Journal: Mechanism and Machine Theory

Abbreviation

Mech. Mach. Theory

Publisher

Elsevier

Journal Volumes

ISSN

0094-114X

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2018 - 201922020 - 202510
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Publications 1 - 10 of 12
  • Face-gear drive: Meshing efficiency assessment
    Item type: Journal Article
    Zschippang, Andreas H.; Weikert, Sascha; Wegener, Konrad (2022)
    Mechanism and Machine Theory
    The estimation of the losses due to friction of meshing tooth flanks is an important point in the design of gear drives. In addition to other design factors, the efficiency of a gear stage is an important criterion for selecting the type of gear and the number of gear stages in order to achieve a desired gear ratio. This manuscript deals with various approaches with which the friction losses that result from the meshing flanks of a face-gear stage can be estimated. In addition, the influence of the directions of sliding and rolling speed is investigated, which is particularly important for gear stages with axle offset, since the two velocities are not collinear. The calculation method is validated for low speeds by means of experiments on a transmission test bench.
  • Synthesis method for the design of variable stiffness components using prestressed singular elastic systems
    Item type: Journal Article
    Boehler, Quentin; Vedrines, Marc; Abdelaziz, Salih; et al. (2018)
    Mechanism and Machine Theory
  • Fast mobility analysis for generative design applications through a real vector space representation of mobilities
    Item type: Journal Article
    Lang, Guilain; Rouvinet, Julien (2024)
    Mechanism and Machine Theory
    During the early stages of design, mechanisms are commonly modeled as perfect joints assembled with infinitely rigid bodies. This representation enables the prediction of the system's mobilities through a mobility analysis. However, traditional mobility analysis tools can be computationally expensive or lack critical information, such as the type or direction of mobilities. It hinders the generation of topology and configuration through generative design schemes. In this paper, we propose an alternative approach to mobility analysis based on a real vector space representation of mobilities. Our method provides relevant information for early design steps while being computationally effective through a novel formulation of series and parallel assembly topological operations. A benchmark on four selected use cases highlights an acceleration of 3 to 4 orders of magnitude compared to traditional approaches. Additionally, design rules on the joints’ positions can be automatically generated with our approach. It enables the automation of the complete design process, including topology and configuration. As such, we provide guidelines to develop a generative design scheme dedicated to the synthesis of guiding mechanisms.
  • Model order reduction for real-time hybrid simulation: Comparing polynomial chaos expansion and neural network methods
    Item type: Journal Article
    Tsokanas, Nikolaos; Simpson, Thomas; Pastorino, Roland; et al. (2022)
    Mechanism and Machine Theory
    Hybrid simulation is used to investigate the dynamic response of a system by combining numerical and physical substructures. To ensure high fidelity results, it is necessary to conduct hybrid simulation in real-time. One challenge in real-time hybrid simulation originates from high-dimensional nonlinear numerical substructures and, in particular, from the computational cost linked to the accurate computation of their dynamic responses. When the computation takes longer than the actual simulation time, time delays are introduced distorting the simulation timescale. In such cases, the only viable solution for performing hybrid simulation in real-time is to reduce the order of such complex numerical substructures. In this study, a model order reduction framework is proposed for real-time hybrid simulation, based on polynomial chaos expansion and feedforward neural networks. A parametric case study is used to validate the framework. Selected numerical substructures are substituted with their respective reduced-order models. To determine the framework’s robustness, parameter sets are defined covering the design space of interest. Comparisons between the full- and reduced-order hybrid model response are delivered. The attained results demonstrate the performance of the proposed framework.
  • Face-gear drive: Assessment of load sharing, transmission characteristics and root stress based on a quasi-static analysis
    Item type: Journal Article
    Zschippang, Andreas; Lanz, Natanael; Küçük, Kubilay Ahmet; et al. (2020)
    Mechanism and Machine Theory ~ Mechanism and Machine Theory
  • Adaptive model predictive control for actuation dynamics compensation in real-time hybrid simulation
    Item type: Journal Article
    Tsokanas, Nikolaos; Pastorino, Roland; Stojadinovic, Bozidar (2022)
    Mechanism and Machine Theory
    Hybrid simulation is used to obtain the dynamic response of a system whose components consist of physical and numerical substructures. The coupling of these substructures is achieved by actuation systems, which are commanded in closed-loop control setting. To ensure high fidelity of such hybrid simulations, performing them in real-time is necessary. However, real-time hybrid simulation poses challenges since the inherent dynamics of the actuation system introduce time delays, thus modifying the dynamic response of the investigated system. Therefore, a tracking controller is required to adequately compensate for such time delays. In this study, a novel tracking controller is proposed for dynamics compensation in real-time hybrid simulations. It is based on adaptive model predictive control, a linear time-varying Kalman filter, and a real-time model identification algorithm. Within the latter, auto-regressive exogenous polynomial models are identified in real-time to estimate the changing plant dynamics. A parametric virtual case study, encompassing a virtual motorcycle, is used to validate the performance and robustness of the proposed controller. Results demonstrate the effectiveness of the proposed controller for real-time hybrid simulations.
  • A fast, non-implicit SDOF model for spur gear dynamics
    Item type: Journal Article
    Gkimisis, L.; Vasileiou, George; Sakaridis, Emmanouil; et al. (2021)
    Mechanism and Machine Theory
    Modeling and simulation of the nonlinear dynamic response typical in gear transmissions usually require extensive input from tooth contact analysis combined with data derived from numerical techniques that in turn comprise a time and resource-consuming procedure. In this work, an efficient SDOF model that captures meshing nonlinearities in a non-implicit manner is presented. An extensive geometric analysis designates the underlying physical mechanisms prevailing in tooth meshing enabling incorporation of the effects of backlash, varying mesh stiffness and corner contact. By this analysis, an accurate repositioning method is proposed for involute teeth contact reversal, while a general approximation function for gear pair mesh stiffness including load dependence is formulated and successfully fitted to analytical data. Consequently, distinction between single and double tooth contact is captured through a modified parametric s-curve, including the effect of corner contact. A SDOF dynamical model is formulated for a given pinion rotational velocity and solved numerically. Both static and dynamic results are compared to data available in the literature, showing high agreement with more complex methods, while maintaining the advantages of minimum required pre-calculations and low computational cost. © 2021 Elsevier
  • Symmetry preserving neural network models for spur gear static transmission error curves
    Item type: Journal Article
    Sakaridis, Emmanouil; Kalligeros, Christos; Papalexis, Christos; et al. (2023)
    Mechanism and Machine Theory
    This paper proposes the use of neural networks to predict static transmission error (STE) curves for spur gears. Initially, a dataset spanning a parametric space of 17 parameters and comprising 20000 STE curves is created with a physics based solver, utilizing a dimensionless formulation. This data is used to train and evaluate different neural network architectures, which incorporate the symmetries of periodicity and input–output interchangeability. Results show that a small fully connected network with 3 hidden layers of 60 neurons can capture the highly non-linear STE response accurately, achieving a mean absolute percentage error of 0.075% on previously unseen data, while the incorporation of symmetries noticeably improves performance. The highest errors are below 1% and occur in border regions of the dataset, where training data is sparse. These results indicate that neural network models can faithfully reproduce the predictions of traditional, non-linear solvers and are thus a promising approach for modeling the static response of spur gears over extended parametric spaces. Finally, indicative dynamic simulations investigate the extension of these results to the dynamic regime.
  • Neural network surrogates for finite element models in loaded tooth contact analysis of polymeric gears
    Item type: Journal Article
    Papalexis, Christos; Sakaridis, Emmanouil; Terpos, Klearchos; et al. (2025)
    Mechanism and Machine Theory
    This paper introduces a neural network (NN) model for loaded tooth contact analysis in polymeric gears. The NN model is trained on 10,000 finite element (FE) simulations, which utilize a large deformation framework and span a 17-dimensional input space, including geometry, material and load related parameters. Combining a parametric meshing scheme and a robust implicit solver implementation enables the automated extraction of static transmission error (STE) curves. Leveraging the symmetry of periodicity and a conversion from a dimensional to a dimensionless parametric space, an approximately 9,000-parameter fully connected neural network achieves a mean absolute percentage error of 0.49% on a test set of 1,000 previously unseen STE curves. This error represents an order of magnitude more accurate replication of FE results than typical, analytical, physics-based solvers, with the effects of corner contact being predicted more faithfully. The computational cost of the NN model remains comparable to simple, linearly approximated formulas, indicating that data-driven approaches can be both more accurate and less computationally intensive than physics-based surrogates to FE simulations for large deformations.
  • Face-gear drive: Simulation of shaping as manufacturing process of face-gears
    Item type: Journal Article
    Zschippang, H. Andreas; Weikert, Sascha; Wegener, Konrad (2022)
    Mechanism and Machine Theory
    Gear shaping is the most important process for manufacturing internal gears. Even if this method is also suitable for the production of spur gears, these can be produced more effectively using hobbing. Gear shaping is also interesting for manufacturing of face-gears. Since the shaper cutter is quite simple compared to a hob and the tool costs are therefore relatively low, this method is particularly suitable for small series production. This manuscript deals with the simulation of gear shaping of face-gears with the aim of determining suitable process parameters such as radial and rotary infeed and thus achieving a stable manufacturing process as fast as possible and with low reject rate. The cutting forces are determined on the basis of the theoretical uncut chip thickness, whereby the tool deflection can be calculated using these, provided that the stiffness of the tool, machine tool and component holder is known. In addition, the tool wear can be estimated using a suitable wear model. Experimentally determined cutting forces during the shaping of a face-gear are used to validate the simulation model.
Publications 1 - 10 of 12