Digital twin-driven manufacturing of diamond-reinforced metal matrix composites: a case study on spreading behaviour of AlSi10Mg/diamond powder in selective laser melting
Year: 2026
Type: article
Abstract: Diamond-reinforced metal matrix composites possess significant application value in aerospace and electronic packaging fields, yet their fabrication via selective laser melting remains constrained by insufficient understanding of powder spreading behaviour. A DEM-based digital twin model was established for the SLM spreading process of AlSi10Mg–diamond mixed powder. Model parameters were determined through sliding friction experiments and angle of repose calibration, and in-situ high-speed camera validation confirmed dynamic repose angle errors below 2%. Based on the validated model, the effects of diamond volume fraction, spreading speed, and layer thickness on powder bed coordination number, diamond distribution standard deviation, normalised mean mass flow rate, and its coefficient of variation were quantified. The results demonstrate that layer thickness is the key process parameter for powder bed compactness, high spreading speed is the primary driver of flow instability and uniformity degradation, and increasing diamond content simultaneously degrades flowability and spreading uniformity. Contact force chain analysis revealed that diamond particles induce force arch evolution from ‘insecure → meta-stable → strong’ through mechanical interlocking, thereby driving a flow regime transition from ‘free settling’ to ‘horizontal alignment’. This study provides particle-scale insight and digital twin support for optimising SLM spreading process parameters for diamond-reinforced AlSi10Mg composites.
Source: Digital Twin
Authors: Meng Ye, Chenxi Cai, Guangjun Mu, Hanyang Li, Xuekun Li, Greg Zhu
Institutions: State Key Laboratory of Tribology, Tsinghua University
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Awards: G1940021758, G8846916139
OpenAlex ID: W7203879811
