Universiti Teknologi Malaysia Institutional Repository

Orthogonal based ANN and multiGA for optimization on WEDM of Ti–48Al intermetallic alloys

Yusoff, Yusliza and Mohd. Zain, Azlan and Amrin, Astuty and Sharif, Safian and Haron, Habibollah and Sallehuddin, Roselina (2019) Orthogonal based ANN and multiGA for optimization on WEDM of Ti–48Al intermetallic alloys. Artificial Intelligence Review, 52 (1). pp. 671-706. ISSN 0269-2821

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1007/s10462-017-9602-2


As surveyed, many efforts have been made to model the performances of electrical discharge machining (EDM) using artificial neural network (ANN). However, the selections of the network parameters were mostly prepared in a random manner, resulting to unnecessary trials. Thus, orthogonal array (Taguchi) is employed in the procedure of network function and network architecture assortment to avoid excessive random trial experimentations. This proposed orthogonal based ANN modelling is employed on WEDM of Ti–48Al intermetallic alloys. Meanwhile modified multi objective genetic algorithm (multiGA) is used as the optimization technique. Material removal rate (MRR), surface roughness (Ra), cutting speed (Vc) and width of kerf (Dk) are the machining performances considered in this study. Five machining parameters observed from the previous researches are chosen as significant factors to the machining performances in this study, which are pulse on time, pulse off time, peak current, feed rate and servo voltage. Experimental studies are carried out to verify the machining performances suggested by this approach. Feed forward back propagation neural network (FFNN) is found to be the best network type on the selected dataset. Two hidden layer 5–6–6–4 FFNN showed the most precise and generalized network architecture with very good prediction accuracy. The proposed approach, OrthoANN, reduced ANN experimentation time by a large scale and produced viable results for machining optimization when integrated with multiGA.

Item Type:Article
Uncontrolled Keywords:optimization, orthogonal array, regression
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:87662
Deposited By: Yanti Mohd Shah
Deposited On:30 Nov 2020 20:57
Last Modified:30 Nov 2020 20:57

Repository Staff Only: item control page