Universiti Teknologi Malaysia

About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Control theory. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

Prediction of surface roughness in the end milling machining using Artificial Neural Network

Abstract: This paper presents the ANN model for predicting the surface roughness performance measure in the machining process by considering the Artificial Neural Network (ANN) as the essential technique for measuring surface roughness. A revision of several previous studies associated with the modelling issue is carried out to assess how capable ANN is as a technique to model the problem. Based on the studies conducted by previous researchers, the abilities and limitations of the ANN technique for predicting surface roughness are highlighted. Utilization of ANN-based modelling is also discussed to show the required basic elements for predicting surface roughness in the milling process. In order to investigate how capable the ANN technique is at estimating the prediction value for surface roughness, a real machining experiment is referred to in this study. In the experiment, 24 samples of data concerned with the milling operation are collected based on eight samples of data of a two-level DOE 2^k full factorial analysis, four samples of centre data, and 12 samples of axial data. All data samples are tested in real machining by using uncoated, TiAIN coated and SN"T"R coated cutting tools of titanium alloy (Ti-6A1-4V). The Matlab ANN toolbox is used for the modelling purpose with some justifications. Feedforward backpropagation is selected as the algorithm with traingdx, learngdx, MSE, logsig as the training, learning, performance and transfer functions, respectively. With three nodes in the input layer and one node in the output layer, eight networks are developed by using different numbers of nodes in the hidden layer which are 3-1-1, 3-3-1, 3-6-1, 3-7-1, 3-1-1-1, 3-3-3-1, 3-6-6-1 and 3-7-7-1 structures. It was found that the 3-1-1 network structure of the SN"T"R coated cutting tool gave the best ANN model in predicting the surface roughness value. This study concludes that the model for surface roughness in the milling process could be improved by modifying the number of layers and nodes in the hidden layers of the ANN network structure, particularly for predicting the value of the surface roughness performance measure. As a result of the prediction, the recommended combination of cutting conditions to obtain the best surface roughness value is a high speed with a low feed rate and radial rake angle.

Green Synthesis of Magnetite (Fe3O4) Nanoparticles Using Seaweed (Kappaphycus alvarezii) Extract.

Abstract: In this study, a simple, rapid, and eco-friendly green method was introduced to synthesize magnetite nanoparticles (Fe3O4-NPs) successfully. Seaweed Kappaphycus alvarezii (K. alvarezii) was employed as a green reducing and stabilizing agents. The synthesized Fe3O4-NPs were characterized with X-ray diffraction (XRD), ultraviolet-visible spectroscopy (UV-Vis), Fourier transform infrared (FT-IR), and transmission electron microscopy (TEM) techniques. The X-ray diffraction planes at (220), (311), (400), (422), (511), (440), and (533) were corresponding to the standard Fe3O4 patterns, which showed the high purity and crystallinity of Fe3O4-NPs had been synthesized. Based on FT-IR analysis, two characteristic absorption peaks were observed at 556 and 423 cm−1, which proved the existence of Fe3O4 in the prepared nanoparticles. TEM image displayed the synthesized Fe3O4-NPs were mostly in spherical shape with an average size of 14.7 nm.

Insight into the 2004 Sumatra–Andaman earthquake from GPS measurements in southeast Asia

Abstract: Data collected at approximately 60 Global Positioning System (GPS) sites in southeast Asia show the crustal deformation caused by the 26 December 2004 Sumatra-Andaman earthquake at an unprecedented large scale. Small but significant co-seismic jumps are clearly detected more than 3,000 km from the earthquake epicentre. The nearest sites, still more than 400 km away, show displacements of 10 cm or more. Here we show that the rupture plane for this earthquake must have been at least 1,000 km long and that non-homogeneous slip is required to fit the large displacement gradients revealed by the GPS measurements. Our kinematic analysis of the GPS recordings indicates that the centroid of released deformation is located at least 200 km north of the seismological epicentre. It also provides evidence that the rupture propagated northward sufficiently fast for stations in northern Thailand to have reached their final positions less than 10 min after the earthquake, hence ruling out the hypothesis of a silent slow aseismic rupture.

Standardization and Its Effects on K-Means Clustering Algorithm

Abstract: Data clustering is an important data exploration technique with many applications in data mining. K- means is one of the most well known methods of data mining that partitions a dataset into groups of patterns, many methods have been proposed to improve the performance of the K-means algorithm. Standardization is the central preprocessing step in data mining, to standardize values of features or attributes from different dynamic range into a specific range. In this paper, we have analyzed the performances of the three standardization methods on conventional K-means algorithm. By comparing the results on infectious diseases datasets, it was found that the result obtained by the z-score standardization method is more effective and efficient than min-max and decimal scaling standardization methods.