A Control Method of High Impact Energy and Cosimulation in Powder High‐Velocity Compaction

Abstract

To enhance the impact energy of powder high-velocity compaction (HVC) and thus improve the green density and mechanical properties of the resulting compacts, a mechanical energy storage method using combination disc springs is proposed. e high impact energy is achieved by modifying existing equipment, and the hydraulic control system is developed to implement the automatic control of the energy produced from the disc springs. An interdisciplinary cosimulation platform is established using the ADAMS, AMESim, and LabVIEW software packages to perform the interactive control of the simulation process and the realtime feedback of the simulation results. A mechanical-hydraulic cosimulation of the energy control virtual prototype of the testing machine is conducted using this platform. e influence of the impact energy on the green density is studied according to the HVC experimental results of the iron-based powders, and then, the green compact with the higher relative density is produced. e experimental results indicate that the energy enhancement method using the combination disc springs is reasonable and that the hydraulic control scheme is reliable.

Effects of Sintering Temperature on Densification, Microstructure and Mechanical Properties of Al-Based Alloy by High-Velocity Compaction

“…K. Q. Zhang et al [14] explored a data-driven model for green density prediction by the multilayer perceptron algorithm and optimised the processing parameters of HVC-based metallic powders. D. D. You et al [15] reported that a green compact with a 98% relative density is produced using pure iron powders with HVC.…”

Effects of Compaction Velocity on the Sinterability of Al-Fe-Cr-Ti PM Alloy

“…The process of HVC was simulated using DEM (discrete element method), and the change of porosity in HVC was captured [27]. High impact energy was achieved by modifying the existing equipment, and the hydraulic control system was developed to implement automatic control of the energy produced from the disc springs [28]. Zhang et al proposed a machine-learning approach based on materials informatics to predict the green density of compacts using relevant material descriptors, including chemical composition, powder properties, and compaction energy [29].…”