Meshfree modelling of metal cutting using phenomenological and data-driven material models

Numerical modelling of chip formation is important for a better understanding thus for improvement of the high speed metal cutting process. The objective of this work is to develop a meshfree numerical simulation framework for the chip formation process, where both the phenomenological material model and the data-driven material model can be applied. Firstly, the phenomenological model is applied to capture the serrated chip formation, where a recently developed Galerkin type meshfree scheme, the stabilized Optimal Transportation Meshfree (OTM) method, is applied as a numerical solution method. This enables the modelling of material separation and serrated morphology generation of the cutting process in a more realistic and convenient way. The shear band formation is described by the thermal softening term in the Johnson-Cook plastic flow stress model. Using this model, it can be demonstrated that thermal softening is the main cause of the shear band formation. Additionally, it can be seen that the Johnson-Cook fracture model shows limitations in capturing the fracture on the chip upper surface. Thus, a supplementary condition for the stress triaxiality is applied. This condition allows more accurate measurements of the chip size, i.e. chip spacing, peak and valley. These improvements are demonstrated by comparing the calculated chip morphology, cutting force and chip formation process with experimental results. Subsequently, the data-driven material model is developed to replace the classical material model, where the Machine Learning (ML) based model is first trained offline to fit an observed material behaviour and later be used in online applications. However, learning and predicting history dependent material models such as plasticity is still challenging. In this work, a ML based material modelling framework is proposed for both elasticity and plasticity, in which the ML based hyperelasticity model is directly developed with the Feed forward Neural Network (FNN), whereas the ML based plasticity model is developed by using two approaches including Recurrent Neural Network (RNN) and a novel method called Proper Orthogonal Decomposition Feed forward Neural Network (PODFNN). In the latter case, the accumulated absolute strain is proposed to distinguish the loading history. Additionally, the strain-stress sequence data for the plasticity model is collected from different loading paths based on the concept of sequence for plasticity. By means of the POD, the multi-dimensional stress sequence is decoupled as independent one dimensional coefficient sequences. To apply the ML based material model in finite element analysis, the tangent matrix is derived by the automatic symbolic differentiation tool AceGen. The effectiveness and generalisation of the presented models are investigated by a series of numerical examples using both 2D and 3D finite element analysis. Finally, the ML based material model is applied to the metal cutting simulation

Modelling of Electromechanical Coupling in Geometrically Exact Beam Dynamics

Dielectric elastomers show promising performance as actuators for soft robotics. Thus, accurate and efficient numerical algorithms for the simulation of Dielectric Elastomer Actuators (DEAs) are required for the design and control of the soft robotic system. In this work, the Cosserat formulation of geometrically exact beam dynamics is extended by adding the electric potential as an additional degree of freedom to account for the electrical effects. A formulation of electric potential and electric field for the geometrically exact beam model is proposed such that complex beam deformations can be generated by the electrical forces. The kinematic variables in continuum electromechanics are formulated in terms of beam strains. The electromechanically coupled constitutive model for the beam formulation is obtained by integrating the strain energy in continuum electromechanics over the beam cross section, which leads to a direct transfer of the dielectric constitutive models in continuum mechanics to the beam model. The electromechanically coupled beam dynamics is solved with a variational time integrator scheme. By applying different electrical boundary conditions to the beam nodes, different deformation modes of the beam are obtained in the numerical example

A machine learning based plasticity model using proper orthogonal decomposition

Data-driven material models have many advantages over classical numerical approaches, such as the direct utilization of experimental data and the possibility to improve performance of predictions when additional data is available. One approach to develop a data-driven material model is to use machine learning tools. These can be trained offline to fit an observed material behaviour and then be applied in online applications. However, learning and predicting history dependent material models, such as plasticity, is still challenging. In this work, a machine learning based material modelling framework is proposed for both elasticity and plasticity. The machine learning based hyperelasticity model is developed with the Feed forward Neural Network (FNN) directly whereas the machine learning based plasticity model is developed by using of a novel method called Proper Orthogonal Decomposition Feed forward Neural Network (PODFNN). In order to account for the loading history, the accumulated absolute strain is proposed to be the history variable of the plasticity model. Additionally, the strain-stress sequence data for plasticity is collected from different loading-unloading paths based on the concept of sequence for plasticity. By means of the POD, the multi-dimensional stress sequence is decoupled leading to independent one dimensional coefficient sequences. In this case, the neural network with multiple output is replaced by multiple independent neural networks each possessing a one-dimensional output, which leads to less training time and better training performance. To apply the machine learning based material model in finite element analysis, the tangent matrix is derived by the automatic symbolic differentiation tool AceGen. The effectiveness and generalization of the presented models are investigated by a series of numerical examples using both 2D and 3D finite element analysis

Effect of Shear on Ultrasonic Flow Measurement Using Nonaxisymmetric Wave Modes

Nonaxisymmetric wave propagation in an inviscid fluid with a pipeline shear flow is investigated. Mathematical equation is deduced from the conservations of mass and momentum, leading to a second-order differential equation in terms of the acoustic pressure. Meanwhile a general boundary condition is formulated to cover different types of wall configurations. A semianalytical method based on the Fourier-Bessel theory is provided to transform the differential equation to algebraic equations. Numerical analysis of phase velocity and wave attenuation in water is addressed in the laminar and turbulent flow. Meanwhile comparison among different kinds of boundary condition is given. In the end, the measurement performance of an ultrasonic flow meter is demonstrated

Asiatic acid improves high-fat-diet-induced osteoporosis in mice via regulating SIRT1/FOXO1 signaling and inhibiting oxidative stress

Asiatic acid can attenuate osteoporosis through suppressing adipogenic differentiation and osteoclastic differentiation. Oxidative stress enhances osteoclastic differentiation but represses osteogenic differentiation to promote osteoporosis. However, the role and mechanism of asiatic acid in osteoporosis have not been reported. Firstly, mice were fed with high-fatdiet (HFD) with or without asiatic acid for 16 weeks. Data from an automatic biochemical analyzer showed that HFD induced down-regulation of high-density lipoprotein (HDL) and an increase of serum levels of triglyceride (TG), total cholesterol (TC) and low-density lipoprotein (LDL). However, asiatic acid administration attenuated the decrease of HDL and increase of serum TG, TC and LDL in osteoporotic mice. Secondly, HFD induced high levels of malondialdehyde (MDA) and reactive oxygen species (ROS), low levels of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) in osteoporotic mice. However, the levels of MDA, ROS, SOD and GSH-Px in osteoporotic mice were reversed by asiatic acid administration. (this section is unclear and requires revision) Asiatic acid administration reduced expression of c-telopeptide of type 1 collagen (CTX-1), enhanced alkaline phosphatase (ALP) and procollagen type 1 N-terminal propeptide (P1NP) in HFD-induced osteoporotic mice. (this section is unclear and requires revision) Thirdly, asiatic acid promoted calcium deposition in bone marrow cells and osteogenic differentiation in osteoporotic mice, but decreased ALP in bone marrow cells. Lastly, asiatic acid enhanced SIRT1 and nuclear FOXO1 (Nu-FOXO1) expression, while it reduced Acetyl FOXO1 (AcFOXO1) in osteoporotic mice. In conclusion, asiatic acid might inhibit oxidative stress and promote osteogenic differentiation through activating SIRT1/FOXO1 to attenuate HFD-induced osteoporosis in mice