Search Results (453)

Barrel tools are increasingly used in high-precision machining of free-form surfaces. However, limited studies evaluate their performance specifically on spherical geometries, where tool–surface contact characteristics differ significantly. Understanding how tool geometry and process parameters influence surface quality and cutting forces in such cases remains underexplored. This study evaluates how barrel cutter radius and varying machining parameters affect cutting forces and surface roughness when milling internal and external spherical surfaces. Machining tests were conducted on structural steel 1.1191 using two barrel cutters with different curvature radii (85 mm and 250 mm) on a 5-axis CNC machine. Feed per tooth and radial depth of cut were systematically varied. Cutting forces were measured using a dynamometer, and surface roughness was assessed using the Rz parameter, which is more sensitive to peak deviations than Ra. Novelty lies in isolating spherical surface shapes (internal vs. external) under identical path trajectories and systematically correlating tool geometry to force and surface metrics. The larger curvature tool (250 mm) consistently generated up to twice the cutting force of the smaller radius tool under equivalent conditions. External surfaces showed higher Rz values than internal ones due to less favorable contact geometry. Radial depth of the cut had a linear influence on force magnitude, while feed rate had a limited effect except at higher depths. Smaller-radius barrel tools and internal geometries are preferable for minimizing cutting forces and achieving better surface quality when machining spherical components. The aim of this paper is to determine the actual force load and surface quality when using specific cutting conditions for internal and external spherical machined surfaces. Full article

Manufacturing in modern industrial sectors involves the machining of components where the undeformed chip thickness inevitably decreases to values comparable to the tool edge radius. Under such conditions, the ploughing effect between the workpiece and the tool becomes dominant, followed by the noticeable formation of a stagnation zone. This paper presents research focused on the analysis of the cutting process for small cross-sections of the removed layers, based on cutting force components. This study investigated the machining of two titanium alloy grades—Ti Grade 5 (Ti-6Al-4V) and Ti Grade 2—with the main focus on process stability. A material separation model was analyzed to demonstrate the mechanism of material flow within the cross-section of the machined layer. It was found that the material has a limited ability to flow sideways at the boundary of the chip thickness, thus determining the probable size of the stagnation zone in front of the cutting edge. Orthogonal cutting experiments enabled the determination of the minimum chip thickness coefficient for constant temperature conditions, independent of the tool edge radius, as $h_{min0}=$0.313. In oblique cutting tests, the sensitivity of thin-layer machining was demonstrated for the determined values of minimum undeformed chip thickness. By applying the 0–1 test for chaos, the measurement time (parameter $T·dt$) was determined for both titanium alloys to determine the range of observable chaotic behavior. The analyses confirmed that Ti Grade 2 enters chaotic dynamics much more rapidly than Ti Grade 5 and displays local cutting instabilities independent of the uncut chip thickness. Full article

Gas–water two-phase flow in horizontal and inclined wells is significantly influenced by gravitational forces and spatial asymmetry around the wellbore, resulting in complex and variable flow patterns. Accurate measurement of water holdup is essential for analyzing phase distribution and understanding multiphase flow behavior. Water holdup imaging provides a valuable means for visualizing the spatial distribution and proportion of gas and water phases within the wellbore. In this study, air and tap water were used to simulate downhole gas and formation water, respectively. An array capacitance arraay tool (CAT) was employed to measure water holdup under varying total flow rates and water cuts in a horizontal well experimental setup. A total of 228 datasets were collected, and the measurements were processed in MATLAB (2020 version) using three interpolation algorithms: simple linear interpolation, inverse distance interpolation, and Lagrangian nonlinear interpolation. Water holdup across the wellbore cross-section was also calculated using arithmetic averaging and integration methods. The results obtained from the three imaging algorithms were compared with these reference values to evaluate accuracy and visualize imaging performance. The CAT demonstrated reliable measurement capabilities under low- to medium-flow conditions, accurately capturing fluid distribution. For stratified flow regimes, the linear interpolation algorithm provided the clearest depiction of the gas–water interface. Under low- to medium-flow rates with high water content, both inverse distance and Lagrangian methods produced more refined images of phase distribution. In dispersed flow conditions, the Lagrangian nonlinear interpolation algorithm delivered the highest accuracy, effectively capturing subtle variations within the complex flow field. Full article

The aim of this study was to analyse in detail the effect of varying the parameters of the Johnson–Cook (JC) material model on the results of a numerical simulation of the orthogonal turning process of the Ti6Al4V titanium alloy. The first step involved an experimental study, including the recording of cutting force components and temperature, as well as the measurement of chip geometry, which was used to validate the FEM simulation. This was followed by a sensitivity analysis of the JC model with respect to five parameters, namely A, B, C, m, and n, each modified independently by ±20%. The effects of these changes on cutting forces, cutting zone temperature, stresses, and chip geometry were evaluated. The results showed that parameters A, B, and m had the greatest influence on the physical quantities analysed, while C and n are of secondary importance. The analysis highlighted the need for precise calibration of the JC model parameters, especially when modelling machining processes involving difficult-to-machine materials. The results provided practical guidance for optimising the selection of constitutive parameters in machining simulations. Full article

The commercialization of selective broccoli harvesters, a critical response to agricultural labor shortages, is hampered by end effectors with large operational envelopes and poor adaptability to complex field conditions. To address these limitations, this study developed and evaluated a novel end-effector with an integrated transverse cutting mechanism and a foldable grasping cavity. Unlike conventional fixed cylindrical cavities, our design utilizes actuated grasping arms and a mechanical linkage system to significantly reduce the operational footprint and enhance maneuverability. Key design parameters were optimized based on broccoli morphological data and experimental measurements of the maximum stem cutting force. Furthermore, dynamic simulations were employed to validate the operational trajectory and ensure interference-free motion. Field tests demonstrated an operational success rate of 93.33% and a cutting success rate of 92.86%. The end effector successfully operated in dense planting environments, effectively avoiding interference with adjacent broccoli heads. This research provides a robust and promising solution that advances the automation of broccoli harvesting, paving the way for the commercial adoption of robotic harvesting technologies. Full article

Background/Objectives: Limb malalignment disrupts physiological joint forces and predisposes individuals to the development of osteoarthritis. Surgical interventions such as distal femur or high tibial osteotomy aim to restore mechanical balance on weight-bearing joints, thereby reducing long-term morbidity. Accurate alignment is crucial since it cannot be adjusted after stabilization with plates and screws. Recent advances in personalized medicine offer the opportunity to tailor surgical corrections to each patient’s unique anatomy and biomechanical profile. This study evaluates the benefits of 3D planning and patient-specific cutting guides over traditional 2D planning with standard implants for alignment correction procedures. Methods: We assessed limb alignment parameters pre- and postoperatively in patients with varus and valgus lower limb malalignment undergoing acute realignment surgery. The cohort included 23 opening-wedge high tibial osteotomies and 28 opening-wedge distal femur osteotomies. We compared the accuracy of postoperative alignment parameters between patients undergoing traditional 2D preoperative X-ray planning and those using 3D reconstructions of CT data. Outcome measures included mechanical axis deviation and tibiofemoral angles. Results: 3D reconstructions of computerized tomography data and patient-specific cutting guides significantly reduced the variation in postoperative limb alignment parameters relative to preoperative goals. In contrast, traditional 2D planning with standard non-custom implants resulted in higher deviations from the targeted alignment. Conclusions: Utilizing 3D CT reconstructions and patient-specific cutting guides enhances the accuracy of postoperative limb realignment compared to traditional 2D X-ray planning with standard non-custom implants. Patient-specific instrumentation and personalized approaches represent a key step toward precision orthopedic surgery, tailoring correction strategies to individual patient anatomy and potentially improving long-term joint health. This improvement may reduce the morbidity associated with lower limb malalignment and delay the onset of osteoarthritis. Level of Evidence: Therapeutic Level III. Full article

This article proposes a model to estimate the optimal cutting speed and depth of cut used in the machining process by chip removal during the turning operation, considering the structural integrity of the workpiece to be machined. The structural integrity model is proposed considering the rounding of the cutting tool nose as a measure of roughness requested in the workpiece, the electrical power capacity delivered by the machine tool motor as a load-limiting factor for the process, the geometry of the desired workpiece, and the physical machining parameters given by cutting tool manufacturers. Based on these criteria, an estimation algorithm is proposed that integrates these parameters and executes the search for the optimal cutting depth and cutting speed, meeting the structural integrity criterion in accordance with the minimum machining time criterion in the turning process, establishing a balance between process reliability and minimization of machining time. The proposed model is innovative since it presents a new methodology to determine the depth of cut and calculate the machining speed under the criterion of preserving the structural integrity of the piece to be machined to the maximum. This means that the depth of cut and spindle speed estimated under the proposed methodology guarantee that during the machining process, the workpiece will not suffer structural damage from the cutting forces involved in the machining process, minimizing the effects of loading in areas of stress concentration, thereby contributing to highlighting and involving the concept of process reliability. This model provides a new theoretical method for technologists involved in the calculation of the machining process, offering them a theoretical basis for their proposals for depth of cut and cutting speed. Full article

This paper presents the design and validation of a novel specialized fixture device for machining inclined planes with barrel cutters on 3-axis CNC machine tools. Barrel milling, also known as Parabolic Performance Cutting (PPC), is extensively used on 5-axis machines to enhance the efficiency of machining complex surfaces. While significant research has focused on optimizing barrel milling for aspects such as surface roughness and cutting forces, implementing this technique on 3-axis machines poses a challenge due to limitations in tool orientation. To overcome this, an innovative adaptable device was designed, enabling precise workpiece orientation relative to the barrel cutter. To overcome this limitation, an adaptable device was designed that enables precise workpiece orientation relative to the barrel cutter. The device utilizes interchangeable locating elements for different cutter programming angles (such as 18°, 20°, and 42.5°), thereby ensuring correct workpiece positioning. Rigid workpiece clamping is provided by the device’s mechanism to maintain precise workpiece positioning during machining, and probing surfaces are integrated into the device to facilitate the definition of the coordinate system necessary for CNC machine programming. Device control was performed using a Hexagon RA-7312 3D measuring arm. Inspection results indicated minimal dimensional deviations (e.g., surface flatness between 0.002 mm and 0.012 mm) and high angular accuracy (e.g., angular non-closure of 0.006°). The designed device enables the effective and precise use of barrel cutters on 3-axis CNC machines, offering a previously unavailable practical and economical solution for cutting tool tests and cutting regime studies. Full article

The study investigates the problem of modeling cutting-force components through response surface methodology and reports the results of an investigation into the impact of machining parameters on the cutting mechanics of polymer–matrix composites. The novelty of this study is the modeling of cutting forces and the determination of mathematical models of these forces. The models describe the values of forces as a function of the milling parameters. In addition, the cutting resistance of the composites was determined. The influence of the material and rake angle of individual tools on the cutting force components was also determined. Measurements of the main (tangential) cutting force showed that, using large rake angles for uncoated carbide tools, one could obtain maximum force values that were similar to those obtained with polycrystalline diamond tools with a small rake angle. The results of the analysis of the tangential component of cutting resistance showed that, regardless of the rake angle, the values range from 140 N to 180 N. An analysis of the feed component of cutting resistance showed that the maximum values of this force ranged from 46 N to 133 N. The results showed that the highest values of the feed component of cutting resistance occurred during the machining of polymer composites with carbon fibers and that they were most affected by feed per tooth. It was also shown that the force models determined during milling with diamond insert tools had the highest coefficient of determination in the range of 0.90–0.99. The cutting resistance analysis showed that the values tested are in the range of 3.8 N/mm² to 15.5 N/mm². Full article

This study proposes a dual-statistical and gradient-based framework to evaluate the machinability of five engineering alloys under CNC turning. Cutting force and surface deformation were measured across five machining zones. Finite difference-based gradients revealed spatial variations in material response. Stainless Steel 304 showed the highest cutting force (328 N), while Aluminum 6061 had the highest deformation (0.0164 mm). Carbon Steel 1020 exhibited the highest force-to-deformation efficiency (>97,000 N/mm). Arithmetic and harmonic means highlighted statistical sensitivities, while principal component analysis (PCA) identified clustering among materials and reduced dimensionality. A composite machinability score, integrating stiffness variation, efficiency gradients, and multivariate features, ranked Aluminum 6061 highest, followed by Brass C26000 and Bronze C51000. This methodology enables interpretable benchmarking and informed material selection in precision manufacturing. Full article

This study focuses on optimizing machining parameters in the micro-milling of AlSi10Mg aluminum alloy produced via the powder bed fusion additive manufacturing process. Although additive manufacturing enables complex geometries and minimizes material waste, challenges remain in reducing surface roughness and cutting forces during post-processing. Micro-milling experiments were conducted using spindle speeds up to 60,000 rpm, with varied feed rates and cutting depths. Cutting forces (Fx, Fy, and Fz) were measured using a Kistler-9119AA1 mini dynamometer, while surface roughness (Ra) was evaluated with a Nanovea-ST400 3D optical profilometer. Five advanced machine learning models, random forest regressor (RFR), gradient boosting regressor (GBR), LightGBM, CatBoost, and k-nearest neighbors (KNN), were employed to predict cutting forces and surface roughness, with CatBoost achieving the highest predictive accuracy (R² > 0.96). Among all models, CatBoost achieved the best predictive performance, with test R² values exceeding 0.96 for both force and Ra estimations. Experimental and ML-based results demonstrated that higher feed rates and depths of cut increased cutting forces, particularly in the Fx direction, while elevated spindle speeds reduced forces due to thermal softening. Surface roughness was minimized at lower feed rates and higher spindle speeds. The optimal machining conditions for achieving Ra < 1 µm were identified as ap = 50 µm, n = 30,000 rpm, and fz = 0.25 µm/tooth. This integrated approach supports precision machining of AM aluminum alloys. Full article

This study presents an integrated stiffness modeling and evaluation framework for an orthopedic surgical robot, aiming to enhance cutting accuracy and operational stability. A comprehensive stiffness model is developed, incorporating the stiffness of the end-effector, cutting tool, and force sensor. End-effector stiffness is computed using the virtual joint method based on the Jacobian matrix, enabling accurate analysis of stiffness distribution within the robot’s workspace. Joint stiffness is experimentally identified through laser tracker-based displacement measurements under controlled loads and calculated using a least-squares method. The results show displacement errors below 0.3 mm and joint stiffness estimation errors under 1.5%, with values more consistent and stable than those reported for typical surgical robots. Simulation studies reveal spatial variations in operational stiffness, identifying zones of low stiffness and excessive stiffness. Compared to prior studies where stiffness varied over 50%, the proposed model exhibits superior uniformity. Experimental validation confirms model fidelity, with prediction errors generally below 5%. Cutting experiments on porcine femurs demonstrate real-world applicability, achieving average stiffness prediction errors below 3%, and under 1% in key directions. The model supports stiffness-aware trajectory planning and control, reducing cutting deviation by up to 10% and improving workspace stiffness stability by 30%. This research offers a validated, high-accuracy approach to stiffness modeling for surgical robots, bridging the gap between simulation and clinical application, and providing a foundation for safer, more precise robotic orthopedic procedures. Full article

Cutting force is a critical indicator reflecting the interaction between the cutting tool and the workpiece in machining processes. Conventional measurement methods using dynamometers are accurate but costly and challenging for real-time applications. This study proposes a novel transfer learning-based method for estimating milling cutting forces using only spindle vibration signals without direct force sensors. The proposed approach consists of two stages: First, an autoencoder is trained with measured cutting force data to construct a latent feature space. Second, a target encoder aligns spindle vibration signals to this latent space, allowing the decoder to reconstruct estimated cutting forces. To reflect machining parameters into the learning model, the input dataset was constructed by integrating material type, cutting speed, and cutting direction as additional inputs into each model’s inputs. Experiments were conducted on Ti-6Al-4V and STS316L workpieces under various machining conditions. Under normal conditions, the proposed method achieved an average Pearson correlation coefficient (PCC) of 0.9213 ($F_x$) and 0.9072 ($F_y$). Under abnormal transient conditions, robust performance was maintained, with PCC values of 0.8573 ($F_x$) and 0.9202 ($F_y$). The results demonstrate that the proposed method can effectively monitor cutting forces and reflect changes across a variety of machining environments using only vibration signals. Full article

The present work intends to assess the impact of trochoidal toolpath rounding radius loop adjustments on surface roughness, nose radius wear, and resultant cutting force during end milling of AISI D3 steel. Twenty experimental trials have been performed utilizing a face-centered central composite design through a response surface approach. Artificial Neural Network (ANN) models were built to forecast outcomes, utilizing four distinct learning algorithms: the Batch Back Propagation Algorithm (BBP), Quick Propagation Algorithm (QP), Incremental Back Propagation Algorithm (IBP), and Levenberg–Marquardt Back Propagation Algorithm (LMBP). The efficacy of these models was evaluated using RMSE, revealing that the LMBP model yielded the lowest RMSE for surface roughness (Ra), nose radius wear, and resultant cutting force, hence demonstrating superior predictive capability within the trained dataset. Additionally, a Genetic Algorithm (GA) was employed to ascertain the optimal machining settings, revealing that the ideal parameters include a cutting speed of 85 m/min, a feed rate of 0.07 mm/tooth, and a rounding radius of 7 mm. Moreover, the detachment of the coating layer resulted in alterations to the tooltip cutting edge on the machined surface as the circular loop distance increased. The initial arc radius fluctuated by 33.82% owing to tooltip defects that alter the edge micro-geometry of machining. The measured and expected values of the surface roughness, resultant cutting force, and nose radius wear exhibited discrepancies of 6.49%, 4.26%, and 4.1%, respectively. The morphologies of the machined surfaces exhibited scratches along with laces, and side flow markings. The back surface of the chip structure appears rough and jagged due to the shearing action. Full article

This study investigates the machining performance of AZ91D magnesium alloy under three different cooling environments: dry, minimum quantity lubrication (MQL), and nano-reinforced MQL (NanoMQL) with multi-walled carbon nanotubes. Turning experiments were conducted on a CNC lathe with systematically varied cutting parameters, including cutting speed (150–450 m/min), feed rate (0.05–0.2 mm/rev), and depth of cut (0.5–2 mm). The machining performance was evaluated through cutting force measurements, surface roughness analysis, and tool wear examination using SEM. The results demonstrate that the NanoMQL environment significantly outperforms both dry and conventional MQL conditions, providing a 42.2% improvement in surface quality compared to dry machining and a 33.6% improvement over conventional MQL. Cutting forces were predominantly influenced by the depth of cut and the feed rate, while cutting speed showed variable effects. SEM analysis revealed that the NanoMQL environment substantially reduced built-up edge formation and flank wear, particularly under aggressive cutting conditions. The superior performance of the NanoMQL environment is attributed to the enhanced thermal conductivity and lubrication properties of carbon nanotubes, which form a protective tribofilm at the tool–workpiece interface. This study provides valuable insights for optimizing the machining parameters of AZ91D magnesium alloy in industrial applications, particularly where high surface quality and tool longevity are required. Full article