Track 1: Peer-Reviewed Research Schedule

The design of high-performance multi-stage geartrains requires balancing conflicting objectives, power density, reliability, and layout feasibility, within a highly constrained, irregular design space. Conventional multi-objective evolutionary algorithms often produce near-optimal solutions with high constraint violation rates, requiring manual post-processing. This paper presents a hybrid evolutionary-deterministic framework coupling NSGA-II global search with a nested Powell’s conjugate direction method for local tooth profile refinement. Macro-geometric parameters are explored globally, while addendum and dedendum coefficients are deterministically fine-tuned to satisfy industrial tolerances on contact ratio, interference reserve, and tip clearance. Applied to a double-stage automotive geartrain, the framework increases population feasibility from 24% to 69%, reduces profile-dependent constraint violations by over 60%, and yields Pareto-optimal solutions with safety factors 15–20% higher at equivalent transmission mass.

Bevel gear trains are used extensively today, from drive axles to robotics. However, applications with internal bevel gears stayed limited, mostly due to the lack of design tools and limitations in manufacturing. This work provides an overview of the applications with internal bevel gears today. Velocity diagram is presented to aid in the kinematic analysis of the bevel gearboxes with the rotating carriers. General kinematic and power-flow analysis method, based on the work of Del Pio, is used in modified form for the efficiency evaluation of the bevel gear boxes with the internal bevel gears. An overview of the available methods for design and manufacture of the internal bevel gears is provided.

ISO 21771, Cylindrical involute gears and gear pairs, details the geometry and nomenclature of cylindrical gearing. New versions of both part 1, Concepts and geometry, and part 2, Calculation and measurement of tooth thickness and backlash, have recently been released. This presentation will detail how these standards were developed, what new material is included, and the benefits of using them. The benefits to both the individuals who participate in standards development activities and to the companies who sponsor them will also be discussed.

Hybrid polymer–metal worm gears offer advantages in noise, backlash control, and lubrication-free operation, but their strength assessment remains challenging due to polymer material variability and the lack of standardized contact stress criteria. This study proposes a correlated nonlinear FEA framework for evaluating bending and contact stress in hybrid worm gears. Bending safety factors are assessed using ISO/TS 14521 and pulsator test allowable, while contact behavior is evaluated through comparison with ISO/TS 14521 mean Hertzian stress and contact mechanics theory. The framework further demonstrates that physics-based plausibility criteria can distinguish between candidate FEA configurations even when experimental correlation targets are limited. The resulting correlated model provides a physically grounded foundation for future investigations into polymer worm gear behavior.

This work was carried out by Rossi S.p.A. within the DNV Type-Approval Certification of the EP planetary gearbox series. The objective was to define validated load sharing factor values for approximately one hundred single-stage planetary units, including four-planet and three-planet configurations. The methodology combines analytical calculations, MFBD simulations, statistical analysis, and experimental validation. A Monte Carlo approach was applied to evaluate the influence of planet pin positioning errors using input distributions derived from production measurements and CMM data. Analytical and numerical models showed consistent trends, with the analytical method providing conservative results and the MFBD approach offering higher physical fidelity. Three characteristic load sharing factor families were identified and experimentally verified. The proposed methodology provides a practical framework for certification of complete planetary gearbox ranges.

The electrification of automotive mobility increases thermal and energy demands on drivetrains, highlighting power losses that remain insufficiently characterized. Among load independent sources, rotary lip seals can significantly contribute to losses across a wide speed range. However, existing global models for losses prediction show inconsistencies in their assumptions regarding torque dependence on shaft diameter and speed. To address this, an extensive experimental campaign was carried out on lip seals under various speeds, loads, and lubrication modes using a dedicated high speed test rig. The results highlight the strong influence of operating conditions on frictional losses and form the basis for the development of a proposed robust predictive model for lip seal power loss estimation.

Friction losses are a major source of inefficiency in drivetrain systems, yet simulation-based friction models are often developed separately for individual machine elements, limiting system-level analyses. This work presents a component-independent friction modeling approach based solely on material, geometry, and lubricant properties, avoiding component-specific parameterization. The physically based framework is consistently applicable to rolling bearings and gears, including deep groove ball bearings, angular contact ball bearings, cylindrical roller bearings, and spur and helical gears. Friction torque is calculated locally by considering pressure distribution, lubricant film thickness, and relative motion, while also accounting for seal losses, fluid drag, and unloaded contacts. The model captures variations in bearing and gear design, enabling systematic drivetrain studies. Experimental validation confirms strong predictive accuracy and supports efficient drivetrain optimization.

In this paper, an end-to-end simulation approach is presented to assess heat transport in industrial gearboxes using a coupled FEM–SPH multiphysics framework. The lubricant and the internal air are modeled using the meshfree smoothed particle hydrodynamics (SPH) method. Solid components such as gears and housings are represented by finite element methods (FEM) to capture heat conduction in the structures. The interaction between fluid and solid domains is handled through integral boundary formulations, allowing mesh-based surfaces to be consistently coupled with the particle-based fluid representation. In addition, a thermal wall model is employed to accurately resolve heat fluxes at fluid–solid interfaces. The proposed approach is demonstrated on an industrial gearbox to evaluate its thermal management concept.

Elastohydrodynamic (EHD) analysis of rolling element bearings hinges on accurate lubricant rheology. Publicly available rheological datasets for commercially available lubricants are limited, largely due to the specialized instrumentation needed for high-pressure viscometry. This study analyzed single-sourced mineral oils with the aim of quantifying correlations in the high-pressure rheological properties across seven ISO viscosity grades (22-220). A high-pressure falling body viscometer was used to collect viscometry data. The asymptotic iso-viscous pressure-viscosity coefficients (α^*) were calculated by integrating the Yasutomi function along isotherms. α^* model errors were quantified and compared to mineral oil specific α^* models published in ISO 1281, ISO 15144, AGMA 925, and AIR 5433. The influence of α^* model errors on ISO 281’s bearing-life modification factors was evaluated, highlighting implications for bearing life calculations.

The increasing use of fast-switching frequency converters in electric vehicles and wind turbines promotes parasitic currents through electrical machines and downstream gearboxes. These currents stress bearings and gears, potentially causing lubricant degradation, frosting, fluting, and premature component failure. To assess such stresses, machine elements must be interpreted as electrical systems. This study investigates the electromechanical behavior of ball bearings and spur gears on test rigs under varying operating conditions and with different lubricants. Broadband impedance measurements using a network analyzer were complemented by charging curve measurements of tribological contacts in bearings and gear meshes. The results demonstrate a strong dependence of the electrical equivalent circuit on tribological conditions. The approach supports lubricant selection, identifies critical current paths, and enables targeted measures to prevent premature failures.

A physics-informed neural network (PINN) framework for standards-compliant, accelerated gear drive design is presented. Tooth-root bending, flank-fatigue, specific sliding, meshing interference and gear-volume objectives are assembled into a differentiable weighted-residual loss, evaluated at every optimization step against the closed-form expressions of ISO 6336, DIN 3990 and VDI 2736, with no surrogate substituted for the standard equations. Polymer gear fatigue data from internal VDI 2736 Part 4 test campaigns underpin the thermoplastic material layer. A multi-start strategy with a feasibility gate returns reproducible, quasi-globally optimal, guaranteed-feasible results. Output geometry is a manufacturing-ready STEP file implementing the DIN 3960 rounded-rack trochoid. Two case studies, a helical thermoplastic pair (VDI 2736 Method C) and a steel pair (ISO 6336 Method B, 2019), demonstrate practical applicability of the framework.

Wind turbines operate under highly varying and unpredictable flow conditions that result in complex load patterns generated by the interplay of inertial, gravitational, and aerodynamic forces. In this work, we use a Physics-Informed Neural Network (PINN) to predict the loads at the blade root of several 2.8-MW wind turbines using operational SCADA data as input. The goal is to enhance wind turbine health monitoring by leveraging data from channels routinely available to turbine operators. The PINN is trained on high-frequency load data collected during a large field campaign and physics-based load estimates as a constraint to enhance accuracy and numerical stability. We will show how the PINN approach can provide accurate load estimates and discuss limitations and potential applications of the model output.

Increasing requirements for process stability, productivity, and noise, vibration, and harshness (NVH) performance in gear manufacturing demand analytical approaches beyond isolated process evaluation. Although large volumes of machine, process, and inspection data are available, these sources are typically analyzed separately, limiting the identification of robust relationships between manufacturing conditions, geometry deviations, and NVH-relevant waviness characteristics. This paper presents an integrated framework for end-to-end process intelligence based on the aggregation and standardization of machine signals, process parameters, and inspection data within a unified data model. Implemented in the ARGUS monitoring system, deployed across hundreds of machines, the approach enables cross-process correlation and pattern detection. A centralized cockpit application extends these capabilities by linking analytical insights to workflows and intelligent automation, supporting data-driven optimization of manufacturing processes.

The silent operation of electric vehicle motors turns gearbox noise more perceptible. Enabled by additive manufacturing, lattice structures can be tailored to introduce frequency-specific vibration inhibition. This study presents the application of a locally resonant lattice structure within a gear body to attenuate vibration. Lattice parameters were varied in a numerical model to assess the influence of local resonators on vibration attenuation. The most effective lattice-enhanced gear configuration was manufactured and experimentally evaluated through modal analysis. The final gear design achieved attenuation of up to 20 dB over a bandwidth of around 1000 Hz. Results confirm the capacity of lattice structures to reduce vibration propagation, contributing to targeted vibration mitigation and gear whine reduction in electric drivetrains.

Current correlational deep learning diagnostics function as “black boxes,” failing to resolve complex interactions between manufacturing deviations and gear dynamics. This paper introduces a “Causal Digital Twin” framework fusing metrology with telemetry for deterministic root cause analysis. The methodology integrates a Physics-Informed Neural Network (PINN)—estimating unmeasurable tribological states via Reynolds and Hertzian equations—with a Structural Causal Model (SCM) leveraging do-calculus (P(Y ∣ do(X)) to evaluate counterfactual scenarios. This framework mathematically isolates overlapping fault signatures (e.g., micropitting vs. debris denting) and maps micro-geometry tolerances to operational noise. Validation on an EV transmission test rig demonstrates an 85% reduction in diagnostic false positives compared to traditional CNNs, enabling zero-shot prognostics.

Main Author: Arian Sardari
RWTH Aachen University

The alignment of bearing bores in a gearcase has a significant effect on gear tooth alignment (lead) of the assembled gearbox. Gear form quality standards for parallel shaft gearing, such as ANSI/AGMA 2000-A88, ANSI/AGMA ISO 1328-1 B14, & AGMA 2015-1-A01 provide guidance on gear tooth alignment tolerances, but these standards do not consider the effect of gearcase bore alignment on gear tooth alignment.
 
This paper provides simplified formulas for determining the tolerances of gearcase bore alignment based on its effect on gear tooth alignment tolerances. Graphs are also included that contain gearcase bore alignment tolerances for various quality standards and gear quality levels. The graphs illustrate the significant effect of gearcase bore alignment on gear tooth alignment.

Gear transmissions are subject to increasing demands for load capacity, efficiency, and durability, making advanced surface finishing and tolerance reassessment important. This study investigates the geometric signature induced by Vibratory Finishing (VF) on gear flanks, focusing on form deviations and the VF-induced depression, quantified by profile form deviation (ffα) and morphological descriptors. Deviation mapping and surface topography measurements were combined to relate the observed geometry to the investigated process parameters: processing time and vibratory amplitude. Results indicate that the depression is not stochastic, but a parameter-dependent and controllable response. Treating this deviation as a predictable geometric outcome can support process optimization, dimensional control, and new quality criteria while preserving VF benefits, such as roughness reduction and possible durability improvement.

High-quality precision transmissions are critical in automotive, aerospace, and wind applications, where performance, reliability, and efficiency are paramount. Gear performance and NVH are strongly influenced by manufacturing-induced surface properties and tooth flank and root geometry. This work presents an integrated simulation framework that leverages gear manufacturing simulation to perform finite element analysis aimed at evaluating the effect of machining-induced flank and root deviations on tooth stresses. A CAD-based kinematic simulation of power skiving predicts flank and root topography, which is subsequently used in a finite element model to assess bending stresses under representative loading conditions. A parametric study demonstrates the sensitivity of root stresses to machining-induced geometric variations, offering a predictive tool for process-informed gear design and tighter integration between manufacturing simulation and structural analysis.

The design of carburized case depth is challenging, as it must account for hardened‑layer properties, loading conditions, and multiple failure modes. While ISO 6336‑5 provides guidance for macropitting, bending fatigue, and case crushing, no engineering rule exists for selecting case depth against Tooth Flank Fracture (TFF). This work models a cylinder‑to‑cylinder Hertzian contact and computes subsurface stresses analytically by combining Johnson’s mechanical stress model with Lang’s residual stress model. A hardness‑dependent endurance limit and the Dang Van multiaxial criterion are used to evaluate fatigue risk. Two case‑depth definitions are introduced: minimum and robust case depth. A numerical design‑of‑experiments approach yields two engineering rules, one identifying when carburizing is required, and one estimating the needed case depth, highlighting the key geometric, mechanical, and metallurgical parameters.

Case-hardening is widely used heat treatment process for achieving a high load carrying capacity in gears, with carburizing temperatures typically between 930°C and 1050°C. Elevated temperatures and prolonged process times, can promote coarse prior austenite grain sizes that may reduce the load carrying capacity. While strength reductions due to coarse grain have been reported for simple test specimens, a systematic investigation on case-hardened gears has not yet been done. This work quantifies the influence of coarse grain on the tooth root bending strength of 18CrNiMo7-6 spur gears (module 5 mm) using a pulsator test rig, complemented by metallographic characterization. Three potential countermeasures, mechanical cleaning via shot blasting, shot peening and an additional blind hardening cycle are evaluated, and recommendations are derived.

An innovative quenching process, DANTE Controlled Gas Quench (DCGQ), has been in development with U.S. Army DEVCOM AvMC since 2016 to minimize the distortion of gears and bearings made of high hardenability steels. During DCGQ, parts are quenched to a temperature above the martensite start temperature (Ms), followed by cooling through the martensitic transformation temperature range using a programmed “recipe” that controls the ambient gas temperature. Experiments have proved that the DCGQ process can effectively control the quench distortion and produce mechanical properties equivalent to the conventional HPGQ process for Pyrowear 675, Pyrowear 53, Ferrium C64, and M50NiL steels. An effort to compare bending fatigue performance between DCGQ and HPGQ is currently ongoing. This effort includes STBF testing using Pyrowear 53 gears.

Rolling-bearing accuracy standards define local geometric tolerances but do not directly describe how integrated support-level positional variations propagate into system-level functional shaft-position behavior. This study presents a deterministic realizability-governed positional transmission framework for bearing–shaft–housing systems. The methodology decomposes support behavior into Integrated Bearing Backlash (IB), Integrated Bearing Transmission Error (IBTE), and Integrated Bearing Position Error (IBPE), which subsequently propagates to the gear location as Functional Shaft Position Error (FSPE). Representative dual-support configurations are investigated to evaluate admissibility saturation, contributor redistribution, support asymmetry, and propagated orbit distortion behavior. The results demonstrate that admissibility-governed realizability transitions directly control blocked positional content formation, contributor dominance, and propagated FSPE amplification within rotating shaft systems.

Gear noise is a major NVH challenge in transmissions, caused by excitation at the gear mesh. While targeted flank modifications such as controlled waviness can improve excitation behavior, even small unintended periodic surface structures may significantly increase vibration and noise. Therefore, these structures are becoming increasingly important in design and quality assurance. Excitations are classified as integer or non-integer. Integer excitations occur at the gear mesh order and its harmonics, whereas non-integer excitations cannot be expressed as multiples of the mesh frequency. These are typically caused by manufacturing-induced deviations, such as grinding vibrations, spindle irregularities, or tool imperfections, leading to pitch errors. Since their effects are not yet fully understood, this study investigates the influence of non-integer excitation patterns using gears with periodic flank structures.

The presentation will review the activities and highlights of the MPMA BTC (Bearing Technical Committee) for the past 12 months. Interesting projects include a Bearing Failure Atlas for Wind Turbine Bearings to be published as a technical report. Working with ISO Working Group SC4/WG 8 to update ISO 15243, Rolling Bearings – Damage and failures – Terms, characteristics and causes. There have been two proposals within ISO where standards have been proposed that would include standardization of “internal” components. One was for the standardization of tapered rollers and the other for metric maintenance free plain bearings. These proposals are of concern as the internal properties are features that allows for differentiation manufacturer to manufacturer and as such should remain open.

The durability performance of gears in electric transmissions is strongly influenced by gear misalignment and process-induced variations such as surface finish, hardness gradients, and residual stresses. This study presents a high-fidelity simulation methodology to estimate the root durability of helical gears estimated by combining the gear misalignment and manufacturing process variations using the transmission 3d and multiaxial fatigue model FEMFAT. This approach enables a realistic representation of stress history for fatigue life prediction. Material properties and residual stress gradients are defined in FEMFAT along the gear tooth root from surface to core, to accurately represent depth-dependent material behavior. Furthermore, study provides insight into the relative contributions of system-level misalignment and process-induced variations on gear root durability life, supporting improved design robustness and manufacturing optimization.

Compared to metallic gears, plastic gears exhibit a limited power transmission capability at identical component size. To increase load-carrying capacity and service life, short-fiber-reinforced plastics are increasingly employed, whose mechanical behavior such as strength, stiffness, and fracture behavior depends significantly on the fiber morphology. The fiber morphology is largely determined by the injection molding process and the selected process parameters. This study pursues a holistic simulation strategy ranging from injection molding simulation to a multiphysical gear simulation. The multiphysical gear simulation couples friction and wear calculations. The parameters analyzed include, among others, tooth root stress, friction, and wear. The fiber morphology resulting from the injection molding simulation is considered element-wise in the multiphysical gear simulation using a coupled anisotropic, viscoelastic material model.

Wear is one of the most common damage mechanisms in plastic gears and directly affects transmission accuracy, NVH behavior, and service life. This study presents a combined experimental and numerical investigation of wear in injection-molded plastic gears. Six material combinations, including plastic–plastic and steel–plastic gear pairs, were tested under dry and grease-lubricated conditions at a controlled gear temperature of 80°C. Wear progression was monitored through periodic tooth thickness measurements up to 2×10⁶ load cycles and complemented by digital microscopy and nonlinear finite element simulations. The results revealed significant differences in wear performance between material combinations and showed that grease lubrication did not universally reduce wear. Furthermore, progressive wear was found to modify tooth stress distribution and promote crack initiation in heavily worn tooth regions.