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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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International Journal of Engineering, Science and Technology
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2141-2839
Published by African Journals Online Homepage  [261 journals]
  • Effect of heat treatment and ageing on microstructure for hypoeutectic
           Al-7Si alloy and hybrid metal matrix composites

    • Authors: B.M. Viswanatha, M. Prasanna Kumar, S. Basavarajappa, T.S. Kiran, S. Kanchiraya
      Pages: 1 - 11
      Abstract: In the present investigation on fabrication and microstructure of aluminium based hybrid composites. A356 based aluminium matrix material with varying reinforcement percentage from 0 to 9 wt. % in steps of 3 wt. % silicon carbide (SiCp) and fixed quantity of 3 wt. % of graphite (Gr) particles were used in fabrication. The specimens were fabricated by stir-cast method. Heat treatment was carried out for the cast specimen at 540oC for 12 hours and further ageing was carried out at 155ºC for 3, 6, 9 and 12 hours durations. The specimen after heat treatment and ageing were quenched in water at 60oC. The prepared specimens (ascast and aged) were examined using optical microscope to know the particle distribution in the matrix. Hardness and tensile were carried out for as-cast and aged specimen. The results were compared with as-cast and aged specimens. There was a significant improvement in hardness and tensile properties due to increase in the weight percentage of SiCp. The specimen A356-9SiCp-3Gr aged at 9 hrs showed improved hardness, and tensile when compared to other tested specimen. The presence of reinforcements (SiCp and Gr) significantly affects the solid state transition kinetics that improves the properties of composites. The presences of reinforcements in the specimens are evident from the electron dispersive spectroscopy (EDS) analysis. 
      PubDate: 2022-05-30
      DOI: 10.4314/ijest.v13i4.1
      Issue No: Vol. 13, No. 4 (2022)
       
  • Fault diagnosis of SKF-6205 bearing with modified empirical mode
           decomposition

    • Authors: Ankit Darji, Divyang Pandya
      Pages: 12 - 20
      Abstract: Rolling element bearings are broadly used in the rotating machines to support static and dynamic loads. In this research, the advance signal processing techniques are use for processing of bearing fault signals. Experimental validation with genuine vibration signals calculated from bearings with seeded defects on bearing elements. The model-based fault diagnosis method has attempted to diagnose incipient fault detection and classification of bearing with data driven approach. Feature extraction technique has been developed with hybrid signal processing technique based on the band pass filter nature of Empirical mode decomposition (EMD), the resonant frequency bands have owed in specific mono component signals called Intrinsic Mode Functions (IMFs). Synchronized resonant frequency band (SRFB) is obtained on based of orthogonal real wavelet using spectral kurtosis. Biorthogonal 5.5 wavelet, a real wavelet has been selected as a suitable wavelet for WPT based on “Maximum Relative Wavelet Energy” and “Maximum Energy-to-Shannon entropy ratio”. Three, Feature extraction techniques like continuous wavelet transform (CWT), wavelet packet transform (WPT) and modified Hilbert Huang Transforms (HHT) are compared on bases of their classification accuracy with different classification algorithm and filters. Various supervised classifiers have been compared through a common platform of Waikato Environment for Knowledge Analysis (WEKA) and concluded the k- nearest neighbour (KNN) as an effective available classifier for rolling element bearing. Further, asymmetric proximity function based KNN (APF-KNN) has out performs with modified feature selection criteria. Feature extraction through modified HHT and APFKNN has been future as a most effectual fault classification method. For testing any unknown data, simplified method has been demonstrated, which make the proposed data driven approach more realistic, faster and automated. 
      PubDate: 2022-05-30
      DOI: 10.4314/ijest.v13i4.2
      Issue No: Vol. 13, No. 4 (2022)
       
  • Heavy metals and essential elements in table salt extracted from Bahi
           wetlands in Central Tanzania

    • Authors: Innocent J. Lugendo, John Bugumba
      Pages: 21 - 31
      Abstract: Salt from Bahi wetlands is allegedly containing high concentrations of heavy metals since the wetlands are in the proximity of the prospective uranium mining sites. This means Bahi salt could be an important route through which salt consumers are exposed to high concentrations of heavy metals. This study has analysed 50 salt samples of Bahi salt in order to determine the concentrations of both heavy metals and essential elements using the Energy Dispersive X-Ray Fluorescence Spectrometry (EDXRF). Analytical results show that apart from Na and Cl, Bahi salt is composed of several elements including heavy metals such as Cr, Cd, Pb, Th and U. The salt also contains essential elements such as Mg, K, Ca, Mn, Fe, Co, Zn and Br. The mean concentrations of Cr, Cd, Pb, Th and U ranged from 20 - 25μg/g, 3.8 – 8.85 μg/g, 2.29 – 5.8 μg/g, 6.22 – 15.8 μg/g and 6.5 – 9.12 μg/g respectively. All these toxic elements were in higher concentrations than the recommended maximum tolerable limits (MTL). Meanwhile the daily intake rate of essential elements due consumption of Bahi salt leads to the hazard index (HI) greater than 1 for children. This implies that unless purified, Bahi salt may be unsafe for children. Thus, both salt producers and responsible authorities at Bahi wetlands should collaborate to introduce suitable production methods ensuring effective salt purification before it reaches its consumers.
      PubDate: 2022-05-30
      DOI: 10.4314/ijest.v13i4.3
      Issue No: Vol. 13, No. 4 (2022)
       
  • An innovative approach to classify hierarchical remarks with multi-class
           using BERT and customized na├»ve bayes classifier

    • Authors: M.M. Dhina, S. Sumathi
      Pages: 32 - 45
      Abstract: Text classification is the process of grouping text into distinct categories. Text classifiers may automatically assess text input and allocate a set of pre-defined tags or categories depending on its content or a pre-trained model using Natural Language Processing (NLP), which actually is a subset of Machine Learning (ML). The notion of text categorization is becoming increasingly essential in enterprises since it helps firms to get ideas from facts and automate company operations, lowering manual labor and expenses. Linguistic Detectors (the technique of determining the language of a given document), Sentiment Analysis (the process of identifying whether a text is favorable or unfavorable about a given subject), Topic Detection (determining the theme or topic of a group of texts), and so on are common applications of text classification in industry. The nature of the dataset is Multi-class and multi-hierarchical, which means that the hierarchies are in multiple levels, each level of hierarchy is multiple class in nature. One of ML’s most successful paradigms is supervised learning from which one can build a generalization model. Hence, a custom model is built, so that the model fits with the problem. Deep learning (DL), part of Artificial Intelligence (AI) , does functions that replicate the human brain's data processing capabilities in order to identify text or artifacts, translate languages, detect voice, draw conclusions and so on. Bidirectional Encoder Representations from Transformers (BERT), a Deep Learning Algorithm performs an extra-ordinary task in NLP text classification and results in high accuracy. Therefore, BERT is combined with the Custom Model developed and compared with the native algorithm to ensure the increase in accuracy rates.
      PubDate: 2022-05-30
      DOI: 10.4314/ijest.v13i4.4
      Issue No: Vol. 13, No. 4 (2022)
       
  • CFD analysis of the low Reynolds S1223 airfoil

    • Authors: E. Mollica, A. Timmoneri
      Pages: 46 - 49
      Abstract: The Low Reynolds S1223 airfoil has been modeled and performance has been evaluated numerically through CFD open source OpenFOAM suite. The numerical results have been validated with available experimental data at Re = 2e05 obtained by means of wind-tunnel tests. Results are also reported at Re = 2e04 and Re = 2.06 in terms of lift and coefficients, streamlines, pressure coefficient and velocity distributions, at 3 different AOA. The main original aspect of this numerical research has been the sensitivity analysis of the aerodynamic performance of the S1223 airfoil over a wide range of Reynolds number. In particular, the effects at low Reynolds number 2e04 have been investigated.
      PubDate: 2022-05-30
      DOI: 10.4314/ijest.v13i4.5
      Issue No: Vol. 13, No. 4 (2022)
       
  • Basalt fiber and its composite manufacturing and applications: An overview

    • Authors: Ashokkumar R. Tavadi, Yuvaraja Naik, K. Kumaresan, N.I. Jamadar, C. Rajaravi
      Pages: 50 - 56
      Abstract: Basalt fiber is a low cost materiel obtained from the Basalt rock from earth, and it can be divided into small pieces to form fiber. In this paper dealing with Basalt fiber manufacturing, property of composites and application are reviewed. The results indicate that as compared to glass fibers, carbon fibers and armide fiber, basalt fiber possesses excellent machine driven properties such as wave permeability, electrical properties, non-conductive and insulation properties. BFRP has higher flexural strength and better adhesion as related to CFRP and GFRP. When temperature increases thermal and mechanical properties of BFRP reduces considerable. Due to the above advantages, the addition of Basalt fiber and its composites can be used in sound and sound absorption and thermal insulation application.
      PubDate: 2022-05-30
      DOI: 10.4314/ijest.v13i4.6
      Issue No: Vol. 13, No. 4 (2022)
       
  • Mechanical properties of geopolymer concrete with varying cement content
           using flyash and ground granulated blast furnace slag

    • Authors: P. Ashveenkumar, M. Preethi, P. Prashanth
      Pages: 57 - 64
      Abstract: In the recent past, the importance of geopolymer concrete as an eco-friendly product to replace portland cement concrete is continuously increasing over time. Yet less research effort has been invested in this area compared with some topical issues in civil engineering. Thus, the objective of this article is to analyse the mechanical properties of geopolymer concrete where the cement is replaced by fly ash and ground granulated blast-furnace slag (GGBS). Sodium silicate and sodium hydroxide 8 molarity solution was used. The compressive strength of a cube in an 8 molarity solution was measured for various mixtures (i.e. G50F50 where G and F stand for GGBS and flyash, respectively while the numerical value denotes the cement percentage) and the cement contents (i.e. 0, 10, 20, 30, 40%). The cube specimens are 100mmx100mmx100mm with the ambient curing at 35- 400C. In total, 9 cubes, 3 beams and 3 cylinders are cast at 7days, 14days and 28days while the compressive strengths of different mixes and cubes are calculated. For 28days, beams and cylinders are measured for flexural and tensile strength. The compressive strength at 7,14 and 28 days nearly doubled the target strength by using geopolymer concrete instead of normal concrete. Compressive strength is about 10% higher at 7 and 14days and 20% higher at 28days after replacing 40% of the cement. Flexural strength increased by 50% when 40% of the cement was replaced but split tensile strength only increased by 1%.
      PubDate: 2022-05-30
      DOI: 10.4314/ijest.v13i4.7
      Issue No: Vol. 13, No. 4 (2022)
       
 
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