Search Results (108)

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

Reducing gear noise in electric vehicle (EV) drivetrains is crucial due to the absence of internal combustion engine noise, making even minor acoustic disturbances noticeable. Manufacturing parameters significantly influence gear-generated noise, yet traditional analytical methods often fail to predict these complex relationships accurately. This research addresses this gap by introducing a data-driven approach using machine learning (ML) to predict gear noise levels from manufacturing and sensor-derived data. The presented methodology encompasses systematic data collection from various production stages—including soft and hard machining, heat treatment, honing, rolling tests, and end-of-line (EOL) acoustic measurements. Predictive models employing Random Forest, Gradient Boosting (XGBoost), and Neural Network algorithms were developed and compared to traditional statistical approaches. The analysis identified critical manufacturing parameters, such as surface waviness, profile errors, and tooth geometry deviations, significantly influencing noise generation. Advanced ML models, specifically Random Forest, XGBoost, and deep neural networks, demonstrated superior prediction accuracy, providing early-stage identification of gear units likely to exceed acceptable noise thresholds. Integrating these data-driven models into manufacturing processes enables early detection of potential noise issues, reduces quality assurance costs, and supports sustainable manufacturing by minimizing prototype production and resource consumption. This research enhances the understanding of gear noise formation and offers practical solutions for real-time quality assurance. Full article

Study design: Trends in the utilization of Mandibulo-Maxillary Fixation (MMF) are shifting nowadays from tooth-borne devices over specialized screws to hybrid MMF devices. Hybrid MMF devices come in self-made Erich arch bar modifications and commercial hybrid MMF systems (CHMMFSs). Objective: We survey the available technical/clinical data. Hypothetically, the risk of tooth root damage by transalveolar screws is diminished by a targeting function of the screw holes/slots. Methods: We utilize a literature review and graphic displays to disclose parallels and dissimilarities in design and functionality with an in-depth look at the targeting properties. Results: Self-made hybrid arch bars have limitations to meet low-risk interradicular screw insertion sites. Technical/clinical information on CHMMFSs is unevenly distributed in favor of the SMARTLock System: positive outcome variables are increased speed of application/removal, the possibility to eliminate wiring and stick injuries and screw fixation with standoff of the embodiment along the attached gingiva. Inferred from the SMARTLock System, all four CHMMFs possess potential to effectively prevent tooth root injuries but are subject to their design features and targeting with the screw-receiving holes. The height profile and geometry shape of a CHMMFS may restrict three-dimensional spatial orientation and reach during placement. To bridge between interradicular spaces and tooth equators, where hooks or tie-up-cleats for intermaxillary cerclages should be ideally positioned under biomechanical aspects, can be problematic. The movability of their screw-receiving holes according to all six degrees of freedom differs. Conclusion: CHMMFSs allow simple immobilization of facial fractures involving dental occlusion. The performance in avoiding tooth root damage is a matter of design subtleties. Full article

Gear whine noise is governed not only by intentional microgeometry modifications but also by unavoidable pitch (indexing) deviation. This study presents a workflow that couples a tooth-resolved surface scan with a calibrated pitch-deviation table, both imported into a multibody dynamics (MBD) model built in MSC Adams View. Three operating scenarios were evaluated—ideal geometry, measured microgeometry without pitch error, and measured microgeometry with pitch error—at a nominal speed of 1000 r min⁻¹. Time domain analysis shows that integrating the pitch table increases the mean transmission error (TE) by almost an order of magnitude and introduces a distinct 16.66 Hz shaft order tone. When the measured tooth topologies are added, peak-to-peak TE nearly doubles, revealing a non-linear interaction between spacing deviation and local flank shape. Frequency domain results reproduce the expected mesh-frequency side bands, validating the mapping of the pitch table into the solver. The combined method therefore provides a more faithful digital twin for predicting tonal noise and demonstrates why indexing tolerances must be considered alongside profile relief during gear design optimization. Full article

This study analyzes technological system stability during side milling by evaluating the influence of two critical parameters: the radial depth of cut (A_e) and feed per tooth (f_z). The experiment was conducted on prismatic samples with stepped geometries to measure deformation at various levels of Ae and f. Regression analysis showed a significant influence of both factors on deformation, with Ae having a stronger effect. The model explains a high level of the variance, confirming the reliability of the experimental data. The results provide guidance for optimizing parameters to improve stability and reduce dimensional deviations. Full article

This paper presents an analysis of the abrasive wear influence on the tooth flank geometry of plastic gear wheels, emphasizing the contribution of tooth stiffness to the observed changes. The study examined gear wheels made from polylactic acid (PLA) with wall thicknesses of 0.6 mm, 1.0 mm and 2.4 mm, manufactured using FDM technology. A standard layer height of 0.2 mm was chosen as it offers a balance between good precision and reasonable printing times. The PLA gear wheels were tested for wear in a meshing configuration with a metallic reference gear. The results indicate that wear intensity increases as tooth stiffness decreases, suggesting an inverse proportionality between abrasive wear and tooth stiffness. In all tested cases, the tooth tip was more affected by abrasive wear compared to the rest of the profile. The analysis establishes that sliding velocity has the greatest influence on the abrasive wear characteristics of the evaluated gears. Based on experimental findings, a mathematical model was developed for simulating abrasive wear in plastic gears, with scalability across various manufacturing technologies. For PLA gears, both experimental and simulated data confirm that full tooth infill is essential for functional durability. Full article

Background: While dental implants have become a reliable solution for tooth loss, their long-term success is increasingly challenged by biological and technical complications such as impact fracture and peri-implantitis. These complications significantly impact implant longevity and patient satisfaction. Aim: This consensus conference aimed to identify and standardize clinical guidelines to prevent implant fractures and peri-implant diseases based on current evidence and expert opinions. Methods: A panel of 10 expert clinicians and researchers in prosthodontics participated in the Osstem Global Consensus Meeting. This paper focuses on the prosthetic division. A structured literature review was conducted, and evidence was synthesized to formulate consensus-based clinical recommendations. Participants answered structured questions and discussed discrepancies to achieve consensus. Results: The panel reached consensus on several key prosthetic risk factors, including (1) the role of biomechanical overload in implant fracture, (2) the impact of emergence profile design on peri-implant tissue stability, (3) the influence of implant positioning and connection geometry on marginal bone loss, and (4) the importance of occlusal scheme and restorative material selection, particularly in high-risk patients such as bruxers. Guidelines to prevent implant fracture and peri-implantitis were developed, addressing these factors with practical preventive strategies. Conclusions: Despite the limitations of narrative methodology and reliance on retrospective data and expert opinion, this consensus provides clinically relevant guidelines to aid in the prevention of mechanical failures and peri-implant diseases. The recommendations emphasize prosthetically driven planning, individualized risk assessment, and early intervention to support long-term implant success. 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

An environmentally friendly alternative to phosphate-based lubrication was studied through the lateral cold extrusion forging of internal gears using pulsating motion. A die set with a removable punch enabled a detailed observation of relubrication, forming load, material flow, and gear geometry. Pulsating motion with liquid lubricant significantly reduced the forming load during punch penetration, while no such effect was observed under dry conditions. Even when the number of pulses (n) was set to 1, relubrication occurred, and a comparable load reduction to that of n = 3 was achieved, shortening the forming time. When n = 3, pulsating motion contributed to increased gear height and reduced separated burr formation; however, it also caused slightly incomplete tooth filling, which may be undesirable for precision applications. Varying the pulse start position from 5.50 mm to 13.30 mm influenced forming load and material flow, further affecting gear geometry. During punch extraction, the presence of liquid lubricant reduced the load and suppressed material displacement, while dry conditions led to higher extraction loads and more deformation. Full article

Labyrinth seals, extensively used in aerospace and turbomachinery as non-contact sealing devices, undergo accelerated wear and enhanced leakage due to repeated rub-impact between rotating shafts and sealing rings. To address the problem of increased leakage under rub-impact conditions, this research integrates experimental and numerical methods to investigate the deformation mechanisms and leakage characteristics of thermoplastic labyrinth seals. A custom designed rub-impact test rig was constructed to measure dynamic forces and validate finite element analysis (FEA) models with an error of 5.1% in predicting tooth height under mild interference (0.25 mm). Computational fluid dynamics (CFD) simulations further demonstrated that thermoplastic materials, such as PAI and PEEK, displayed superior resilience (with rebound ratios of 57% and 70.3%, respectively). Their post-impact clearances were 4.8–18.3% smaller than those of PTFE and F500. Leakage rates were predominantly correlated with interference, causing a substantial increase compared to the original state; at 0.25 mm interference (reverse flow), increases ranged from 151% (PAI) to 217% (PTFE), highlighting material-dependent performance degradation. Meanwhile, tooth orientation modulated leakage by 0.5–3% through the vena contracta effect. Based on these insights, two optimized inclined-tooth geometries were designed, reducing leakage by 28.2% (Opt1) and 28.1% (Opt2) under rub-impact. These findings contribute to the development of high-performance labyrinth seals suitable for extreme operational environments. 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

Optical diagnostic techniques represent an attractive complement to conventional pulp vitality tests, as they can provide direct information about the vascular status of the pulp. However, the complex, multi-layered structure of a tooth significantly influences the detected signal and, ultimately, the diagnostic decision. Despite this, the impact of the various dental components on light propagation within the tooth, particularly in the context of diagnostic applications, remains insufficiently studied. To help bridge this gap and potentially enhance diagnostic accuracy, this study employs digital optical twins based on the Monte Carlo method. Using incisor and molar models as examples, the influence of tooth and pulp geometry, blood concentration, and pulp composition, such as the possible presence of pus, on spectrally resolved transmission signals is demonstrated. Furthermore, it is shown that gingival blood absorption can significantly overlay the pulpal measurement signal, posing a substantial risk of misdiagnosis. Strategies such as shifting the illumination and detection axes, as well as time-gated detection, are explored as potential approaches to suppress interfering signals, particularly those originating from the gingiva. Full article

This paper presents a comprehensive methodology to enable the optimization of an automotive electric axle to reduce noise emissions and improve load distribution. The proposed method consists of the application of two sequential optimization procedures. The first one focuses on the gears’ macro-geometry, based on an objective function that combines the contact ratio, power loss, and center distance. The second one optimizes the micro-geometry of the teeth to reduce the sound pressure generated by tooth impacts. Mechanical stress limits are considered as a constraint in the optimization process. Shafts, joints, and the electric motor are analyzed, taking into account their deformation that influences the dynamics of the entire system. The results of the proposed procedure are verified through experimental measurements and the comparison can be considered successful. Full article

This study investigates the effectiveness of trochoidal (adaptive) milling in machining Glass Fiber Reinforced Polymer (GFRP), emphasizing its potential advantages over conventional milling. Six coated solid carbide end mills, each with distinct geometries, were evaluated under identical conditions to assess the cutting forces, surface quality, dimensional accuracy, burr formation, chip size distribution, and tool wear. Trochoidal milling demonstrated shorter cycle times—up to 23% faster—and higher material removal rates (MRRs), while conventional milling provided superior dimensional control and smoother surfaces in certain fiber-sensitive regions. A four-tooth cutter with a low helix angle (10°) and aluminum-oxide coating delivered the best overall performance, balancing minimal tool wear with high-quality finishes (arithmetic mean roughness, Ra, as low as 1.36 μm). The results indicate that although conventional milling can exhibit a 25%-lower RMS cutting force, its peak forces and extended machining times may limit the throughput. Conversely, trochoidal milling, when coupled with an appropriately robust tool, effectively manages the cutting forces, improves the surface quality, and reduces the machining time. Most chips produced were less than 11 μm in size, highlighting the need for suitable dust extraction. Notably, a hybrid approach—trochoidal roughing followed by conventional finishing—offers a promising method for achieving both efficient material removal and enhanced dimensional accuracy in GFRP components. Full article

This article provides a comparative analysis of the dimensional accuracy and post-surface characteristics of gears produced by the 3D printing technique Direct Metal Laser Sintering (DMLS) from 1.2709 steel immediately after printing and after grinding and grinding treatment. The following tests were performed on the fabricated samples: metallography, hardness measurement, self-stress, surface roughness, and the gears’ shape were dimensioned and measured. The results show that post-processing influences the distribution of residual stress and the printed model’s hardness. The results show that heat treatment results in clear directionality marks and micropores, increasing the material’s hardness to 54.3 HRC ± 0.6 HRC, indicating effective strengthening. Grinding significantly improved the holes’ accuracy, changed the compressive intrinsic stresses to a tensile state, and reduced radial runout, improving gear geometries. In addition, it was noted that different results were obtained for roughness parameters depending on the gear tooth tested. Full article