A comparative investigation of hardness and compression strength of Nickel coated B4C reinforced 601AC/201AC selective layered functionally graded materials

Abstract

Abstract High hardness and compression strength are the essential properties in many engineering applications. Requirement of light weight materials with high mechanical properties as a function of direction leads to the development of Aluminium based Functionally Graded Materials (FGM). Two different matrix metal blends 601AC and 201 AC are used for the comparison. Mechanical Alloying technique (MA) is used to blend the metal powders. The FGM specimens with 2 layers, 3 layers and 5 layers are fabricated using Powder Metallurgy (P/M) technique. B4C with Nickel coating and without Nickel coating is used as particulate. Two different average particle size of B4C (20 μm and 50 μm) was selected. The configurations used for the FGMs are 0%/5% for 2 layered specimen, 0%/5%/10% for 3 layered specimen and 0%/5%/10%/15%/20% for five layered specimen. Specimens are prepared according to ASTM B925-3 standard for micro-hardness test and compressive strength test. Graphs are plotted in terms of number of layers, particle size, sintering temperature, and coating condition. From the experiments it is observed that, number of layers influences the hardness and compressive strength. The average specimen hardness is enhanced from 55 HV for two layered FGM to 73 HV for three layered FGM. The compressive strength is increase from 613 MPa for three layered specimen without Nickel coated B4C to 732 Mpa for three layered specimen with Nickel coating. Best possible combination of all parameters is observed from the experiment is particle size of 20 μm, sintering temperature of 570 °C, and B4C particulate with Nickel coating. Compared to the hardness values for two different matrix materials 601AC/201AC, closure values are observed for the best possible combination of other variables. Where-as for the compressive strength 201AC matrix shows superior values compared to 601AC for the best possible combination of all the other variables.