Coupled CA-FE simulation for dynamic recrystallization of magnesium alloy during hot extrusion

In the present research,the dynamic recrystallization(DRX) behavior of a newly-developed Mg-Al-Zn-RE alloy with abundant secondphase particles during hot extrusion is investigated by coupling finite element(FE) and cellular automaton(CA) models.A two-dimensional CA model is developed to quantitatively and topologically evaluate the DRX process during deformation with constant forming conditions.Considering the fact that second-phase particles with various sizes extensively exist in the studied Mg-Al-Zn-RE magnesium alloy,models of DRX nucleation and grain growth velocity are modified.The coefficients of the modified CA model are calibrated by isothermal compression experiments of the magnesium alloy.Subsequently,the CA model is coupled with FE analysis to investigate the DRX behavior during the hot extrusions of the Mg-Al-Zn-RE alloy.The DRX behavior of the magnesium alloy at different stages and positions of extruded plates is simulated by the established model.Finer grains near the edge than in the inner of the plates result from higher strain and strain rate.The influence of extrusion conditions on microstructural evolution is explored.Under the employed forming conditions,average grain size decreases 28-62 times from as-cast and solution-treated to as-extruded state due to grain refinement by DRX.With increasing initial billet temperature or extrusion speed,average grain size increases.The finest grains are obtained at the initial billet temperature of 623 K and the extrusion speed of 7.83 mm/s.Low initial billet temperature or high extrusion speed benefits homogeneous grain distribution.The simulated results are in good agreement with experimental ones.

Design and Analysis of a Horizontal Quasi-Zero Stiffness Vibration Isolator by Combining Rolling-Ball and Disk Springs in Parallel

Combining disk springs having negative stiffness with a rolling-ball in parallel is proposed in this paper. It is used to reduce the system stiffness and the positioning error in a non-ideal environment.The characteristics of a disk spring are analyzed. The dynamic equation of its motion has been obtained based on Newton＇s second law. After definition of a error margin,the dynamic equation of the motion can be treated as a Duffing oscillator,and the influences of non-dimensional parameters on the stiffness and transmissibility are studied. The natural frequency and transmissibility are achieved in a linearization range,where the ratio of linear to nonlinear items is small enough.The influence of mass ratio and non-dimensional parameters on natural frequency are analyzed. Finally,a comparison of numerical example demonstrates that the QZS system can realize a lower stiffness within an increased range.

Framework and development of data-driven physics based model with application in dimensional accuracy prediction in pocket milling

In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same importance but overlooked in current research.If the RBTE reduces by 30%,the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component.Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation.This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing.In this paper,the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components,which enables accurate prediction and compensation of RBTE value for the thin wall components.The physics based model considers the primary root cause,in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness(AMRT)variation,for the RBTE formation.And to incorporate the dynamic and inherent coupling of the complicated manufacturing system,the multi-feature fusion and machine learning algorithm,i.e.kernel Principal Component Analysis(kPCA)and kernel Support Vector Regression(kSVR),are incorporated with the physics based model.Therefore,the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy.The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling,and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling,respectively.

Absorption of some powder materials to YAG laser

Laser powder alloying is widely used for tribological applications. As one of the key pa-rameters , absorptivity of powder materials to laser plays an important role in the processing. Themeasurement of laser absorptivity is essential for absorptivity research. In present work, lumpedmethod based on heat transfer is established for laser absorptivity measurement. The absorptivityof some powder materials as Cu, Fe, Al, NiO, Al2O3, ZrO2, SiC, to YAG laser, are investigated.The results show that the absorptivity of powder materials to YAG laser is generally larger thanthat of bulk materials.