چکیده مقاله
In this research, a newly integrated neural network based methodology is proposed for modeling and optimal selection of process variables involved in powder mixed EDM process Three different kinds of fine abrasive powders, namely, copper Cu: metal , aluminum oxide Al2O3: metal oxide , and silicon carbide SiC: combination of metal and non metal , having the same particle concentration and size of 2 5 2 8 gr/lit and 45 50 μm, respectively, were added separately into the kerosene dielectric liquid To compare and investigate the effects caused by every powder of differently thermophysical properties on the EDM process performance with each other as well as the pure case, a series of experiments were conducted on a specially designed experimental setup developed in the laboratory Discharge current I , pulse period T and source voltage V were selected as the independent input parameters to evaluate the process performance in terms of material removal rate MRR and surface roughness Ra Generally, of the studied additives, Al2O3 produces the greatest MRR, esp , in high currents and low pulse periods, followed by SiC, with Cu powders producing the smallest A 3 6 4 2 size feed forward neural network with back propagation BP learning algorithm is then developed to establish the process model of the best selected Al2O3 powder mixed EDM Training and testing of the neural model are carried out using experimental data Having established the process model, a second network, parallelizing the augmented Lagrange multiplier ALM algorithm, determines the corresponding optimal input parameters by maximizing MRR subject to appropriate operating and prescribed surface roughness constraints The optimization procedure is implemented in each level of machining regimes finishing, semi finishing and roughing and the obtained optimum machining parameters settings are also verified experimentally
کلیدواژهها
نویسندگان
S Assarzadeh
۱Mechanical Engineering Group, Islamic Azad University of Ghouchan, P O Box: ۴۶۳,Ghouchan, Iran
M. Ghoreishi
۲Mechanical Engineering Department, Khajeh Nasir K N Toosi University of Technology
شیوه ارجاع
Assarzadeh, S and Ghoreishi, M.,1386,Modeling and Optimizing Powder Mixed Electrical Discharge Machining (PMEDM) Process using Neural Networks,02nd Tehran International Congress on Manufacturing Engineering (TICME2007),Tehran
ارائهشده در
مجموعه مقالات دومین کنفرانس بین المللی و هشتمین کنفرانس ملی مهندسی ساخت و تولید10 اسفند 1386 · تهران