Abrasive Jet Drilling of Alumina Ceramic with Pressurized-Fluidized Bed Set-up

Abstract

Nontraditional methods of machining processes cover a wide range of technologies, possessing slower material removal rate with high accuracy and precision which are impractical and costly in traditional machining methods. Abrasive Jet Machining (AJM) is an effective technique that can precisely machine hard, brittle and heat sensitive materials like glass, quartz, sapphire, semiconductor materials, super alloys, mica, refractory materials and ceramics to create slots, thin sections, contouring, drilling, etching, deburring, and polishing. Pressurized and Fluidised Bed Abrasive Jet Machining (PFB-AJM) is one such modern machining process that employs the high speed homogeneous mixture of compressed air and abrasive particles to impinge on the work piece surface with high kinetic energy to remove material by impact erosion. The principle of development of brittle fracture, expansion and propagations of cracks in lateral and longitudinal directions due to repeated impact of the solid abrasive grains on the tough and hard work piece material occurs in AJM, where the total kinetic energy is converted into impact energy. The effective utilization of the resources or selected input process parameters and previous experience of the operators affect the machining processes of different non traditional methods (NTD). Hence the machining parameters are optimized for the economic, efficient and effective utilization of the resources. This paper focuses on experimentation of K-60 alumina ceramic work piece material with silicon carbide (SiC) abrasives on an indigenously fabricated AJM set up basing upon pressurized power feed and fluidized bed mixing chamber system. The impact of three process parameters such as pressure (P), nozzle tip distance (Z) and grain size (G) on the three responses viz. material removal rate (W), surface roughness (Ra), and depth of cut (H) are analyzed. Experiments are carried out according to the Taguchi Orthogonal Array. Then principal component analysis (PCA) is used for optimization of the process parameters to obtain the optimal parameter settings for achieving the optimal responses and finally the results are experimentally verified.