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Sains Malaysiana 42(12)(2013): 1735–1741
Prediction of Tool Life in End Milling of Ti-6Al-4V Alloy
Using
Artificial Neural Network and Multiple Regression Models
(Ramalan Hayat Mata Alat dalam Kisar Hujung AloiTi-6Al-4V Menggunakan
Rangkaian Neural Tiruan dan
Model Regresi Pelbagai)
SALAH AL-ZUBAIDI*, JAHARAH A. GHANI & CHE HASSAN CHE HARON
Department of Mechanical and Material Engineering, Faculty
of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi,
Selangor, Malaysia
Received: 22 March 2012/Accepted: 19 May 2012
ABSTRACT
Tool life of the cutting tools is considered as one of the factors which has effects on machining costs and the quality
of machined parts. The topic of tool life prediction has been an interesting
and important research topic attracting the attention of a wide number of
researchers in this particular area. In terms of the suitable methods used in
this research topic, it is stated that both statistical and artificial
intelligence (AI)
approaches can be employed to model tool life. For further justifying the
capability of the ANN model in predicting tool life, the current
study was based on conducting experimental work for collecting the experimental
data. After carrying out the experiment, 17 data sets were collected and they
were divided into two subsets; the first one for training and the second for
testing. Since the data sets seemed to be lower than the number of data sets
used in previous studies, we attempted to make verification of the ability of
the ANN model in learning and adapting with low training and testing
data. Diverse topologies accompanied with single and two hidden layers were
created for modeling the tool life. For choosing the best and most effective
network, the study adopted the mean square error function as criteria for the
evaluation of the network selection. Thus, based on the data generated from the
same experiment, a regression model (RM) was constructed employing the SPSS software.
A comparison between the ANN model and RMs in terms of their
accuracy was carried out and the findings revealed that the accuracy of the ANN was
higher than that of the RM.
Keywords: Artificial neural network; prediction; tool life;
uncoated carbide
ABSTRAK
Salah satu faktor yang memberi kesan terhadap kos pemesinan dan kualiti produk yang dimesin. Topik mengenai ramalan hayat mata alat sangat menarik dan merupakan kajian yang penting dan menarik perhatian sebahagian jumlah penyelidik dalam bidang ini. Kaedah yang sesuai digunakan dalam kajian ini ialah statistik dan pintar buatan bagi memodelkan hayat mata alat. Bagi justifikasi keupayaan model ANN dalam ramalan hayat mata alat, kajian ini berdasarkan kepada membuat kerja eksperimen untuk pengumpulan data. Selepas menjalankan eksperimen, 17 set
data telah dikumpulkan dan dibahagikan kepada dua subset data; pertama untuk latihan dan kedua untuk ujian. Disebabkan set data agak rendah berbanding kajian sebelum ini, keupayaan model ANN dikaji dalam pembelajaran dan adaptasi dengan data latihan dan ujian yang rendah. Topologi yang besar berserta satu dan dua lapis tersorok telah direka bagi memodelkan hayat mata alat. Bagi memilih rangkaian terbaik dan paling berkesan, kajian ini menggunakan fungsi min ralat kuasa dua sebagai kriteria untuk penilaian rangkaian yang dipilih. Oleh itu, berdasarkan data yang dijana daripada eksperimen, model regresi (RM) telah dibangunkan menggunakan perisian SPSS. Perbandingan kejituan antara model ANN dan RMS telah dibuat, dan hasil kajian menunjukkan kejituan model ANN lebih tinggi berbanding dengan RM.
Kata kunci: Hayat mata alat; karbida tak bersalut; ramalan; rangkaian neural tiruan
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*Corresponding author; email: salah@eng.ukm.my
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