Optimum bushing length in thermal drilling of galvanized steel using artificial neural network coupled with genetic algorithm

Abstract

Thermal drilling is a novel sheet-metal-hole-making technique that utilizes the heat produced at the interface of the rotating conical tool and workpiece in order to soften the workpiece and pierce a hole into it. In this work, experiments with thermal drilling of galvanized steel were conducted based on the Taguchi L27 orthogonal array. Significant process parameters such as rotational speed, tool angle and workpiece thickness were varied during the experimentation. In thermal drilling, the thermal-drill tool pushes aside a large amount of workpiece material to form a sleeve, which is often referred to as the bushing length. A predictive model for the bushing length was developed using a feed-forward artificial neural network based on experimental data. As the bushing length is closely associated with the tapping process, the influences of the input process parameters play a vital role in fastening galvanized steel with threaded fasteners in diverse engineering applications. The optimization problem was solved by implementing a genetic algorithm under constraint limits to maximize the bushing length. Further, a confirmation test was conducted with the intention to compare the optimum value and its corresponding bushing length predicted by the genetic algorithm. Good agreement was observed between the predicted and the experimental values. Keywords: thermal drilling, artificial neural network, genetic algorithm, galvanized steel, bushing length Termi~no vrtanje je nova, za vrtanje lukenj v plo~evino, uporabljena tehnika, ki izkori{~a toploto, proizvedeno na povr{ini vrte~e se konice orodja na obdelovancu z namenom, da ga zmeh~a in vanj naredi luknjo. V delu so bili izvedeni preizkusi na osnovi metode Taguchi L27 z ortogonalno matriko s termi~nim vrtanjem galvaniziranega jekla. Pomembni parametri postopka, kot so: hitrost vrtenja, kot orodja in debelina obdelovanca, so se med eksperimentiranjem spreminjali. Pri toplotnem vrtanju, vrtanje orodja potisne stran ve~materiala obdelovanca tako, da se tvori navarek (rokav) okoli luknje, ki se pogosto omenja kot dol`ina {ablone. Napovedni model za dol`ino {ablone, je razvit z uporabo umetne nevronske mre`e, ki temelji na znanstvenih podatkih. Ker je dol`ina {ablone precej povezana s procesom izdelave navoja, vplivi teh vhodnih procesnih parametrov igrajo klju~no vlogo pri pritrditvi galvaniziranega jekla z navojem pritrdilnih elementov v razli~nih in`enirskih aplikacijah. Problem optimizacije je bil re{en z implementacijo genetskega algoritma na podlagi omejitev za pove~anje dol`ine {ablone. Ugotovljeno je bilo dobro ujemanje med napovedano in eksperimentalno vrednostjo. Klju~ne besede: termi~no vrtanje, umetna nevronska mre`a, genetski algoritem, galvanizirano jeklo, dol`ina {ablone

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