Parameter selection approach and optimization in abrasive water jet machining

Çetin, Beril (2021) Parameter selection approach and optimization in abrasive water jet machining. [Thesis]

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Abrasive Water Jet Machining (AWJM) is a non-traditional machining technique that uses high pressurized water for production. It is advantageous in comparison to conventional milling in terms of being cost, time and environmentally effective. For this to be the case, parameters must be chosen accordingly. Since AWJM has been in use for over decades, a significant amount of research has been made of parameter effects on mainly obtaining the best surface quality and maximum material removal rate. This thesis proposes a parameter selection approach, taking the interrelations among the parameters that affect the process. By looking at the relations, a method is proposed to choose parameters within specific ranges. The most effective parameters are abrasive flow rate, pressure and feed rate, whereas stand-off distance can be taken as minimum in general. The proposed approach gives an insight into parameter relations, in addition to guiding the parameter selection order. Minimum cycle time and minimum total cost are also computed through different solvers. In this research, FMINCON solver of MATLAB © and Genetic Algorithm are used. Both time and cost are computed by the mentioned optimization techniques and compared in terms of their result. It is seen that both methods have similar results, however the randomness in genetic algorithm, FMINCON solver is preferred in this case. Flatness, perpendicularity and roundness features of parts that AWJM produced are also examined. It is concluded that abrasive water jet machining is capable of achieving part quality within the tolerance values for these features.
Item Type: Thesis
Uncontrolled Keywords: parameter selection. - optimization in abrasive water jet machining. -- genetic algorithm. -- parameter optimization. -- cost and time minimization. -- parametre seçimi. -- aşındırıcı su jetinde optimizasyon. -- genetik algoritma. -- parametre optimizasyonu. --maliyet ve zaman minimizasyonu.
Subjects: T Technology > TS Manufactures > TS0155-194 Production management. Operations management
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Manufacturing Systems Eng.
Faculty of Engineering and Natural Sciences
Depositing User: IC-Cataloging
Date Deposited: 27 Oct 2021 10:50
Last Modified: 26 Apr 2022 10:39

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