Novel constitutions of ceramic bond the new opportunity of engineering materials via solid-state process attaining enhanced material characteristics to overcome the drawback of conventional materials used in aquatic applications. The copper-based materials have great potential to explore high corrosion resistance and good thermal performance in the above applications. The main objectives of this research are to develop and enhance the characteristics of the copper-based hybrid nanocomposite containing different weight percentages of alumina and graphite hard ceramics synthesized via solid-state processing (powder metallurgy). The presence of alumina nanoparticles with a good blending process has to improve the corrosion resistance, and graphite nanoparticles may limit the weight loss of the sample during potentiodynamic corrosion analysis. The developed composite’s micro Vickers hardness is evaluated by the E384 standard on ASTM value of 69 Hv and is noted by increasing the weight percentages of alumina nanoparticles. The conduction temperature of actual sintering anticipates the thermogravimetric analysis of developed composite samples varied from 400°C to 750°C. The thermogravimetric graph illustration curve of the tested sample found double-step decomposition identified between 427°C and 456°C. The potentiodynamic analyzer is used to evaluate the corrosion behaviour of the sample and the weight loss equation adopted for finding the theoretical weight loss of the composite.
Machining titanium alloy (Ti6Al4V) used in orthopedic implants by conventional metal cutting processes is challenging due to excessive cutting forces, low surface integrity and tool wear. To overcome these difficulties and for ensuring high-quality products, various industries employ wire electrical discharge machining (WEDM) for precise machining of intricate shapes in titanium alloy. The objective is to make WEDM machining parameters as efficient as possible for machining the bio-compatible alloy Ti6Al4V using box-behnken design (BBD) and Non-dominated Sorting Genetic Algorithm II (NSGA II). A quadratic mathematical model is created to represent the productivity and the quality factor (MRR and surface roughness) in terms of varying input parameters, such as pulse active (Ton) time, pulse inactive (Toff) time, peak amplitude (A) current and applied servo (V) voltage. The established regression models and related prediction plots provide a reliable approach for predicting how the process variables affect the two responses viz MRR and SR. The effects of four process variables on both the responses were examined, and the findings revealed that the pulse duration and voltage has a major influence on the rate at which material is removed (MRR) whereas pulse duration influence quality (SR). The trade-off between MRR and SR, when significant process factors are included emphasizes the need for a reliable multi-objective optimization method. The intelligent metaheuristic optimization method named non-dominated sorting genetic algorithm II (NSGA II) is utilized to provide pareto optimum solutions in order to achieve high material removal rate (MRR) and low surface roughness (SR).
Machining titanium alloy (Ti6Al4V) used in orthopedic implants via conventional metal cutting processes is challenging due to excessive cutting forces, low surface integrity, and tool wear. To overcome these difficulties and ensure high-quality products, various industries employ wire electrical discharge machining (WEDM) for precise machining of intricate shapes in titanium alloy. The objective is to make WEDM machining parameters as efficient as possible for machining the biocompatible alloy Ti6Al4Vusing Box–Behnken design (BBD) and nondominated sorting genetic algorithm II (NSGA II). A quadratic mathematical model is created to represent the productivity and the quality factor (MRR and surface roughness) in terms of varying input parameters, such as pulse active (Ton) time, pulse inactive (Toff) time, peak amplitude (A) current, and applied servo (V) voltage. The established regression models and related prediction plots provide a reliable approach for predicting how the process variables affect the two responses, namely, MRR and SR. The effects of four process variables on both the responses were examined, and the findings revealed that the pulse duration and voltage have a major influence on the rate at which material is removed (MRR), whereas the pulse duration influences quality (SR). The tradeoff between MRR and SR, when significant process factors are included, emphasizes the need for a reliable multi-objective optimization method. The intelligent metaheuristic optimization method named nondominated sorting genetic algorithm II (NSGA II) was utilized to provide pareto optimum solutions in order to achieve high material removal rate (MRR) and low surface roughness (SR).
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