Optimization of porosity behavior of hybrid reinforced titanium metal matrix composite through RSM, ANN, and GA for multi-objective parameters

Abstract

Abstract Titanium matrix composites (TMCs) have high specific strength and stiffness, and high-temperature TMCs can reduce weight by up to 50% when compared with monolithic super alloys while preserving equal stiffness and strength in jet engine systems for propulsion. The purpose of this work examines the use of mathematical models and learning approaches to optimize response such as porosity and control variables in synthesized hybrid titanium metal matrix composites (HTMMCs) reinforced by B4C-SiC-MoS2-ZrO2. To further understand the impacts of process factors on porosity reduction, the study employs methodologies such as the response surface methodology (RSM), integrated artificial neural networks (ANN), and genetic algorithm (GA). The findings indicate that these strategies have the potential to contribute to the industry. The optimal combination of 7.5wt.% SiC, 7.5wt.% B4C, 7.5wt.% ZrO2, 4wt.% MoS2, and 73.5wt.% Ti compositions was determined utilizing process factors such as milling period (6h), compaction pressure (50MPa), compact duration (50min), sintering temperature (1200°C), and sintering time (2h) as compared to pure Ti grade 5. The mechanical properties of the optimum combination of reinforcement weight percentage and process parameters resulted in a minimum porosity of 0.118%, density of 4.36gcm3, and micro-hardness of 63.4HRC boosted by 1.76%, and compressive strength of 2500MPa increased by 2.6%. In addition, these HTMMCs had a minimal wear rate of 0.176mm3/Nm and a corrosion resistance rate of 2.15×10-4mmpy. The investigation result analysis discovered that the RSM and combined ANN-GA models considerably enhanced the forecasting of multidimensional interaction difficulties in composite material production that were highly statistically connected, with R 2 values of 0.9552 and 0.97984. The ANN-GA model provided a 95% confidence range for porosity predictions, which increased the production use of titanium-based particle composites. Furthermore, HMMCs can be utilized in the automotive and aviation industries with enhanced corrosion and wear resistance.

Keywords

• Hybrid titanium metal matrix composite
• Porosity
• Response surface methodology
• Artificial neural networks
• Genetic algorithm