annulus-based baseline, and Alpha Shapes, evaluates the stability limits of the proposed method under the tested conditions. The reported 37% runtime reduction refers to the CERBE primitive-extraction core for a 150,000-point dataset, while the complete CAD-oriented pipeline additionally includes preprocessing, contour ordering, topology repair, and solid construction. VERICUT-based validation shows that the generated models are CAD/CAM-executable, with small absolute deviations in key sectional parameters and limited multi-section contour deviations under the tested simulated grinding conditions.
In freeform surface CNC machining, existing locally-smoothed toolpath (LST) and globally-smoothed toolpath (GST) methods mainly operate at the line level and therefore have limited capability in addressing inconsistencies between neighboring paths. This issue often leads to non-uniform parallel tool marks and degraded surface finish, particularly on complex surfaces. To address this problem, this paper proposes a surface-smoothed toolpath (SST) based on cutter location surface (CLS) representation, providing a theoretical framework that enables a shift to the surface level. First, a reversible parameterization framework is established to reconstruct the CLS from cutter location points. A UV-Z scalar B-spline representation is then developed to enable efficient forward and inverse evaluations required for CNC interpolation. Based on the reconstructed CLS, the SST is defined at the surface level, and its C2 continuity is theoretically analyzed. The proposed framework ensures smooth evolution along individual toolpaths while maintaining geometric consistency across neighboring paths through the shared CLS representation. An SST-oriented interpolation computational scheme is further developed to support practical feedrate planning and CNC implementation. Comparative studies with LST and GST are conducted on the wavy, mountain-shaped, and human-face surfaces through geometric analysis, kinematic evaluation, and machining experiments. The results demonstrate that SST significantly improves neighboring consistency and surface quality while maintaining profile errors at the same order of magnitude as those of LST and GST. In representative cases, the maximum feedrate difference between neighboring paths is reduced to below 200 mm/min.
Chromatic confocal sensors (CCSs) offer nanometric resolution and sub-micrometric accuracy, making them increasingly valuable in industrial dimensional metrology. When CCS-based dimensional results are compared with tactile probing, surface roughness may lead to systematic optical-tactile deviations due to the different probe-surface interaction mechanisms. An extensive experimental campaign on 29 steel specimens with varied roughness (Ra from 0.0125 µm to 50 µm) produced by diverse machining processes was conducted, complemented by modelling of the probe–surface interactions using morphological filtering simulations. Results reveal that for fine surfaces (Ra < 3 µm), the optical-tactile deviation remains limited, indicating consistency between CCSs and tactile probing under the adopted measurement configuration, while on rougher surfaces, the optical-tactile deviation increased systematically, reflecting the different interaction mechanisms of the probes with surface topography. A proportional relationship between such deviations, the mean peak height roughness parameter Rp and the stylus tip diameter dtip was identified, enabling a simple compensation model to reduce the optical-tactile deviation and align CCS-based dimensional results with the tactile reference. These findings enhance the understanding of CCS metrological performance for dimensional measurements on rough surfaces, extend their applicability and comparability to tactile measurements, and support their reliable integration in advanced manufacturing applications.
Carbon fiber reinforced epoxy resin (CF/EP) composite material has become a key lightweight manufacturing material in aerospace and other fields due to its outstanding properties such as high specific strength and high specific modulus. However, its low interlaminar strength and anisotropy lead to defects like delamination, burrs, and tears during secondary processing. Although ultrasonic vibration-assisted milling is feasible, the influence of ultrasonic process parameters on the processing effects of CF/EP composite material with different fiber orientation angles (FOA) remains unclear. Therefore, this research employs a combined finite element simulation and experimental approach to compare the processing effects of conventional milling (CM), longitudinal ultrasonic vibration milling (LUVM), and longitudinal-torsional ultrasonic vibration milling (LTUVM) on CF/EP with FOA of 0°, 90°, and 45°. Results indicate that ultrasonic vibration milling significantly outperforms CM, altering the fiber fracture pattern, with LTUVM demonstrating the most effective results. The longitudinal-torsional composite vibration of LTUVM differentially modulates the dominant failure modes for different fiber orientation angles: at 0°, the torsional component provides lateral shear to suppress fiber pull-out; at 90°, the longitudinal impact accelerates bending fracture; and at 45°, the composite trajectory dynamically optimizes the fiber shear angle, thereby overcoming the limitations of single-axis longitudinal vibration regarding sensitivity to fiber orientation. In terms of milling force, LTUVM further reduces force by 8–17% compared to LUVM across different fiber angles, with the greatest reduction of up to 13% observed at 90° FOA. Regarding processing temperature, LTUVM achieves an additional 11–13% reduction compared to LUVM, with the most significant cooling effect observed at 45° FOA. Observations of top and bottom edge damage reveal that LTUVM significantly reduces fiber pull-out length through combined longitudinal-torsional vibration, resulting in the shortest edge burrs and nearly eliminating layering defect. Surface morphology analysis indicates that the surface roughness values under LTUVM are reduced by 24–38% compared to LUVM, with fewer surface defects and more uniform resin smearing. This research analyzes the processing effects of LTUVM on CF/EP composite material with different FOA, providing theoretical basis and process references for high-quality processing of carbon fiber composites.
Machine on/off control is an effective approach for achieving energy-saving production scheduling. However, frequent restarts would incur additional energy consumption and accelerate machine degradation. Existing studies on energy-saving distributed flexible job shop scheduling (EDFJSP) overlook this trade-off between restart energy and machine lifespan, which limits the practical effectiveness of energy-saving scheduling. This paper investigates the energy-saving distributed flexible job shop scheduling problem with machine restart-aware (EDFJSP-MR), aiming to minimize both makespan and cumulative carbon emissions. To solve EDFJSP-MR, we propose a two-stage reinforcement learning-guided memetic algorithm (TSRLMA) and develop a dynamic threshold restart-aware strategy that balances reducing energy consumption and maintaining machine longevity. The TSRLMA employs customized crossover and mutation operators to preserve diversity and integrates two novel neighborhood structures to enhance search efficiency. Most importantly, two-stage reinforcement learning strategy further adapts crossover and local search selection through distinct action–reward strategies, enabling continuous optimization throughout the search. The EDFJSP-MR is validated with CPLEX solver, and the performance of TSRLMA is evaluated against five renowned algorithms on benchmark instances. The results indicate that TSRLMA achieves faster convergence and higher-quality solutions, and demonstrates strong applicability and robustness across five real-world aerospace composite manufacturing systems.
This study compares the grindability of stress-relieved additively manufactured Ti-64 (AM Ti-64) and wrought Ti-64 (W Ti-64) during dry grinding with a vitrified-bonded alumina wheel. The objective is to identify how microstructure and surface condition affect grinding forces, wheel wear, residual stress, surface roughness, and specific energy. Across all grinding conditions, both tangential and normal forces increased with the number of passes, indicating progressive wheel wear. Compared with W Ti-64, AM Ti-64 consistently generated higher grinding forces and higher tensile residual stresses, reflecting its harder, more heterogeneous surface condition and stronger tendency toward wheel loading and attritious wear. At lower depth-of-cut conditions, AM Ti-64 showed a pronounced increase in normal force, with increases of about 20–56% from 10-20 to 20–30 passes in the four low-depth conditions (3 µm and 5 µm), leading to wear flats, surface burning, and crack formation. At higher depth conditions (7 µm and 11 µm), the wheel experienced greater grit microfracture during grinding of AM Ti-64, producing a self-sharpening effect that limited the rise in specific energy and improved cutting action. Overall, W Ti-64 exhibited more stable grinding behaviour, whereas AM Ti-64 showed a strong dependence on process severity. The results show that effective grinding of AM Ti-64 requires careful selection of downfeed to avoid thermal damage and premature wheel wear.
Additive manufacturing (AM) enables the fabrication of highly complex components but suffers from poor surface quality that limits functional performance. Viscous film-based electrochemical polishing (ECP) achieves excellent conformal polishing of complex structural parts by establishing a near-equipotential boundary through the formation of high-impedance viscous films on workpiece surfaces. However, on rough AM surfaces, viscous film conformal replication minimizes impedance differences between protrusions and pits, resulting in poor surface uniformity. To address this limitation, a tailored viscous film distribution strategy is proposed by directionally disrupting the viscous film on protrusions while preserving it in pits, thereby amplifying the protrusion–pit impedance difference. Based on this concept, a tailored viscous film electrochemical mechanical polishing (TVF-ECMP) method is developed, in which controlled abrasive–workpiece motion selectively removes viscous films from protrusions, maintaining rapid anodic dissolution on protrusions while suppressing dissolution in pits. After 20 min of TVF-ECMP, the maximum height of profile (Rz) of additively manufactured Inconel 718 surface decreases by over 90%, which cannot be achieved by conventional ECP. The tailored viscous film concept can also be extended to other multi-field-assisted ECP approaches for rough AM surfaces.
The geometry and handedness of helices produced by polygonal turning are highly sensitive to the relative speed ratio between the tool and workpiece, making reliable analytical prediction of process outcomes challenging, particularly under asynchronous conditions. Asynchronous speed ratios for the context of this study are all speed ratios except the special ones (synchronous) that produce prismatic polygonal shapes. This study presents a generalized numerical framework for polygonal turning that explicitly accounts for fractional speed ratios, enabling prediction of both regular lobed geometries and helically lobed profiles, including near-flat surfaces produced over a prescribed axial length. An algorithmic formulation is developed to model helix angle and direction under arbitrary tool–workpiece speed ratios and feed rates and is validated through dedicated machining experiments. The model captures the transition from regular lobed geometries under synchronous rotation to helically lobed geometries under asynchronous rotation and establishes the conditions governing helix formation, handedness, and sensitivity. The effects of speed ratio and feed on helix angle are analytically estimated and experimentally verified, showing that for a fixed speed ratio, increasing feed reduces the helix angle, while for a fixed feed, increasing the speed ratio drives the helix angle toward zero as the process approaches synchronous conditions and further changes helix handedness as it crosses the synchronous point. A sensitivity analysis demonstrates that helix direction is extremely sensitive to small changes in speed ratio and spindle rotation sense, with changes as small as one RPM sufficient to reverse helix handedness depending on whether the tool or workpiece rotational speed dominates. Micro-CT cross-sectional imaging confirms the predicted helical twist along the machined length and reveals localized surface waviness and chip side flow near tool entry and exit, while the central region remains comparatively smooth. At very low feed rates on the order of 0.0025 mm/rev, thread-like geometries with minimal waviness and negligible exit burrs are achieved. The model predicts thread pitch with an error of approximately 4.5% and helix angle with errors below 3.5% across a range of conditions. Finally, the framework is demonstrated through the successful fabrication of dental screw prototypes, highlighting its applicability for industrial implementation of polygonal turning with controlled helical geometry.
Electrical treatment (ET) enables rapid microstructural restoration in deformed CP-Ti sheets, thereby significantly improving formability and exhibiting strong potential for integration into various forming processes to overcome intrinsic forming-limit constraints. However, the physical origins governing the effects of ET remain insufficiently understood. In this study, the microstructural restoration behavior under ET was compared with that under conventional furnace annealing using quasi in-situ electron back scatter diffraction characterization. It was found that beyond mere acceleration, ET enables a larger extent of recovery and induces a distinct microstructural transformation pathway. Subsequently, the potential mechanisms, including selective heating, electromigration, and charge imbalance effects, were investigated through finite element simulations, theoretical modeling and first-principles calculations. The results reveal that selective heating promotes dislocation glide by inducing transient overheating. Electromigration exhibits strong electro-thermal coupling, which is intensified with increasing temperature and current density, thereby providing a substantial kinetic driving force for self-diffusion at elevated temperatures. The charge imbalance effect locally enhances electromigration via defect-induced current crowding and, concurrently, weakens atomic bonding at defect sites, thereby reducing barriers for both dislocation glide and self-diffusion, as well as grain boundary migration resistance and pinning strength. Finally, the respective contributions of these mechanisms to the divergent microstructural restoration behaviors were systematically elucidated.
To overcome the bottleneck that conventional flexible plates lack the stiffness required to maintain SiC wafer surface shape accuracy and thus deliver low removal rates, we propose a planarization approach based on a soft-hard combined polishing plate (SHCPP). The SHCPP uses an epoxy honeycomb as rigid support to suppress plate deformation and employs a magnetorheological elastomer (MRE) loaded with diamond abrasives to provide flexible and controllable removal of SiC wafers. Following this design, the SHCPP was fabricated and applied to ultra-precision polishing of SiC wafers. Finite element simulation and multi-dimensional metrology revealed its “rigid-support–soft-removal, chemical–mechanical zone-coupling” characteristics. After 60 min of polishing, the wafer exhibited a material removal rate of 9.4 nm/min, surface roughness dropped from 180 nm to 5.6 nm, and surface shape accuracy remained excellent (Bow 1.6 µm, Warp 5.5 µm, TTV 1.0 µm). The removal contributions ranked pure mechanical > magneto-mechanical > Fenton-reaction–mechanical. The SHCPP realizes favorable planarization by using the hard matrix to restrain macro-scale interface deformation, the soft matrix to conform flexibly to the SiC surface, magnetic control to raise the soft matrix modulus by 13.0%, and the Fenton reaction to generate a soft SiO2 layer that lowers removal resistance.
High efficiency milling shows great promise for improving the machinability of aviation aluminum alloys. However, predicting the machined surface errors of aluminum alloys holes remains challenging due to the complex effects of random cutting vibration and residual stress release. In this study, machined hole surface morphology model considering random cutting vibration and residual stress release is proposed, the machined surface errors prediction method based on the proposed surface morphology is developed and validated. The residual stress release process and its influencing law is clarified. Results reveal that the average prediction accuracy of random cutting vibration displacement, residual stress and machined surface errors are 85.89%, 85.62% and 92.83%, respectively. Furthermore, the accuracy of the proposed surface prediction method was verified. This work links entire machined surface formation and evolution process and the findings deepen the mechanistic understanding of multiple effects of dynamic milling cutter trajectories and residual stress release.
Conventional steel ball rolling processes suffer from defects such as incomplete filling and surface scratches, and often exhibit limited forming stability. To address these issues, a single-roller multi-wedge cross rolling (SRMCR) process is proposed for simultaneously forming multiple balls. A theoretical model based on the principle of volume conservation is established to determine key die geometric parameters and precisely control billet volume during forming. Finite element simulations are conducted to investigate the metal flow behavior, stress-strain evolution and damage distribution. The results show that asynchronous metal flow occurs along the rolling direction during the SRMCR process. The strain distribution exhibits clear symmetry and consistent deformation among adjacent balls, indicating stable forming behavior. Distributions of stress triaxiality, the Lode parameter, and Cockcroft-Latham damage reveal that the pole and connecting neck regions exhibit higher damage tendencies than the core region. Process parameter analysis indicates that a diameter expansion ratio of 110% combined with linear wedge-height variation provides the optimum forming conditions for 6 mm steel balls. Validation experiments produce 6 mm steel balls with complete filling, no obvious scratches, and significantly improved mechanical properties. Multi-ball experiments achieve stable simultaneous forming of up to eight balls with a material utilization exceeding 90%, demonstrating good scalability and industrial applicability. Microstructural analysis indicates that the strengthening mainly results from severe plastic deformation-induced dislocation accumulation and subgrain formation. The proposed process overcomes the limitations of conventional ball rolling processes and provides an effective approach for the precision cold forming of small-diameter steel balls.
This study deals with a nondeterministic-polynomial (np) hard optimization scheduling problem of multi-model lines to identify an optimal job order sequence for permutation flow shop characteristics using a genetic algorithm. The aim of this study is to eliminate setup times that occur in the material placement by defining precedence constraints of batch orders. In addition, these precedence constraints are adapted in such a way that they reduce the amount of variant changeovers in order to improve the resulting costs of internal logistics processes. To validate the empirical study, four variants are investigated, each of which is grouped into fixed batches with a batch size of 14 job orders. The number of assembly stations in the study is 67, the processing times of the variants are known and the number of job orders (for the period under investigation) is constant. The job order sequences are optimized for the objective functions number of variant changeovers and analyzed with regard to variant changeovers, idle times and flow times. In this context, a statistical evaluation is carried out with regard to the significance of the influencing variables on the objective functions mentioned. As a result, it can be stated that the precedence constraints PRC.2 and PRC.3 can be used both to eliminate setup times and to reduce costs in the internal logistics process. In this context no negative effects on the flow time, idle time and runtime can be determined.
Friction plug-riveting spot welding (FPRSW) is a solid state joining technique for steel/aluminum structures. Constant axial force tracking is essential for maintaining process stability and joint quality. However, during friction between the rivet and the aluminum/steel sheets, drastic interfacial temperature fluctuations occur. As a result, the rivet material properties evolve in a strongly nonlinear manner with temperature. This nonlinearity often causes force overshoot during rivet–aluminum friction and undershoot during rivet/steel welding, which severely challenges conventional PID control. To address this issue, a two stage composite control strategy is proposed. The first stage employs a Johnson-Cook feedforward model, in which the temperature softening term is replaced by an energy-normalized variable derived from real time mechanical work. The second stage uses a neural network tuned PID (NN-PID) feedback controller for adaptive parameter regulation. Experimental results show that the proposed method eliminates overshoot in the aluminum penetration stage without increasing cycle time. The undershoot is reduced by 40.96%, 60.48%, and 76.25% under welding forces of 6000, 7000, and 8000 N, respectively. Joint performance is also enhanced, with the maximum tensile load increased by 5–15% and energy absorption improved by up to 35%. The proposed strategy optimizes both force trajectory and joint mechanical performance without relying on direct interface temperature measurement, providing a practical pathway toward intelligent control of dissimilar metal welding processes.
