2018 : Artificial neural network and genetic algorithm for multi-objective optimization in drilling of glass fiber reinforce polymer-stainless steel stacks

Ir. Bobby Oedy Pramoedyo S. MSc., Ph.D.
Prof. Dr.-Ing. Ir. Suhardjono MSc.


Abstract

Glass fiber reinforced polymer (GFRP)-stainless steel stacks used in the aircraft structural components. The assembly process of this components requires mechanical joining using bolt and nut. The conventional drilling process is commonly used for producing hole to position the bolt correctly. Drilling is the complex machining process due to the variation in geometrical chance along the cutting edge. Thrust force and hole surface roughness are responses that used to evaluate the performance of drilling process. The quality characteristic of these responses are “smaller-is-better.” The aim of this experiment is to identify the combination of process parameters for achieving required multiple performance characteristics in drilling process of GFRP-stainless steel stacks materials. The three important process parameters,i.e., point angle, spindle speed, and feed rate were used as input parameters. Point angle was set at …