Hybrid multi-objective optimization of microstructural and mechanical properties of B4C/A356 composites fabricated by FSP using TOPSIS and modified NSGA-II
(1. School of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran;
2. School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran;
3. Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran)
Abstract: A356 alloy was used as the base metal to produce boron carbide (B4C)/A356 composites using friction stir processing (FSP). The microstructural and mechanical properties of B4C/A356 composites were optimized using artificial neural network (ANN) and non-dominated sorting genetic algorithm-II (NSGA-II). Firstly, microstructural properties of the composites fabricated in different processing conditions were investigated. Results show that FSP parameters such as rotational speed, traverse speed and tool pin profile significantly affect the size of the primary silicon (Si) particles of the base metal, as well as the dispersion quality and volume fraction of reinforcing B4C particles in the composite layer. Higher rotational to traverse speeds ratio accompanied by threaded pin profile leads to better particles distribution, finer Si particles and smaller B4C agglomerations. Secondly, hardness and tensile tests were performed to study mechanical properties of the composites. FSP changes the fracture mechanism from brittle form in the as-received metal to very ductile form in the FSPed specimens. Then, a relation between the FSP parameters and microstructural and mechanical properties of the composites was established using ANN. A modified NSGA-II by incorporating diversity preserving mechanism called the ε elimination algorithm was employed to obtain the Pareto-optimal set of FSP parameters.
Key words: friction stir processing; B4C; composite; multi-objective optimization; TOPSIS method