الفهرس 
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Abstract
Advanced materials such as alloys, and composite materials etc. are replacing other materials in many different manufacturing applications. This is due to the improvements achieved in their engineering properties. Among various advanced materials come the Armox 500T, Titanium alloy Ti-6Al-4V, and CFRP laminates. These materials are exploited in many military and industrial applications especially in the Egyptian industry. The demand on these advanced materials acquire non-traditional machining techniques for better cutting performance. Because of they are often offering high strength and hardness. Productivity in terms of material removal rate beside the cutting surface quality particularly surface roughness and kerf tapering are the most commonly issues encountered with production engineers during machining these materials.
Abrasive waterjet (AWJ) is one of the advanced machining processes applied widely in industry. It is superior to many other cutting techniques in processing various materials, particularly in processing difficult-to-cut materials. AWJ has many advantages like no heat affected zone. And has the ability to cut very complicated shapes and profiles. Besides the produced cutting surface has a good acceptable surface roughness value. In other words, there is no need for a secondary machining process. This technology is being progressively used in various industries. However, its cutting capability in terms of the material removal rate and kerf tapering are the major obstructions limiting its further applications.
Artificial intelligence (AI) has been established as the area of computer science dedicated to produce software capable of sophisticated, intelligent, computations similar to those that the human brain routinely performs. AI techniques have gained popularity in engineering applications in recent yearsdue to their efficiency and effectiveness. Artificial neural network (ANN) and genetic algorithm (GA) are two of the most promising AI techniques. ANN and GA techniques has been conducted to estimate optimal process parameters in AWJ operation. These two techniques are both considered to be appropriate in the process of modeling and optimization for AWJ machining process.
Scope and aim of this study are to investigate, model, and optimize the AWJ process in order to improve the AWJ process performance. Firstly, an experimental investigation is undertaken to study the major cutting performance measures in AWJ of three selected advanced materials. The considered AWJ process parameters include traverse speed, waterjet pressure, abrasive flow rate, and stand-off distance. The significant control factors have been found with further discussion of their effect on material removal rate, surface roughness, and kerf taper. Secondly, ANN models were introduced as one of the AI techniques. ANN with a back-propagation algorithm and a feed forward structure has been developed for these proposed models. Proposed ANN models were trained using conducted experimental data. ANN Models are verified and validated before implementation in optimization process. Finally, multi-objective optimization was carried out using genetic algorithm technique. GA technique was applied to obtain maximum material removal rate (MRR) with minimum surface roughness (Ra), and minimum kerf tapering (KT).
It was evidence that multi-objective optimization using GA is capable of offering sets of optimal solutions. These sets of optimal solutions include a combination of AWJ process parameters settings to obtain satisfying MRR, Ra, and KT according to production engineer requirements.