Department of Mechanical Engineering

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The present research studies the vibration and bifurcation analysis of a spinning rotor-disk-bearing system to carefully scrutinize the dynamic stability under extrinsic mass unbalance and pulsating axial load. The shaft is flexible and taken into account the geometrical nonlinearities due to large elastic deformation in bending. The rotor is supported by flexible bearings, which are modeled as an equivalent spring-damper system having linear and nonlinear stiffness elements. Equation of motion of the rotating system, which includes flexible shaft, rigid disk, and flexible bearing, is derived using extended Hamilton principle with the assumption of the Euler's beam theory. We studied initially the modal analysis to determine the modal parameters, i.e. natural frequency and mode shapes prior to the investigation of the dynamics of the system. Further, we developed the bifurcation diagram for steady-state solutions and to study the subsequent dynamic stability and verify with the findings solved numerically. The interactive behavior among the nonlinear shaft-bearing, axial load and an unbalance has been analyzed. Numerical simulation tools, i.e. frequency–response characteristics, time history, phase trajectories, and Poincaré map have been used to highlight the presence of nonlinear phenomena and its important role towards evaluating the system dynamics and subsequent stability. This current research showed that flexible bearings stabilize the system as a result of increasing the restoring force. Analyzing the bifurcation diagrams of pulsating axial load, we found that the system exhibits complex phenomena as multiple but stable periodic orbits leading to period doubling. The present system is highly vulnerable to catastrophic failure due to the S–N and Pitchfork bifurcation. The present research enables the notability of axial load and mechanical unbalance on the overall system behavior and stability in real working conditions. © IMechE 2020.

World University root node

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

Dynamic stability and bifurcation phenomena of an axially loaded flexible shaft-disk system supported by flexible bearing

AISI 304L stainless steel is most commonly used for spent nuclear fuel management; however, the welded joints of this steel are susceptible to intergranular stress corrosion cracking (IGSCC) under the influence of low-temperature sensitization. In the present research, the temperature history of two different groove designs (conventional and narrow groove) has been analyzed to ascertain the propensity of the weld zone to intergranular corrosion (IGC). 3D finite element models (FEMs) have been developed to retrieve the nodal thermal history and predict the region susceptible to IGSCC. The FEM results predicted a lower duration of exposure to the IGC temperature range for narrow groove design as compared to conventional design. The lower duration of exposure exhibits a lower propensity to chromium carbide precipitation and the tendency to IGSCC. The FEM analysis also has been used to observe the difference in the size of the region susceptible to IGSCC in the heat-affected zone of the respective weld designs. The predicted results obtained from the numerical analysis were validated by comparing the chromium carbide precipitation for both the groove designs. © 2020, Wroclaw University of Science and Technology.

Archives of Civil and Mechanical Engineering

Finite element investigation of IGSCC-prone zone in AISI 304L multipass groove welds

In present work, two nuclear grade steel (P91, P92) are joined using the arc welding process. The welded joints were subjected to the heat treatment in order to restore the mechanical properties and overcome the heterogeneity across the joints. The weldments were studied for microstructure evolution and mechanical behavior under different condition of heat treatment. The variation in mechanical behavior obtained for the welded joints were tried to relate the microstructtunl evolution. After the normalizing based heat treatment, homogeneity with negligible delta ferrite across the welded joints was observed.

ARCHIVES OF METALLURGY AND MATERIALS

A COMPARATIVE STUDY OF TWO NUCLEAR STEEL GRADES WELDED JOINTS

This article presents a method for quantitative estimation of the mass of an entity attached to the surface of an electrostatically actuated clamped-free microbeam implemented as a mass sensor. For this investigation, the microbeam is modeled as a Euler–Bernoulli beam taking into account the effects of electrostatic nonlinearity, viscous energy dissipation together with the effect of the fringing field capacitance. A modal superposition technique is used to simulate the dynamic response of the microcantilever. The dynamic pull-in voltage of microcantilever is evaluated using a developed modal. The present dynamic pull-in voltage results are compared with the existing results and on the basis of comparison, the accuracy of the developed model is ascertained. For mass quantification, firstly, an input shaping technique is employed to stabilize the microbeam at the specified position and then voltage perturbation is induced to make the system sensitive to inertial change. A parasitic mass expressed as a fraction of the total mass of the beam is then added resulting in an increase in the amplitude of vibration and hence an alteration in the capacitance. An empirical relation between the added mass and the change in capacitance is proposed. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Microsystem Technologies

A novel capacitive mass sensor using an open-loop controlled microcantilever

In the last two decades, the lattice Boltzmann method (LBM) has emerged as a promising tool for simulating complex fluid flows. The method uses a uniform mesh spacing throughout the domain. Near solid boundaries, the resolution required to capture steeper gradients in flow variables can cause massive computational load due to the same mesh resolution being used everywhere else in the domain. This limitation is circumvented by using multi-block LBM in which finer mesh is used near the solid-fluid interface, whereas coarser mesh is used away from the solid boundaries. Consequently, multi-block approach requires exchange of data between the coarse and the fine grid at the interface at each time step. For two-dimensional flow problems like flow past a cylinder and flow in a lid driven cavity, the transfer of data between coarse and fine grid by using univariate splines has already been developed successfully. However, an efficient and accurate interpolation scheme in three-dimensional fluid flow simulation using multi-block LBM has not been demonstrated so far. This work employs a bivariate version of splines and devises an efficient algorithm for the solution of unknown coefficients. At the interface of the coarse and fine grid blocks, bicubic splines are constructed using the data on the coarse grid. The bicubic splines are subject to appropriate continuity conditions across adjacent splines and suitable boundary conditions. The resulting coupled equations in terms of the unknown coefficients are solved using a direct method without matrix inversion. The validation of the use of bicubic splines is done by solving flows around a three-dimensional cylinder and sphere with the help of multi-block LBM. Subsequently, the bicubic spline interpolation is employed for moving boundary simulation of a three-dimensional rigid hovering wing. The coefficients of drag and lift computed using multiblock LBM based on bicubic spline interpolation shows considerable reduction in noise compared to available results that use uniform mesh. © 2020, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

AIAA Scitech 2020 Forum

Improving resolution of three-dimensional lattice boltzmann simulations using bicubic spline interpolation for moving boundaries

Dielectric elastomers (DEs) are a class of highly deformable electro-active polymers (EAPs) employed for electromechanical transduction technology. Imparting anisotropy to the material behaviour of DEs by adding soft fibres has become promising for suppressing the electromechanical instability besides enhancing the actuation performance. Practically dielectric elastomer based devices undergoes transient motion, making dynamic analysis a crucial aspect for the design of such actuators. In this work, we analyse the electro-viscoelastic response of a transversely isotropic dielectric elastomer subjected to suddenly applied constant and harmonic electric loads. A material model of anisotropic viscoelastic dielectric elastomers is presented by additively decomposing the isotropic free energy density corresponding to DE matrix and anisotropic free energy density corresponding to fibre into equilibrium and viscous parts. Based on the developed material model, equations governing the dynamic motion of the actuator is derived using the Euler-Lagrange equation for non-conservative system. Evolution equations consistent with the laws of thermodynamics are presented to obtain the isotropic and anisotropic viscous stretches. Developed dynamic model is utilized for describing the dynamic response, instability analysis, periodicity, and resonance properties of a dielectric elastomer actuator for different values of viscoelasticity and anisotropy parameters. Phase portrait and Poincarè maps are presented to analyse the periodicity of the nonlinear oscillations of the actuator. The results reveal that viscoelasticity delays the onset of dynamic pull-in instability. Simultaneously, a quasi-periodic to periodic transition is also achieved. The results presented can help in efficient and robust designing of dielectric elastomer based actuators subjected to dynamic loading. © 2020 IOP Publishing Ltd.

Smart Materials and Structures

Nonlinear dynamic analysis of aniso-visco-hyperelastic dielectric elastomer actuators

Development and proposition of a numerical model to capture the shrinkage induced flow during directional solidification of a pure substance in a bottom cooled cavity are carried out. A novel numerical scheme involving fixed grid-based volume fraction updating is proposed to track the solid-liquid interface, considering the inclusion of the shrinkage effect. Directional solidification in bottom cooled orientation is of particular interest since shrinkage and buoyancy effects oppose each other. The results from the proposed numerical model indicated the existence of an unprecedented flow reversal phenomenon during the progression of the solidification process, caused by the opposing nature of shrinkage and buoyancy effects. The flow reversal phenomena predicted by the numerical model are validated by conducting experiments involving directional solidification of coconut oil in a bottom cooled cavity. Qualitative and quantitative measurements of the velocity field and interface growth are obtained using the particle image velocimetry technique and compared with three dimensional numerical results. Once the flow reversal phenomena are established through numerical and experimental evidences, case studies are performed, considering varying material properties, cold boundary temperatures, initial temperatures of the melt, and cavity heights to find the effect of each of these parameters on flow reversal phenomena. The parametric study also allowed us to check the robustness and consistency of the proposed model. The proposed model will serve as an important milestone toward the development of numerical models for capturing macro-scale shrinkage defects and prediction of composition heterogeneity during directional alloy solidification. © 2020 Author(s).

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Physics of Fluids

Shrinkage induced flow during directional solidification of pure substance in a bottom cooled cavity: A study on flow reversal phenomena

Cutting force is the fundamental parameter determining the productivity and quality of the milling operation. The development of a generic cutting force model for end milling operation necessitates a large number of experiments. The experimental data contains multiple outliers due to noise and process disturbances lowering prediction accuracy of the model. This paper presents a novel approach combining the mechanistic model and the supervised neural network (NN) model to predict instantaneous cutting force variation during the end milling operation. The approach proposes training of an NN model using datasets generated from the mechanistic force model instead of using experimental data. The methodology generates a large number of datasets for the training of an NN model without conducting rigorous experimentation. A set of NN architectures were developed, and an appropriate network was derived by comparing performance parameters. A series of end milling experiments were conducted to examine the efficacy of the proposed approach in predicting cutting forces over a wide range of cutting conditions. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Journal of Intelligent Manufacturing

Machine learning-based instantaneous cutting force model for end milling operation

The identification of appropriate empirical relationships between cutting force coefficients and the uncut chip area is crucial for the successful implementation of mechanistic models in estimating cutting forces for flat-end milling operation. The derivation of empirical relationships necessitates machining experiments to record cutting force components under diverse conditions. The experimental data collected from force sensors (mainly dynamometers) contains outliers and noise that deteriorate the goodness of fit and thereby prediction accuracy of the model. This paper presents an application of the Gaussian approach to refine experimentally measured force data by systematically eliminating outliers and noise, followed by the use of pre-processed data to derive empirical relationships. The proposed methodology is implemented in the form of a computational tool to eliminate irrational data points automatically and obtain meaningful cutting force coefficients relationships using regression models. The outcomes of the study are substantiated further by conducting a set of experiments over a wide range of cutting conditions. The root mean square error (RMSE) of measured and predicted cutting forces is estimated for models with and without a data refining approach, which showed marked improvement in the prediction accuracy. Based on the outcomes of the present study, it can be concluded that the prediction ability of the mechanistic force model can be improved considerably with the augmentation of the Gaussian approach. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

International Journal of Advanced Manufacturing Technology

Gaussian approach–based cutting force coefficient identification for flat-end milling operation

The layered fabrication approach induces directional anisotropy and impacts mechanical strength of FDM components significantly. This paper proposes generalized machine learning based parameter optimization framework to determine optimal build orientation for FDM components. The algorithm determines ideal build orientation by maximizing the minimum Factor of Safety (FoS) for the component under prescribed loading conditions ensuring its even distribution. An Artificial Neural Network (ANN) coupled with Bayesian algorithm has been employed to accelerate the optimization process. The algorithm begins with an initial sample data collected using brute force approach; uses single layered ANN for approximation and optimization is achieved using Bayesian algorithm. A series of computational experiments considering five different test components has been devised to evaluate the performance and efficacy of the proposed algorithm. These experiments demonstrated that the proposed algorithm can determine the optimum building orientation effectively with certain limitations. © 2020 CAD Solutions, LLC,.