journal article·2026-01Open access

Research and application of a vibration compaction experimental system for similar materials based on an excitation dynamics model

Yunhao Wu, Yong Zhang, Baoyang Wu, Mengtian Li, Hanpeng Wang, Bing Zhang, Wei Wang, Ruijie Zheng

Abstract

To explore novel methods for preparing similar model materials, this paper establishes an excitation dynamics model for the model materials was, and develops a vibration compaction molding machine. Subsequently, systematic vibration compaction tests on specimens were conducted employing the orthogonal experimental design. Similar model materials were prepared under varying parameters of static platen pressure, excitation frequency, and vibration duration. The tests yielded key physical and mechanical property parameters of the different similar material specimens, including density, compressive strength, elastic modulus, tensile strength, and water absorption rate. The results were then analyzed using methods such as range analysis, analysis of variance, and multiple regression analysis. The findings demonstrate that: (1) Excitation frequency significantly influences the mechanical properties of the similar materials. (2) Regression equations describing the effect of vibration compaction parameters on the mechanical parameters of the similar materials were derived and validated through fitting verification. The relative error between the fitted values and the measured values ranged from 0.45% to 12.50%. Finally, based on the vibration compaction testing machine, a vibration compaction experimental device for a 3D intelligent similar material model laying system was successfully developed and implemented, offering a new approach for the preparation of similar model materials.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Powder Metallurgy Techniques and Materials · Epoxy Resin Curing Processes · Injection Molding Process and Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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