Investigation of Microstructure, Mechanical, and Tribological Behaviors of AA7075 Reinforced with B4C Composites Developed Through Floating Die

Abstract

Aluminum-based matrix composites manufactured through powder metallurgy are seeing growing demand in the aerospace and defense sectors. AA7075 with different weight percentages of B₄C (0%, 5%, 10%, and 15%) were fabricated using a floating die and tested to detect their density, porosity, and mechanical and tribological characteristics. Additionally, the impact of four factors, including B₄C weight percentage (5%, 10%, and 15%), sliding speed (1 m/s, 1.5 m/s, and 2 m/s), load (10 N, 15 N, and 20 N), and sliding distance (500 m, 1000 m, and 1500 m), on the wear characteristics of AA7075/B₄C composites were studied. The design of experiments was conducted using an L9 orthogonal array. The analysis was conducted using response surface methodology, ANOVA, and Taguchi to detect the ideal parameters for reducing the wear. The study unveiled homogeneous distribution and robust matrix-reinforcement interfacial bonding between B₄C and AA7075. Furthermore, the failure models of the composites were uniform across all the specimens. Notably, compared to earlier studies, the composite reinforced with 5% of B₄C exhibited a substantial 18.94% (410 MPa) increase in compressive strength,12.85% reduction in weight (2.49 g/ml), and 88.9% reduction in wear loss (4.01 µm). Additionally, it revealed that sliding distance (68.67%) had the greatest influence on wear loss, ranked next were sliding speed (22.1%), B₄C% (5.75%), and load (3.48). In contrast, the coefficient of friction (COF) was affected by B₄C% (54.95%), and ranked next were sliding distance (22.89%), load (16.75%), and sliding speed (5.41%). The achieved improvements in strength, wear resistance, and weight reduction make the AA7075/5% B₄C composite highly desirable for aerospace, defense, and automotive applications. Additionally, the mathematical models for wear and COF that have been developed in this work reduce time, enhance optimization efficiency, and eliminate the need for costly trials.