Search Results (148)

Machining nickel-based superalloys, such as Inconel 718, poses a significant technological challenge due to their high-temperature strength and low thermal conductivity, leading to rapid tool wear. This paper presents a comprehensive comparative analysis of two roughing strategies: high-feed milling and plunge milling, utilizing a unique custom-designed milling head. The primary objective was to evaluate the impact of tool material reinforcement on the process by comparing SiC whisker-reinforced ceramic inserts (CW100) with non-reinforced inserts (CS300). The experiment involved measuring cutting force components, power consumption, and analyzing tool wear progression (VBB) and mechanisms. Results showed that the presence of the reinforcing phase is critical for reducing the axial force component (Fz), particularly in plunge milling, where CW100 inserts achieved a 30–35% force reduction and avoided the catastrophic failure observed in non-reinforced ceramics. Microscopic analysis confirmed that composite inserts undergo predictable abrasive wear, whereas CS300 inserts are prone to brittle fracture and spalling. Multi-criteria optimization using Grey Relational Analysis (GRA) identified high-feed milling with reinforced inserts as the most efficient strategy, while also positioning plunge milling with composites as a competitive, less energy-intensive alternative. Full article

Machining of medium-carbon steels, such as AISI 1050, poses a significant challenge in terms of achieving stable cutting conditions, controlled chip evacuation and high surface integrity, in particular when full-face milling is performed under elevated material removal rates. The tool surface engineering approach, particularly laser-induced micro-texturing, comprises a promising route toward modifying the tribological conditions at the tool–chip interface, thus affecting friction, heat generation, chip formation and the resultant surface finish. This study investigates the combined effects of cutting speed, axial depth of cut and tool micro-texture orientation (parallel versus orthogonal to the chip flow direction) on machining performance under wet conditions. In addition to the experimental analysis of cutting forces, chip morphology and surface roughness, this work integrates a full factorial Design of Experiments, regression modeling, and ANOVA to quantify the statistical significance of each factor and to identify dominant interactions. The regression models show strong predictive capability across all measured responses, while the ANOVA confirms the axial depth of cut and tool texture orientation as the most influential parameters. Multi-objective optimization by Pareto analysis further underlines the superiority of orthogonal micro-texturing, which consistently reduces the cutting forces and improves surface quality while promoting controlled chip segmentation. The results provide quantitative and statistically validated evidence of the enhancement of lubrication effectiveness, reduction in interface friction, and stabilization in chip formation provided by the micro-textured tools. Overall, the findings contribute to the development of data-driven machining strategies and surface-engineered cutting tools in view of improved productivity, energy efficiency and surface integrity in advanced manufacturing applications. Full article

Micro milling of metallic materials presents unique dynamic challenges due to highly nonlinear cutting forces and the susceptibility to self-excited vibrations (chatter). This paper presents a novel mathematical model for chatter prediction in micro milling, based on an enhanced formulation of cutting forces that includes the frictional interaction between the tool’s flank face and the machined surface. The proposed approach enables accurate simulation of the cutting process and prediction of the limiting depth of cut, beyond which chatter occurs. Experimental validation was performed using pneumatic spindle and micro end mills, with chatter detection based on surface inspection via digital microscopy. A strong correlation was observed between the simulated and experimentally determined limiting depths of cut, confirming the model’s predictive capability. This research offers a new methodology for modelling cutting forces and improves the ability to predict chatter in micro milling processes, contributing to the optimization of machining parameters across a wide range of materials. Full article

This research introduces a new model to predict the roll force during hot rolling of steel, based on a statistical analysis of approximately 38,980 sets of measurements in a commercial mill with five finishing stands. The study includes ten different steel grades and features models of both single grades and the entire dataset. Three models are developed and compared: a temperature-dependent strain rate model (M1), a strain rate model (M2), and a simplified strain rate model (M3). The decrease in temperature with roll stand has a strong cross-correlation with compensating decreases in strain and contact length by roll stand, such that both the temperature and strain terms are statistically insignificant. The final model (M3)—$F\left[\mathrm{N}\right]=113.1·\dot{ϵ}{\left[{\mathrm{s}}^{-1}\right]}^{0.3141}·w\left[\mathrm{mm}\right]·\ell \left[\mathrm{mm}\right]$—relates force (F) to strain rate ($\dot{ϵ}$), width (w), and contact length (ℓ) and achieves an $R^2$ fit of 0.946 over all 10 steel grades. Although the single-grade models show slightly higher accuracy, the final model retains robust predictive capability with only two fitting parameters. This model enables fast and easy estimation of roll force for commercial hot rolling of low-carbon, medium-carbon, and high-strength–low-alloy steels. Full article

Digital Twin Systems (DTSs) are increasingly recognized as enablers of data-driven manufacturing, yet many implementations remain limited to monitoring or visualization without closed-loop control. This study presents a fully integrated DTS for CNC milling that emphasizes real-time bidirectional coupling between a real machine and a virtual counterpart as well as the use of machine-native signals. The architecture comprises a physical space defined by a five-axis machining center, a virtual space implemented via a dexel-based technological simulation environment, and a digital thread for continuous data exchange between those. A full-factorial simulation study investigated the influence of dexel density and cycle time on engagement accuracy and runtime, yielding an optimal configuration that minimizes discretization errors while maintaining real-time feasibility. Latency measurements confirmed a mean response time of 34.2 ms, supporting process-parallel decision-making. Two application scenarios in orthopedic implant milling validated the DTS: process force monitoring enabled an automatic machine halt within 28 ms of anomaly detection, while adaptive feed rate control reduced predicted form error by 20 µm. These findings demonstrate that the DTS extends beyond passive monitoring by actively intervening in machining processes; enhancing process reliability and part quality; and establishing a foundation for scalable, interpretable digital twins in regulated manufacturing. Full article

This paper reports the results of a study examining the effect of thermomechanical interactions that occur during a milling process conducted at a maximum axial depth of cut for a thin wall made of aluminium alloy 7050 T7451. The impact of cutting speed and wall thickness on cutting force and cutting temperature was determined. Response surface methodology and face-centred central composite design were used. It was found that raising the cutting speed to approximately v_c ≈ 700 m/min led to an increase in cutting force component F_x and cutting temperature T, followed by a decrease in their values. Nonetheless, these variables at v_c = 900 m/min were considerably higher than those observed at v_c = 300 m/min. The thinnest tested wall of t = 1 mm exhibited the greatest process instability and evident signs of chatter, while a wall thickness increase to t = 2 mm resulted in improved process stability and reduced flatness deviation. The interaction between the cutting force and the cutting temperature, as well as the occurrence of chatter, were established as two dominant factors affecting thin wall machining accuracy. Results showed that the assumed empirical models could be used to predict the tested dependent variables under similar milling conditions. Full article

Wood–plastic composite (WPC) is being increasingly adopted in construction and furniture applications due to its durability and recyclability. This study investigates face-milling responses—resultant cutting force and cutting temperature—under systematically varied cutting parameters, and develops a radial basis function neural network for predictive modeling. Experiments were conducted on a computer numerical control machining center using a polycrystalline diamond end-milling cutter for face milling with fixed axial depth of cut. Feed speed, radial depth of cut, and spindle speed were selected as input factors. The results indicate that feed speed and radial depth of cut generally increase all force components, whereas higher spindle speed tends to reduce force magnitudes while elevating temperature. The radial basis function neural network yields acceptable accuracy for resultant cutting force (coefficient of determination R² ≈ 0.91) and acceptable accuracy for cutting temperature (R² ≈ 0.81). These findings demonstrate the feasibility of radial basis function neural network based prediction for WPC face milling and provide guidance for parameter selection. Full article

Tool edge preparation is the process aimed at eliminating edge defects and optimizing the micro-geometric parameters of cutting tools. Drag finishing, the primary engineering method, subjects tools to planetary motion (simultaneous revolution and rotation) within abrasive media to remove burrs and micro-chips, thereby improving cutting performance and extending tool life. A discrete element method (DEM) model of drag finishing edge preparation was developed to investigate the effects of processing time, tool rotational speed, and rotation direction on abrasive-mediated tool wear behavior. The model was validated through milling cutter edge preparation experiments. Simulation results show that increasing the processing time causes fluctuating changes in average abrasive velocity and contact forces, while cumulative energy and tool wear increase progressively. Elevating tool rotational speed increases average abrasive velocity, contact forces, cumulative energy, and tool wear. Rotation direction significantly impacts tool wear: after 2 s of clockwise (CW) rotation, wear reached 1.45 × 10⁻⁸ mm; after 1 s of CW followed by 1 s of counterclockwise (CCW) rotation, wear was 1.25 × 10⁻⁸ mm; and after 2 s of CCW rotation, wear decreased to 1.02 × 10⁻⁸ mm. Experiments, designed based on simulation trends, confirm that edge radius increases with time and tool rotational speed. After 30 min of processing at 60, 90, and 120 rpm, average edge radius increased to 22.5 μm, 28 μm, and 30 μm, respectively. CW rotation increased the edge shape factor K, while CCW rotation decreased it. The close agreement between experimental and simulation results confirms the model’s effectiveness in predicting the impact of edge preparation parameters on tool geometry. Rotational speed control optimizes edge preparation efficiency, the predominant tangential cumulative energy reveals abrasive wear as the primary material removal mechanism, and rotation direction modulates the shape factor K, enabling symmetric edge preparation. Full article

Ultrasonic-assisted machining (UAM) has emerged as a transformative technology for increasing material removal efficiency, improving surface quality and extending tool life in precision manufacturing. This review specifically focuses on the application of it to titanium aluminide (TiAl) alloys. These alloys are widely used in aerospace and automotive sectors due to their low density, high strength and poor machinability. This review covers various aspects of UAM, including ultrasonic vibration-assisted turning (UVAT), milling (UVAM) and grinding (UVAG), with emphasis on their influence on the machinability, tool wear behavior and surface integrity. It also highlights the limitations of single-energy field UAM, such as inconsistent energy transmission and tool fatigue, leading to the increasing demand for multi-field techniques. Therefore, the advanced machining strategies, i.e., ultrasonic plasma oxidation-assisted grinding (UPOAG), protective coating-assisted cutting, and dual-field ultrasonic integration (e.g., ultrasonic-magnetic or ultrasonic-laser machining), were discussed in terms of their potential to further improve TiAl alloys processing. In addition, the importance of predictive force models in optimizing UAM processes was also highlighted, emphasizing the role of analytical and AI-driven simulations for better process control. Overall, this review underscores the ongoing evolution of UAM as a cornerstone of high-efficiency and precision manufacturing, while providing a comprehensive outlook on its current applications and future potential in machining TiAl alloys. Full article

Robotic milling offers significant advantages for machining large aerospace components due to its low cost and high flexibility. However, compared to computerized numerical control (CNC) machine tools, robot systems exhibit lower stiffness, leading to force-induced deformation during milling process that significantly compromises path accuracy. This study proposed a dynamic robot posture programming method to enhance the stiffness for aluminum alloy milling task. Firstly, a milling force prediction model is established and validated under multiple postures and various milling parameters, confirming its stability and reliability. Secondly, a robot stiffness model is developed by combining system stiffness and milling forces within the milling coordinate system to formulate an optimization index representing stiffness performance in the actual load direction. Finally, considering the constraints of joint limit, singular position and joint motion smoothness and so on, the robot posture in the milling trajectory is dynamically programmed, and the joint angle sequence with the optimal average stiffness from any cutter location (CL) point to the end of the trajectory is obtained. Under the assumption that positioning errors were effectively compensated, the experimental results demonstrated that the proposed method can control both axial and radial machining errors within 0.1 mm at discrete points. For the specific milling trajectory, compared to the single-step optimization algorithm starting from the initial optimal posture, the proposed method reduced the axial error by 12.23% and the radial error by 8.61%. Full article

Corner radius end mills (CREMs) are widely used in machining due to their unique tool geometry, which improves surface quality. Variations in cutting force during machining significantly impact machining quality. Therefore, precisely predicting cutting forces is critical for controlling machining chatter and enhancing accuracy. Traditional element force models have complex formulas and high computational demands when considering tool runout. This paper proposes a hybrid prediction model for CREMs that integrates the separate-edge-forecast method and the BP neural network. The integration approach incorporates runout effects into cutting force coefficients and addresses nonlinear effects from runout. The accuracy of the cutting force prediction model was validated through side milling on 7075 aluminum alloy. The results indicate that the maximum error between the predicted and measured forces is 9.43%, demonstrating that this model ensures high prediction accuracy while reducing computation cost. Full article

In flank milling of thin-walled workpieces, machining deformation is a key issue affecting workpiece accuracy and process stability. Although the traditional finite element method (FEM) offers high accuracy, its low computational efficiency makes it difficult to meet the requirements for rapid prediction in engineering practice. For this purpose, this paper proposes an efficient method for predicting workpiece deformation based on the voxel octree model. First, based on the analysis of the contact position between the cutting tool and the workpiece, the thin-walled workpiece is divided into six levels of voxel units, using a voxel octree model. Then, the stiffness matrix and update model of the voxel units are established. Finally, the deformation prediction is completed by calculating the micro-milling force and the voxel stiffness matrix. The experimental results show that the workpiece deformation predicted by the proposed method is highly consistent with the actual machining measurement. At the same time, compared with traditional FEM and voxel model methods, the calculation time is reduced by 90% and 13.2%, respectively. This method can provide rapid decision support for the optimization of thin-walled workpiece machining processes and effectively improve the efficiency of preliminary research in actual machining. Full article

While the machining of Inconel 718 has been widely studied, its cast counterpart Inconel 713LC remains underexplored, despite its relevance in high-temperature aerospace and energy components. This work presents a comprehensive investigation of dry milling behavior in Inconel 713LC, focusing on the interplay between tool wear, cutting forces, surface integrity, and chip formation across a broad range of cutting parameters. A stable process window was identified: 30–50 m/min cutting speed and 0.045–0.07 mm/tooth feed, where surface roughness remained below Ra 0.6 µm and tool life exceeded 10 min. Outside this window, rapid thermal and mechanical degradation occurred, leading to flank wear beyond the 550 µm limit and unstable chip morphology. The observed trends align with those in Inconel 718, allowing the cautious transfer of established strategies to cast alloys. By quantifying key process–performance relationships and validating predictive models for tool life and cutting forces, this study provides a foundation for optimizing the dry machining of cast superalloys. The results advance sustainable manufacturing practices by reducing reliance on cutting fluids while maintaining surface and dimensional integrity in demanding applications. Full article

The increasing demand for energy efficiency in manufacturing has driven the need for advanced modeling techniques to optimize power consumption in machining processes. This study presents a novel approach to modeling power consumption in milling processes using machine learning, leveraging a custom-designed braking device integrated into the milling machine’s main spindle to measure friction forces with high precision. A comprehensive dataset of observations, including parameters such as speed, force, intensity, apparent power, active power, and power factor, was collected under loaded conditions. Nine machine learning models—Linear Regression, Random Forest, Support Vector Regression, Polynomial Regression, Multi-Layer Perceptron with 2 and 3 layers, K-Nearest Neighbors, Bagging, and a hybrid Probabilistic Random Forest—Multi-Layer Perceptron (PIRF–MLP)—were evaluated using 5-fold cross-validation to ensure robust performance assessment. The PIRF–MLP model achieved the highest performance, demonstrating superior accuracy in predicting utile power. The feature importance analysis revealed that force and speed significantly influence power consumption. The proposed methodology, validated on a milling machine, offers a scalable solution for real-time energy monitoring and optimization in machining, contributing to sustainable manufacturing practices. Future work will focus on expanding the dataset and testing the models across diverse machining conditions to enhance generalizability. Full article

The 7075-T7451 aluminum alloy, widely used in aerospace, aviation, and automotive fields for critical load-bearing components due to its excellent mechanical properties, suffers from residual stresses induced by thermo-mechanical coupling during milling, which deteriorate workpiece performance. This study explores how key milling parameters—spindle speed *n_c*, feed per tooth *f_z*, cutting depth *a_p*, and cutting width *a_e*—affect surface residual stress and cutting force via orthogonal experiments and finite element analysis (FEA). Results show *a_e* is critical for X-direction residual stresses, while *f_z* dominates Y-direction ones. Cutting force increases with *f_z*, *a_p*, and *a_e* but decreases with higher *n_c*. Multivariate regression-based prediction models for residual stress and cutting force were established, which effectively characterize parameter–response relationships with maximum prediction errors of 18.69% (residual stress) and 12.27% (cutting force), showing good engineering applicability. The findings provide theoretical and experimental foundations for multi-parameter optimization in aluminum alloy milling and residual stress/cutting force control, with satisfactory practical effectiveness. Full article