SiC particle reinforced aluminum matrix composites (SiCp/Al) are widely used in aviation, weaponry, and automobiles because of their excellent service performance. Wire electrical discharge machining (WEDM) regardless of workpiece hardness has become an alternative method for processing SiCp/Al composites. In this paper, the temperature distribution and the discharge crater size of the SiCp/Al composite are simulated by a thermophysical model during a single-pulse discharge process (SPDP) based on the random distribution of SiC particles. The material removal mechanism of the SiCp/Al composite during the multi-pulse discharge process (MPDP) is revealed, and the surface roughness () of the SiCp/Al composite is predicted during the MPDP. The thermophysical model simulation results during the MPDP and experimental characterization data indicate that the removal mechanism of SiCp/Al composite material consists of the melting and vaporization of the aluminum matrix, as well as the heat decomposition and shedding of silicon carbide particles. Pulse-on time (), pulse-off time (), and servo voltage () have a great influence on surface roughness. The Ra increases with an increase in and , but decreases slightly with an increase in . Moreover, compared with experimental data, the relative error of calculated from the thermophysical model is 0.47-7.54%. This means that the developed thermophysical model has a good application and promotion value for the WEDM of metal matrix composite material.
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http://dx.doi.org/10.3390/ma17225546 | DOI Listing |
Materials (Basel)
November 2024
State Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.
Materials (Basel)
October 2024
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
The fabrication of micro-holes in hard-to-machine materials presents considerable challenges in precision machining. This study proposes a novel approach that employs high-strength micro-grinding tools with a central abrasive grain absence to create micro-holes through helical grinding. Due to the random distribution of abrasive grains, the absence of grains at the tool's center becomes an inevitable technical challenge.
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April 2024
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.
The effects of Ti doping on the microstructure and properties of SiC/Al composites fabricated by pressureless infiltration were comprehensively investigated using first-principles calculations and experimental analyses. First-principles calculations revealed that the interface wetting and bonding strength in an Al/SiC system could be significantly enhanced by Ti doping. Subsequently, the Ti element was incorporated into SiC preforms in the form of TiO and TiC to verify the influence of Ti doping on the pressureless infiltration performance of SiC/Al composites.
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January 2024
Ningbo Innovation Center, Zhejiang University, Ningbo 315100, China.
Recent works have experimentally proven that metal matrix composites (MMCs) with network architecture present improved strength-ductility match. It is envisaged that the performance of architecturally designed composites is particularly sensitive to reinforcement strength. Here, reinforcing particles with various fracture strengths were introduced in numerical models of composites with network particle distribution.
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March 2024
School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
SiCp/Al composites offer the advantages of lightweight construction, high strength, and corrosion resistance, rendering them extensively applicable across various domains such as aerospace and precision instrumentation. Nonetheless, the interfacial reaction between SiC and Al under high temperatures leads to degradation in material properties. In this study, the interface segregation energy and interface binding energy subsequent to the inclusion of alloying elements were computed through a first-principle methodology, serving as a dataset for machine learning.
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