Modeling and Optimization of Process Parameters of the Piston Alloy-Based Composite Produced by FSP Using Response Surface Methodology

Abstract

In this paper, A356/B4C composites were fabricated using the friction stir processing (FSP) method. The process’s input parameters, including rotational and transverse speed, were optimized using the response surface methodology (RSM). Three factors and three levels with nine experimental runs made up the design of the experiments. An analysis of variance (ANOVA) was employed to determine whether the constructed model was adequate at a 95% confidence level. This study found that transverse speed was the most critical variable affecting the composites’ silicon (Si) particle size, UTS, and force. The findings demonstrate that the Si particle size of the parent material and the dispersion quality of B4C particles in the aluminum matrix are considerably influenced by the FSP factors, such as rotating speed and transverse speed. Second, tests for tensile strength were conducted to examine the composites’ mechanical properties. Then, using a specially designed fixture to measure force during the process, the forces on the tool, which play a decisive role in determining the tool’s life, were measured in different input parameters. The findings demonstrate that FSP transforms the mechanism of the fracture from brittle to extremely ductile in composites from the as-received metal.

Coupling of surface and volume behaviors to understand the generation of TTS in pure magnesium proceeded by HPT

“…Thus, severe plastic deformation (SPD) technologies have attracted more attention to studying the wear phenomenon and tribological mechanisms. Many of these technologies have been used, including equalchannel angular pressing (ECAP) [21], friction stir processing (FSP) [22], high-pressure torsion (HPT) [23,24], and other techniques. The authors suggest the possibility of exploring these techniques to improve the properties of materials and wear resistance.…”

Designing a Ship Autopilot System for Operation in a Disturbed Environment Using the Adaptive Neural Fuzzy Inference System

“…Additionally, the relationship between the steering gear and propeller was analyzed, as it directly affects the ship's speed and direction. To provide the controller with an accurate representation of the ship's operation and enable the testing of new control methods, the mathematical model was further refined to isolate the components that exclusively affect the course of the ship, such as the rudder, propeller, and hydrodynamic forces [3]. Test experiments were conducted on actual ships, and simulation techniques were utilized to determine the parameters of the model.…”

Prediction of age-hardening behaviour of LM4 and its composites using artificial neural networks

“…The results concluded that ANN was effective enough to provide a correlation between input and output parameters, whereas multiobjective optimization provided optimized FSW parameters. Similarly, Akbari et al [8,9] fabricated aluminum-based composites using FSW, optimized the parameters using RSM, and ANOVA, and numerically simulated the process using the 3D FEM process. These advanced simulations and software helped researchers determine better working parameters and conditions without performing bulk experimentations.…”