Experimental investigation on the effet of input paramètres on surface roughness and MRR of abrasive flow machining process

¹ Research Scholar, Department of Mechanical Engineering, JNT University, Kakinada, India
² Professor, Department of Mechanical Engineering, & Vice-chancellor, JNT University, Anthapur, India

^* Corresponding author: kondala.peram@gmail.com

Abstract

A crucial and costly step in the whole manufacturing process is the precision and super finishing procedure. A step of final finishing is involved in the production of precision components. It accounts for a respectable portion of the cost of production overall, is mostly uncontrolled, and requires a lot of manpower. Abrasive finish methods are being developed to address issues including high direct costs and the production of precision components with particular characteristics for finishing inaccessible places. A vast number of cutting blades with arbitrary orientation and shape are used in the abrasive finishing process. Due to their ability to complete a variety of form geometries with the appropriate dimensional accuracy and surface polish, abrasive fine procedures are often used. The unconventional finishing method known as AFM (abrasive flow machining) presses abrasive viscoelastic polymer on the surface of work piece. Al7075/SiC NMMCs’ internal rounded and hollow surfaces are completed using an AFM trial procedure that is constructed and designed in conjunction with specially produced medium. Workpieces are created using a lathe shortly after stir casting metal matrix nano composites with cross sections of 25 mm in diameter and containing 1 percent,1 ,2,3,4 nano-Sic (50 nm) by weight. Extrusion pressure abrasive particle grain size number of cycles were evaluated for their surface roughness (Ra) than material removal (MR), respectively. The evaluation of the material’s qualities, such as density, hardness, and tensile strength The improvement in the surface completeness of these NMMCs is further shown by the scanning Microscopy OM, EDS SEM, and XED analyses.