الفهرس 
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Abstract
Abstract
Abrasive water jet machining (AWJM) is one of the most important untraditional machining which produced in 1980s. The importance of AWJM appears in cutting the hardest materials at different thicknesses. For the time being, it is used in cutting large thickness materials to avoid the flaws caused by flame cutting. This thesis presents the effect of AWJM parameters on surface quality and a clarification to replace the flame cutting. The variable parameters are mainly water pressure (P), traverse speed (Ts), and abrasive mass flow rate (Abr). One factor at a time (OFAT) design method which change one factor while holding the others constants, and Taguchi design method with orthogonal array L25 are used in this work as experiments design methods. The studied surface integrity parameters are surface roughness (Ra), surface waviness (Wa), and kerf taper degree (Ө) when cutting steel 37 at thickness 55 mm. OFAT is used to obtain the influence of the inputs on outputs. ANOVA is used to detect the most substantial parameters and their effective percentages. By Taguchi analysis coupled with signal to noise ratio (smaller is better), the preferred levels are determined and validated. Artificial neural network (ANN) is used to build a multi prediction modelling between the studied outputs and variables process parameters.
It is concluded that the most substantial parameter is the traverse speed followed by abrasive mass flow rate on surface roughness, waviness, and kerf taper degree, the water pressure is the third substantial parameter on surface waviness and kerf taper degree. After applying of ANN to estimate the waviness, kerf taper degree, and surface roughness, it was concluded that this method is
Abstract
II
suitable for build modelling with maximum error percentage is equal to 8.9%, 8.3%, and 8.9% respectively.