Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 64
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

Artificial neural network (ANN), a Computational tool that is frequently applied in the modeling and simulation of manufacturing processes. The emerging forming technique of sheet metal which is typically called single point incremental forming (SPIF) comes into the map and the research interest towards its technological parameters. The surface quality of the end product is a major issue in SPIF, which is more critical with the hard metals. The part of the brass metal is demanded in many industrial uses because of its high load-carrying capacity and its wear resistance property. Considering the industrial interest and demand of the brass metal products, the present study is done with the SPIF experiment on calamine brass Cu67Zn33 followed by an ANN analysis for predicting the absolute surface roughness. The modeling result shows a close agreement with the measured data. The minimum and maximum errors are found in experiment 3 and experiment 7 respectively. The error of predicted roughness is found in the range of –30.87 to 20.23 and the overall coefficient of performance of ANN modeling is 0.947 which is quite acceptable.
Go to article

Authors and Affiliations

Manish Oraon
1
Vinay Sharma
1

  1. Birla Institute of Technology, Faculty of Production Engineering, India
Download PDF Download RIS Download Bibtex

Abstract

One of the basic parameters which describes road traffic is Annual Average Daily Traffic (AADT). Its accurate determination is possible only on the basis of data from the continuous measurement of traffic. However, such data for most road sections is unavailable, so AADT must be determined on the basis of short periods of random measurements. This article presents different methods of estimating AADT on the basis of daily traffic (VOL), and includes the traditional Factor Approach, developed Regression Models and Artificial Neural Network models. As explanatory variables, quantitative variables (VOL and the share of heavy vehicles) as well as qualitative variables (day of the week, month, level of AADT, the cross-section, road class, nature of the area, spatial linking, region of Poland and the nature of traffic patterns) were used. Based on comparisons of the presented methods, the Factor Approach was identified as the most useful.

Go to article

Authors and Affiliations

M. Spławińska
Download PDF Download RIS Download Bibtex

Abstract

This paper presents a new test method able to infer - in periods of less than 7 seconds - the refrigeration capacity of a compressor used in thermal machines, which represents a time reduction of approximately 99.95% related to the standardized traditional methods. The method was developed aiming at its application on compressor manufacture lines and on 100% of the units produced. Artificial neural networks (ANNs) were used to establish a model able to infer the refrigeration capacity based on the data collected directly on the production line. The proposed method does not make use of refrigeration systems and also does not require using the compressor oil.
Go to article

Authors and Affiliations

Rodrigo Coral
Carlos A. Flesch
Cesar A. Penz
Maikon R. Borges
Download PDF Download RIS Download Bibtex

Abstract

Journal bearings are the most common type of bearings in which a shaft freely rotates in a metallic sleeve. They find a lot of applications in industry, especially where extremely high loads are involved. Proper analysis of the various bearing faults and predicting the modes of failure beforehand are essential to increase the working life of the bearing. In the current study, the vibration data of a journal bearing in the healthy condition and in five different fault conditions are collected. A feature extraction method is employed to classify the different fault conditions. Automatic fault classification is performed using artificial neural networks (ANN). As the probability of a correct prediction goes down for a higher number of faults in ANN, the method is made more robust by incorporating deep neural networks (DNN) with the help of autoencoders. Training was done using the scaled conjugate gradient algorithm and the performance was calculated by the cross entropy method. Due to the increased number of hidden layers in DNN, it is possible to achieve a high efficiency of 100% with the feature extraction method.

Go to article

Authors and Affiliations

T. Narendiranath Babu
Arun Aravind
Abhishek Rakesh
Mohamed Jahzan
D. Rama Prabha
Mangalaraja Ramalinga Viswanathan
Download PDF Download RIS Download Bibtex

Abstract

Artificial neural networks are one of the modern methods of the production optimisation. An attempt to apply neural networks for controlling the quality of bentonite moulding sands is presented in this paper. This is the assessment method of sands suitability by means of detecting correlations between their individual parameters. The presented investigations were aimed at the selection of the neural network able to predict the active bentonite content in the moulding sand on the basis of this sand properties such as: permeability, compactibility and the compressive strength. Then, the data of selected parameters of new moulding sand were set to selected artificial neural network models. This was made to test the universality of the model in relation to other moulding sands. An application of the Statistica program allowed to select automatically the type of network proper for the representation of dependencies occurring in between the proposed moulding sand parameters. The most advantageous conditions were obtained for the uni-directional multi-layer perception (MLP) network. Knowledge of the neural network sensitivity to individual moulding sand parameters, allowed to eliminate not essential ones.
Go to article

Authors and Affiliations

St.M. Dobosz
J. Jakubski
K. Major-Gabryś
Download PDF Download RIS Download Bibtex

Abstract

The article presents the prototype of a measurement system with a hot probe, designed for testing thermal parameters of heat insulation materials. The idea is to determine parameters of thermal insulation materials using a hot probe with an auxiliary thermometer and a trained artificial neural network. The network is trained on data extracted from a nonstationary two-dimensional model of heat conduction inside a sample of material with the hot probe and the auxiliary thermometer. The significant heat capacity of the probe handle is taken into account in the model. The finite element method (FEM) is applied to solve the system of partial differential equations describing the model. An artificial neural network (ANN) is used to estimate coefficients of the inverse heat conduction problem for a solid. The network determines values of the effective thermal conductivity and effective thermal diffusivity on the basis of temperature responses of the hot probe and the auxiliary thermometer. All calculations, like FEM, training and testing processes, were conducted in the MATLAB environment. Experimental results are also presented. The proposed measurement system for parameter testing is suitable for temporary measurements in a building site or factory.

Go to article

Authors and Affiliations

Stanisław Chudzik
Waldemar Minkina
Download PDF Download RIS Download Bibtex

Abstract

When an artificial neural network is used to determine the value of a physical quantity its result is usually presented without an uncertainty. This is due to the difficulty in determining the uncertainties related to the neural model. However, the result of a measurement can be considered valid only with its respective measurement uncertainty. Therefore, this article proposes a method of obtaining reliable results by measuring systems that use artificial neural networks. For this, it considers the Monte Carlo Method (MCM) for propagation of uncertainty distributions during the training and use of the artificial neural networks.

Go to article

Authors and Affiliations

Rodrigo Coral
Carlos A. Flesch
Cesar A. Penz
Mauro Roisenberg
Antonio L.S. Pacheco
Download PDF Download RIS Download Bibtex

Abstract

The purpose of this paper is to compare two approaches applied in property valuation: artificial neural networks and spatial regression. Despite the fact that artificial neural networks are often the first choice for modeling in the big data era, spatial econometrics methods offer incorporation of information on dependences between multiple objects in the studied space. Although this dependency structure can be incorporated into artificial neural network via feature engineering, this study is focused on abilities of reproducing it with machine learning method from crude coordinate data. The research is based on the database of 18,166 property sale transactions in Warsaw, Poland. According to this study, such volume of data does not allow artificial neural networks to compete in reflecting spatial dependence structure with spatial regression models.
Go to article

Authors and Affiliations

Damian Przekop
1

  1. Warsaw School of Economics, Poland
Download PDF Download RIS Download Bibtex

Abstract

The deformation properties of rocks play a crucial role in handling most geomechanical problems. However, the determination of these properties in laboratory is costly and necessitates special equipment. Therefore, many attempts were made to estimate these properties using different techniques. In this study, various statistical and soft computing methods were employed to predict the tangential Young Modulus (Eti, GPa) and tangential Poisson’s Ratio (vti) of coal measure sandstones located in Zonguldak Hardcoal Basin (ZHB), NW Turkey. Predictive models were established based on various regression and artificial neural network (ANN) analyses, including physicomechanical, mineralogical, and textural properties of rocks. The analysis results showed that the mineralogical features such as the contents of quartz (Q, %) and lithic fragment (LF, %) and the textural features (i.e., average grain size, d50, and sorting coefficient, Sc) have remarkable impacts on deformation properties of the investigated sandstones. By comparison with these features, the mineralogical effects seem to be more effective in predicting the Eti and vti. The performance of the established models was assessed using several statistical indicators. The predicted results from the proposed models were compared to one another. It was concluded that the empirical models based on the ANN were found to be the most convenient tools for evaluating the deformational properties of the investigated sandstones.
Go to article

Bibliography

[1] K . Zorlu, C. Gökçeoglu, F. Ocakoglu, H.A. Nefeslioglu, S. Acikalin, Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng. Geol. 96, 141-158 (2008). DOI : https://doi.org/10.1016/j.enggeo.2007.10.009
[2] N . Ceryan, Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks. J. African Earth. Sci. 100, 634-644 (2014). DOI : https://doi.org/10.1016/j.jafrearsci.2014.08.006
[3] A. Shakoor, R.E. Bonelli, Relationship between petrographic characteristics, engineering index properties, and mechanical properties of selected sandstones. Environ. Eng. Geosci. 28, 55-71 (1991). DOI : https://doi.org/10.2113/gseegeosci.xxviii.1.55
[4] A. Ersoy, M.D. Waller, Textural characterisation of rocks. Eng. Geol. 39, 123-136 (1995). DOI : https://doi.org/10.1016/0013-7952(95)00005-Z
[5] F.G. Bell, P. Lindsay, The petrographic and geomechanical properties of some sandstones from the Newspaper Member of the Natal Group near Durban, South Africa. Eng. Geol. 53, 57-81 (1999). DOI : https://doi.org/10.1016/S0013-7952(98)00081-7
[6] R. Prikryl, Assessment of rock geomechanical quality by quantitative rock fabric coefficients: limitations and possible source of misinterpretations. Eng. Geol. 87, 149-162 2006. DOI : https://doi.org/10.1016/j.enggeo.2006.05.011
[7] J.S. Coggan, D. Stead, J.H. Howe, C.I Faulks, Mineralogical controls on the engineering behavior of hydrothermally altered granites under uniaxial compression. Eng. Geol. 160, 89-102 (2013). DOI : https://doi.org/10.1016/j.enggeo.2013.04.001
[8] C .A. Ozturk, E. Nasuf, S. Kahraman, Estimation of rock strength from quantitative assessment of rock texture. Journal of the Southern African Institute of Mining and Metallurgy 114 (6), 471-480 (2014).
[9] E. Ali, W. Guang, A. Ibrahim, Microfabrics-Based Approach to Predict Uniaxial Compressive Strength of Selected Amphibolites Schists Using Fuzzy Inference and Linear Multiple Regression Techniques, Environ. Eng. Geosci. 21 (3), 235-245 (2015). DOI: https://doi.org/10.2113/gseegeosci.21.3.235
[10] X.A. Cabria, Effects of weathering in the rock and rock mass properties and the influence of salts in the coastal roadcuts in Saint Vincent and Dominica. Master Thesis, Twente University, (2015).
[11] N .Q.A.M. Yusof, H. Zabidi, Correlation of Mineralogical and Textural Characteristics with Engineering Properties of Granitic Rock from Hulu Langat, Selangor. Procedia Chemistry 19, 975-980 (2016). DOI : https://doi.org/10.1016/j.proche.2016.03.144
[12] E. Köken A. Özarslan, G. Bacak, Weathering effects on physical properties and material behavior of granodiorite rocks. In: Rock Mechanics and Rock Engineering – From the past to the future Ulusay et al. (Eds), ISRM International Symposium, EUROCK 2016, 331-336 (2016).
[13] T.K. Koca, M.Y. Koca, Classification of weathered andesitic rock materials from the İzmir Subway line on the basis of strength and deformation. Bull. Eng. Geol. Environ. 78, 3575-3592 (2019). DOI : https://doi.org/10.1007/s10064-018-1346-y
[14] M.N. Bidgoli, Z. Zhao, L. Jing, Numerical evaluation of strength and deformability of fractured rocks. Rock Mech. and Geotech. Eng. 5, 419-430 (2013). DOI: https://doi.org/10.1016/j.jrmge.2013.09.002
[15] H. Xu, W. Zhou, R. Xie, L. Da, C. Xiao, Y. Shan, H. Zhang, Characterization of Rock Mechanical Properties Using Lab Tests and Numerical Interpretation Model of Well Logs. Math. Prob. Eng. 5967159, (2016). DOI : https://doi.org/10.1155/2016/5967159
[16] J. Shu, L. Jiang, P. Kong, Q. Wang, Numerical Analysis of the Mechanical Behaviors of Various Jointed Rocks under Uniaxial Tension Loading. Appl. Sci. 9, 1824 (2019). DOI: https://doi.org/10.3390/app9091824
[17] P. Davy, C. Darcel, R. Le Goc, D. Mas Ivars, Elastic Properties of Fractured Rock Masses With Frictional Properties and Power Law Fracture Size Distributions. J. Geophys. Res. 123 (8), 6521-6539 (2018). DOI : https://doi.org/10.1029/2017JB015329
[18] M. Babaeian, M. Ataei, F. Sereshki, F. Sotoudeh, A new framework for evaluation of rock fragmentation in open pit mines. Rock Mech. Geotech. Eng. 11 (2), 325-336 (2019). DOI : https://doi.org/10.1016/j.jrmge.2018.11.006
[19] A.A. Mahmoud, S. Elkatatny, D.A. Shehri, Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations. Sustainability 12, 1880 (2020). DOI: https://doi.org/10.3390/su12051880
[20] D . Lv, Z. Li, J. Chen, H. Liu, J. Guo, L. Shang, Characteristics of the Permian coal-formed gas sandstone reservoirs in Bohai Bay Basin and the adjacent areas. North China, Petrol. Sci. Eng. 78 (2), 516-528, (2011). DOI : https://doi.org/10.1016/j.petrol.2011.06.018
[21] A. Fan, R. Yang, N. Lenhardt, M. Wang, Z. Han, J. Li, Y. Li, Z. Zhao, Cementation and porosity evolution of tight sandstone reservoirs in the Permian Sulige gasfield, Ordos Basin (central China). Marine Petrol. Geol. 103, 276-293 (2019). DOI: https://doi.org/10.1016/j.marpetgeo.2019.02.010
[22] P. Tan, Y. Jin, L. Yuan, et al., Understanding hydraulic fracture propagation behavior in tight sandstone – coal interbedded formations: an experimental investigation. Pet. Sci. 16, 148-160 (2019). DOI : https://doi.org/10.1007/s12182-018-0297-z
[23] D .G. Roy, T.N. Singh, Predicting deformational properties of Indian coal: Soft computing and regression analysis approach. Measurement 149, 106975 (2020). DOI: https://doi.org/10.1016/j.measurement.2019.106975
[24] R. Koch, R. Sobott, Sandsteine: Entstehung, Eigenschaften, Verwitterung, Konservierung, Restaurierung. In: Siegesmund, Snethlage (eds) Schriftenreihe der Deutschen Gesellschaft für Geowissenschaften 59, 145-174 (2008).
[25] J. Rüdrich, T. Bartelsen, R. Dohrmann, S. Siegesmund, Moisture expansion as a deterioration factor for sandstone used in buildings. Environ. Earth Sci. 63, 1545-1564 (2010). DOI: https://doi.org/10.1007/s12665-010-0767-0
[26] F.J. Pettijohn, Sand and sandstone, Springer-Verlag Berlin, (1973). e-ISBN: 978-1-4615-9974-6
[27] J.R.L Allen, Petrology, origin and deposition of the highest Lower Old Red sandstone of Shropshire, England. J. Sedimen. Res. 32 (4), 657-697 (1962).
[28] D .F. Howarth, J.C. Rowlands, Quantitative assessment of rock texture and correlation with drillability and strength properties. Rock Mech. Rock Eng. 20, 57-85 (1987). DOI: https://doi.org/10.1007/BF01019511
[29] A. Azzoni, F. Bailo, E. Rondena, et al., Assessment of texture coefficient for different rock types and correlation with uniaxial compressive strength and rock weathering. Rock. Mech. Rock. Eng. 29, 39-46 (1996). DOI : https://doi.org/10.1007/BF01019938
[30] M. Alber, S. Kahraman, Predicting the uniaxial compressive strength and elastic modulus of a fault breccia from texture coefficient. Rock Mech. Rock. Eng. 42, 117-127 (2009). DOI : https://doi.org/10.1007/s00603-008-0167-x
[31] F. Arıkan R. Ulusay, N. Aydın, Characterization of weathered acidic volcanic rocks and a weathering classification based on a rating system. Bull. Eng. Geol. Environ. 66, 415-430 (2007). DOI : https://doi.org/10.1007/s10064-007-0087-0
[32] Ö. Ündül, A. Tuğrul, On the variations of geoengineering properties of dunites and diorites related to weathering. Environ. Earth Sci. 75, 1326 (2016). DOI: https://doi.org/10.1007/s12665-016-6152-x
[33] E. Köken, S. Top, A. Özarslan, Assessment of Rock Aggregate Quality Through the Analytic Hierarchy Process (AHP). Geotech. Geol. Eng. 38, 5075-5096 (2020). DOI: https://doi.org/10.1007/s10706-020-01349-8
[34] R.H.C. Wong, K.T. Chau, P. Wang, Microcracking and grain size effect in Yuen Long Marbles. Int. J. Rock Mech. Min. Sci. Geomech. Abstr. 33 (5), 479-485 (1996). DOI: https://doi.org/10.1016/0148-9062(96)00007-1
[35] Y.H. Hatzor, V. Palchik, The influence of grain size and porosity on crack initiation stress and critical flaw length in dolomites. Int. J .Rock Mech. Min. Sci. 34 (5), 805-816 (1997). DOI : https://doi.org/10.1016/S1365-1609(96)00066-6
[36] A. Tugrul, I.H. Zarif, Correlation of mineralogical and textural characteristics with engineering properties of selected granitic rocks from Turkey. Eng. Geol. 51 (4), 303-317 (1999). DOI : https://doi.org/10.1016/S0013-7952(98)00071-4
[37] E. Eberhardt, B. Stimpson, D. Stead, Effects of grain size on the initiation and propagation thresholds of stressinduced brittle fractures. Rock Mech. Rock Eng. 32, 81-99 (1999). DOI : https://doi.org/10.1007/s006030050026
[38] R. Přikryl, Some microstructural aspects of strength variation in rocks. Int. J. Rock Mech. Min. Sci. 38 (5), 671-682 (2001). DOI: https://doi.org/10.1016/S1365-1609(01)00031-4
[39] M. Cai, P.K. Kaiser, Y. Tasaka, T. Maejima, H. Morioka, M. Minami, Generalized crack initiation and crack damage stress thresholds of brittle rock masses near underground excavations. Int. J. Rock Mech. Min. Sci. 41 (5), 833-847 (2004). DOI: https://doi.org/10.1016/j.ijrmms.2004.02.001
[40] M. Nicksiar, C.D. Martin, Crack initiation stress in low porosity crystalline and sedimentary rocks. Eng. Geol. 154, 64-76 (2013). DOI: https://doi.org/10.1016/j.enggeo.2012.12.007
[41] E. Köken, Investigations on Fracture Evolution of Coal Measure Sandstones from Mineralogical and Textural Points of View. Indian Geotech. J. 50, 1024-1040 (2020). DOI: https://doi.org/10.1007/s40098-020-00427-1
[42] N . Yesiloglu-Gultekin, E.A. Sezer, C. Gokceoglu, H. Bayhan, An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents. Expert Sys. App. 40 (3), 921-928 (2013). DOI: https://doi.org/10.1016/j.eswa.2012.05.048
[43] N .F. Hassan, O.A. Jimoh, S.A. Shehu, Z. Hareyani, The effect of mineralogical composition on strength and drillability of granitic rocks in Hulu Langat, Selangor Malaysia. Geotech. Geol. Eng. 37, 5499-5505 (2019). DOI : https://doi.org/10.1007/s10706-019-00995-x
[44] R.S. Tandon, V. Gupta, The control of mineral constituents and textural characteristics on the petrophysical & mechanical (PM) properties of different rocks of the Himalaya. Eng. Geol. 153, 125-143 (2013). DOI : https://doi.org/10.1016/j.enggeo.2012.11.005
[45] M. Rӓisӓnen, Relationships between texture and mechanical properties of hybrid rocks from the Jaala-Iitti complex, southeastern Finland. Eng. Geol. 74, 197-211 (2004). DOI: https://doi.org/10.1016/j.enggeo.2004.03.009
[46] E. Cantisani, C.A. Garzonio, M. Ricci, S. Vettori, Relationships between the petrographical, physical and mechanical properties of some Italian sandstones. Int. J. Rock Mech. Min. Sci. 60, 321-332 (2013). DOI : https://doi.org/10.1016/j.ijrmms.2012.12.042
[47] R. Ulusay, K. Tureli, M.H. Ider, Prediction of engineering properties of a selected litharenite sandstone from its petrographic characteristics using correlation and multivariate statistical techniques. Eng. Geol. 38 (1-2), 135-157 (1994). DOI: https://doi.org/10.1016/0013-7952(94)90029-9
[48] S. Kahraman, Evaluation of simple methods for assessing the uniaxial compressive strength of rock. Int. J. Rock Mech. Min. Sci. 38 (7), 981-994 (2001). DOI: https://doi.org/10.1016/S1365-1609(01)00039-9
[49] G.R. Lashkaripour, Predicting mechanical properties of mudrock from index parameters. Bull. Eng. Geol. Environ. 61, 73-77 (2002). DOI: https://doi.org/10.1007/s100640100116
[50] P.A. Hale, A. Shakoor, A Laboratory Investigation of the Effects of Cyclic Heating and Cooling, Wetting and Drying, and Freezing and Thawing on the Compressive Strength of Selected Sandstones. Environ. Eng. Geosci. 9 (2), 117-130 (2003). DOI: https://doi.org/10.2113/9.2.117
[51] C . Gokceoglu, H. Sonmez, K. Zorlu, Estimating the uniaxial compressive strength of some clay bearing rocks selected from Turkey by nonlinear multivariable regression and rule-based fuzzy models. Expert Systems 26 (2), 176-190 (2009). DOI: https://doi.org/10.1111/j.1468-0394.2009.00475.x
[52] M. Khandelwal, T.H. Singh, Correlating static properties of coal measures rocks with P-wave velocity. Int. J. Coal Geol. 79 (1-2), 55-60, (2009). DOI: https://doi.org/10.1016/j.coal.2009.01.004
[53] S. Dehghan, G.H Sattari, S. Chehreh Chelgani, M.A. Aliabadi, Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Min. Sci. Tech. (China), 20 (1), 41-46, (2010). DOI: https://doi.org/10.1016/S1674-5264(09)60158-7
[54] S. Yagiz, Correlation between slake durability and rock properties for some carbonate rocks. Bull. Eng. Geol. Environ. 70 (3), 377-383 (2011). DOI: https://doi.org/10.1007/s10064-010-0317-8
[55] T.N. Singh, A.K. Verma, Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks. Eng. Comput. 28, 1-12 (2012). DOI: https://doi.org/10.1007/s00366-011-0210-5
[56] M. Khandelwal, Correlating P-wave velocity with the physicomechanical properties of different rocks. Pure Appl. Geophys. 170, 507-514 (2013). DOI: https://doi.org/10.1007/s00024-012-0556-7
[57] R. Barzegar, M. Sattarpour, M.R. Nikudel, et al., Comparative evaluation of artificial intelligence models for prediction of uniaxial compressive strength of travertine rocks, Case study: Azarshahr area, NW Iran, Model. Earth Sys. Environ. 2, 76 (2016). DOI: https://doi.org/10.1007/s40808-016-0132-8
[58] A. Teymen, E.C. Mengüç, Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks. Int. J. Min. Sci. Tech. 30 (6), 785-797 (2020). DOI : https://doi.org/10.1016/j.ijmst.2020.06.008
[59] M.L. Larrea, S.M. Castro, E.A. Bjerg, A software solution for point counting. Petrographic thin section analysis as a case study. Arab. J. Geosci. 7, 2981-2989 (2014). DOI: https://doi.org/10.1007/s12517-013-1032-0
[60] E. Köken, Size Reduction Characterization of Underground Mine Tailings: A Case Study on Sandstones. Nat. Resour. Res. 30, 867-887 (2021). DOI: https://doi.org/10.1007/s11053-020-09707-2
[61] E.F. McBride, A classification of common sandstones. J. Sediment. Petrol. 33 (3), 664-669, (1963). DOI : https://doi.org/10.1306/74D70EE8-2B21-11D7-8648000102C1865D
[62] R.H. Dott, Wackes, greywacke and matrix: what approach to immature sandstone classification. J. Sedimen. Res. 34, 625-632 (1964).
[63] R.L. Folk, W.C. Ward, Brazos River bar, a study in the significance of grain size parameters. J. Sedimen. Petrol. 27 (1), 3-26 (1957). DOI: https://doi.org/10.1306/74D70646-2B21-11D7-8648000102C1865D
[64] R.L. Folk, Petrology of sedimentary rocks. Austin: Hemphill Pub. (1981), ISBN: 0-914696-14-9.
[65] I SRM, The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974-2006. In: Ulusay R, Hudson JA (eds) Suggested methods prepared by the commission on testing methods. (2007) International Society for Rock Mechanics (ISRM), (2007), Ankara, Turkey
[66] D .U. Deere, R.P. Miller, Engineering classification and index properties for intact rock. Technical Report Air Force Weapons Laboratory (Report No, AFWL-TR-65-116), 136-184, New Mexico, (1966).
[67] E. Yasar , Y. Erdoğan, Correlating sound velocity with the density, compressive strength and Young’s modulus of carbonate rocks. Int. J. Rock Mech Min. Sci. 41, 871-875 (2004). DOI : https://doi.org/10.1016/j.ijrmms.2004.01.012
[68] I . Yilmaz, G. Yuksek, Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN and ANFIS models. Int. J. Rock Mech. Min. Sci. 46, 803-810 (2009). DOI : https://doi.org/10.1016/j.ijrmms.2008.09.002
[69] Z.A. Moradian, M. Behnia, Predicting the Uniaxial Compressive Strength and Static Young’s Modulus of Intact Sedimentary Rocks Using the Ultrasonic Test. Int. J. Geomech. 9 (1), 14-19 (2009). DOI : https://doi.org/10.1061/(ASCE)1532-3641(2009)9:1(14)
[70] G. Pappalardo, Correlation between P-wave velocity and physical-mechanical properties of intensely jointed dolostones, Peloritani Mounts, NE Sicily. Rock Mech. Rock Eng. 48, 1711-1721 (2015). DOI : https://doi.org/10.1007/s00603-014-0607-8
[71] H. Arman, S. Paramban, Correlating natural, dry, and saturated ultrasonic pulse velocities with the mechanical properties of rock for various sample diameters. Appl. Sci. 10, 9134 (2020). DOI : https://doi.org/10.3390/app10249134
[72] N . Sabatakakis, G. Koukis, G. Tsiambos, S. Papanakli, Index properties and strength variation controlled by microstructure for sedimentary rocks. Eng. Geol. 97, 80-90 (2008). DOI: https://doi.org/10.1016/j.enggeo.2007.12.004
[73] R. Singh, A. Kainthola, T.N. Singh, Estimation of elastic constant of rocks using an ANFIS approach, Appl. Soft Comput. J. 12, 40-45 (2012). DOI: https://doi.org/10.1016/j.asoc.2011.09.010
[74] A.I. Lawal, M.A. Idris, An artificial neural network-based mathematical model for the prediction of blast-induced ground vibrations. Int. J. Environmen. Stud. 77 (2), 318-334, (2020). DOI : https://doi.org/10.1080/00207233.2019.1662186.
[75] S.K. Das, Artificial neural networks in geotechnical engineering: modeling and application issues, Metaheuristics in water, geotechnical and transport engineering, 231-270 (2013).
[76] M. Heidari, G.R. Khanlari, A.A. Momeni, Prediction of Elastic Modulus of Intact Rocks Using Artificial Neural Networks and non-Linear Regression Methods. Australian J. Basic Appl. Sci. 4 (12), 5869-5879 (2010).
[77] D .J. Armaghani, E.T. Mohamad, E. Momeni, M.S. Narayanasamy, An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite. Bull. Eng. Geol. Environ. 74, 1301-1319 (2015). DOI: https://doi.org/10.1007/s10064-014-0687-4
[78] S. Yagiz, E.A. Sezer, C. Gokceoglu, Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks. Int. J. Numer Anal. Methods Geomech. 36 (14), 1636-1650 (2012). DOI : https://doi.org/10.1002/nag.1066
[79] S. Aboutaleb, M. Behnia, R. Bagherpour, B. Bluekian, Using non-destructive tests for estimating uniaxial compressive strength and static Young’s modulus of carbonate rocks via some modeling techniques. Bull. Eng. Geol. Environ. 77 (4), 1717-1728 (2018). DOI: https://doi.org/10.1007/s10064-017-1043-2
[80] A. Jamshidi, H. Zamanian, R. Zarei Sahamieh, The Effect of Density and Porosity on the Correlation Between Uniaxial Compressive Strength and P-wave Velocity. Rock Mech. Rock Eng. 51, 1279-1286 (2018). DOI : https://doi.org/10.1007/s00603-017-1379-8
Go to article

Authors and Affiliations

Ekin Köken
1
ORCID: ORCID

  1. Abdullah Gul University, Nanotechnology Engineering Department, 38170, Kayseri, Turkey
Download PDF Download RIS Download Bibtex

Abstract

This research determines an identification system for the types of Beiguan music – a historical, nonclassical music genre – by combining artificial neural network (ANN), social tagging, and music information retrieval (MIR). Based on the strategy of social tagging, the procedure of this research includes: evaluating the qualifying features of 48 Beiguan music recordings, quantifying 11 music indexes representing tempo and instrumental features, feeding these sets of quantized data into a three-layered ANN, and executing three rounds of testing, with each round containing 30 times of identification. The result of ANN testing reaches a satisfying correctness (97% overall) on classifying three types of Beiguan music. The purpose of this research is to provide a general attesting method, which can identify diversities within the selected non-classical music genre, Beiguan. The research also quantifies significant musical indexes, which can be effectively identified. The advantages of this method include improving data processing efficiency, fast MIR, and evoking possible musical connections from the high-relation result of statistical analyses.
Go to article

Bibliography

1. Briot J.-P., Hadjeres G., Pachet F.-D. (2019), Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, arXiv: 1709.01620.
2. Hagan M.T., Demuth H.B., Beale M. (2002), Neural Network Design, CITIC Publishing House, Beijing.
3. Lamere P. (2008), Social tagging and music information retrieval, Journal of New Music Research, 37(2): 101–114, doi: 10.1080/09298210802479284.
4. Lu C.-K. (2011), Beiguan Music, Taichung, Taiwan: Morningstar.
5. Pan J.-T. (2019), The transmission of Beiguan in higher education in Taiwan: A case study of the teaching of Beiguan in the department of traditional music of Taipei National University of the Arts [in Chinese], Journal of Chinese Ritual, Theatre and Folklore, 2019.3(203): 111–162.
6. Rosner A., Schuller B., Kostek B. (2014), Classification of music genres based on music separation into harmonic and drum components, Archives of Acoustics, 39(4): 629–638, doi: 10.2478/aoa-2014-0068.
7. Tzanetakis G., Kapur A., Scholoss W.A., Wright M. (2007), Computational ethnomusicology, Journal of Interdisciplinary Music Studies, 1(2): 1–24.
8. Wiering F., de Nooijer J., Volk A., Tabachneck- Schijf H.J.M. (2009), Cognition-based segmentation for music information retrieval systems, Journal of New Music Research, 38(2): 139–154, doi: 10.1080/09298210903171145.
9. Yao S.-N., Collins T., Liang C. (2017), Head-related transfer function selection using neural networks, Archives of Acoustics, 42(3): 365–373, doi: 10.1515/aoa-2017-0038.
10. Yeh N. (1988), Nanguan music repertoire: categories, notation, and performance practice, Asian Music, 19(2): 31–70, doi: 10.2307/833866.
Go to article

Authors and Affiliations

Yu-Hsin Chang
1
Shu-Nung Yao
2

  1. Department of Music, Tainan National University of the Arts, No. 66, Daqi, Guantian Dist., Tainan City 72045, Taiwan
  2. Department of Electrical Engineering, National Taipei University, No. 151, University Rd., Sanxia District, New Taipei City 237303, Taiwan
Download PDF Download RIS Download Bibtex

Abstract

This research highlights the vibration analysis on worm gears at various conditions of oil using the experimental set up. An experimental rig was developed to facilitate the collection of the vibration signals which consisted of a worm gear box coupled to an AC motor. The four faults were induced in the gear box and the vibration data were collected under full, half and quarter oil conditions. An accelerometer was used to collect the signals and for further analysis of the vibration signals, MATLAB software was used to process the data. Symlet wavelet transform was applied to the raw FFT to compare the features of the data. ANN was implemented to classify various faults and the accuracy is 93.3%.

Go to article

Authors and Affiliations

Narendiranath Babu Thamba
Kiran Kamesh Thatikonda Venkata
Sathvik Nutakki
Rama Prabha Duraiswamy
Noor Mohammed
Razia Sultana Wahab
Ramalinga Viswanathan Mangalaraja
Ajay Vannan Manivannan
Download PDF Download RIS Download Bibtex

Abstract

This study is aimed at evaluating the applicability of Artificial Neural Network (ANN) model technique for river discharge forecasting. Feed-forward multilayer perceptron neural network trained with back-propagation algorithm was employed for model development. Hydro-meteorological data for the Imo River watershed, that was collected from the Anambra-Imo River Basin Development Authority, Owerri – Imo State, South-East, Nigeria, was used to train, validate and test the model. Coefficients of determination results are 0.91, 0.91 and 0.93 for training, validation and testing periodsrespectively. River discharge forecasts were fitted against actual discharge data for one to five lead days. Model results gave R2 values of 0.95, 0.95, 0.92, 0.96 and 0.94 for first, second, third, fourth, and fifth lead days of forecasts, respectively. It was generally observed that the R2 values decreased with increase in lead days for the model. Generally, this tech-nique proved to be effective in river discharge modelling for flood forecasting for shorter lead-day times, especially in areas with limited data sets.

Go to article

Authors and Affiliations

Arinze A. Obasi
Kingsley N. Ogbu
Louis C. Orakwe
Isiguzo E. Ahaneku
Download PDF Download RIS Download Bibtex

Abstract

In this study, the thermal conductivity ratio model for metallic oxide based nano-fluids is proposed. The model was developed by considering the thermal conductivity as a function of particle concentration (percentage volume), temperature, particle size and thermal conductivity of the base fluid and nano-particles. The experimental results for Al2O3, CuO, ZnO, and TiO2 particles dispersed in ethylene glycol, water and a combination of both were adopted from the literature. Artificial neural network (ANN) and power law models were developed and compared with the experimental data based on statistical methods. ANOVA was used to determine the relative importance of contributing factors, which revealed that the concentration of nano-particles in a fluid is the single most important contributing factor of the conductivity ratio.
Go to article

Authors and Affiliations

Mohammad Hanief
1
Qureshi Irfan
1
Malik Parvez
2

  1. Mechanical Engineering Department, National Institute of Technology Srinagar, India
  2. Chemical Engineering Department, National Institute of Technology Srinagar, India
Download PDF Download RIS Download Bibtex

Abstract

Waste lubricating oil (WLO) is the most significant liquid hazardouswaste, and indiscriminate disposal of waste lubricating oil creates a high risk to the environment and ecology. Present investigation emphasizes the re-refining of used automobile engine oil using the extraction-flocculation approach to reduce environmental hazards and convert the waste to energy. The extraction-flocculation process was modeled and optimized using response surface methodology (RSM), artificial neural network (ANN), and genetic algorithm (GA). The present study assessed parametric effects of refining time, refining temperature, solvent to waste oil ratio, and flocculant dosage. Experimental findings showed that the percentage of yield of recovered oil is to the tune of 86.13%. With the Central Composite Design approach, the maximum percentage of extracted oil is 85.95%, evaluated with 80 minutes of refining time, 50.17 °C refining temperature, 7:1 solvent to waste oil ratio and flocculant dosage of 3 g/kg of solvent and 86.71% with 79.97 minutes refining time, 55.53 °C refining temperature, 4.89:1 g/g solvent to waste oil ratio, 2.99 g/kg of flocculant concentration with Artificial Neural Network. A comparison shows that the ANN gives better results than the CCD approach. Physico-chemical properties of the recovered lube oil are comparable with the properties of fresh lubricating oil.
Go to article

Authors and Affiliations

Sayantan Sakar
1
Deepshikha Datta
2
Somnath Chowdhury
1
Bimal Das
1

  1. National Institute of Technology, Department of Chemical Engineering, Durgapur-713209, India
  2. Brainware University, Department of Chemistry, Barasat, Kolkata, West Bengal 700125
Download PDF Download RIS Download Bibtex

Abstract

An information security audit method (ISA) for a distributed computer network (DCN) of an informatization object (OBI) has been developed. Proposed method is based on the ISA procedures automation by using Bayesian networks (BN) and artificial neural networks (ANN) to assess the risks. It was shown that such a combination of BN and ANN makes it possible to quickly determine the actual risks for OBI information security (IS). At the same time, data from sensors of various hardware and software information security means (ISM) in the OBI DCS segments are used as the initial information. It was shown that the automation of ISA procedures based on the use of BN and ANN allows the DCN IS administrator to respond dynamically to threats in a real time manner, to promptly select effective countermeasures to protect the DCS.
Go to article

Authors and Affiliations

Berik Akhmetov
1
Valerii Lakhno
2
Vitalyi Chubaievskyi
3
Serhii Kaminskyi
3
Saltanat Adilzhanova
4
Moldir Ydyryshbayeva
4

  1. Yessenov University, Aktau, Kazakhstan
  2. National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine
  3. Kyiv National University of Trade and Economics, Kyiv, Ukraine
  4. Al-Farabi Kazakh National University, Almaty, Kazakhstan
Download PDF Download RIS Download Bibtex

Abstract

The invasive method of medically checking hemoglobin level in human body by taking the blood sample of the patient requiring a long time and injuring the patient is seen impractical. A non-invasive method of measuring hemoglobin levels, therefore, is made by applying the K-Nearest Neighbor (KNN) algorithm and the Artificial Neural Network Back Propagation (ANN-BP) algorithm with the Internet of Thingsbased HTTP protocol to achieve the high accuracy and the low endto- end delay. Based on tests conducted on a Noninvasive Hemoglobin measuring device connected to Cloud Things Speak, the prediction process using algorithm by means of Python programming based on Android application could work well. The result of this study showed that the accuracy of the K-Nearest Neighbor algorithm was 94.01%; higher than that of the Artificial Neural Network Back Propagation algorithm by 92.45%. Meanwhile, the end-to-end delay was at 6.09 seconds when using the KNN algorithm and at 6.84 seconds when using Artificial Neural Network Back Propagation Algorithm.
Go to article

Authors and Affiliations

R. Munadi
1
S. Sussi
1
N. Fitriyanti
2
D.N. Ramadan
3

  1. Department Telecomunication Engineering, School of Electric Engineering, Telkom University, Indonesia
  2. Department Physics Engineering, School of Electric Engineering, Telkom University, Indonesia
  3. School of Applied Science, Telkom University, Indonesia
Download PDF Download RIS Download Bibtex

Abstract

A mathematical model is proposed that makes it possible to describe in a conceptual and functional aspect the formation and application of a knowledge base (KB) for an intelligent information system (IIS). This IIS is developed to assess the financial condition (FC) of the company. Moreover, for circumstances related to the identification of individual weakly structured factors (signs). The proposed model makes it possible to increase the understanding of the analyzed economic processes related to the company's financial system. An iterative algorithm for IIS has been developed that implements a model of cognitive modeling. The scientific novelty of the proposed approach lies in the fact that, unlike existing solutions, it is possible to adjust the structure of the algorithm depending on the characteristics of a particular company, as well as form the information basis for the process of assessing the company's FC and the parameters of the cognitive model.
Go to article

Authors and Affiliations

Olena Kryvoruchko
1
Alona Desiatko
1
Igor Karpunin
1
Dmytro Hnatchenko
1
Myroslav Lakhno
2
Feruza Malikova
3
Ayezhan Turdaliev
4

  1. State University of Trade and Economics, Kyiv, Ukraine
  2. National University of Life and EnvironmentalSciences of Ukraine, Kyiv
  3. Almaty Technological University, Almaty,Kazakhstan
  4. Kazakh University of Railways andTransportation, Almaty, Kazakhstan
Download PDF Download RIS Download Bibtex

Abstract

Compacted Graphite Iron (CGI), is a unique casting material characterized by its graphite form and extensive matrix contact surface. This type of cast iron has a tendency towards direct ferritization and possesses a complex set of intriguing properties. The use of data mining methods in modern foundry material development facilitates the achievement of improved product quality parameters. When designing a new product, it is always necessary to have a comprehensive understanding of the influence of alloying elements on the microstructure and consequently on the properties of the analyzed material. Empirical studies allow for a qualitative assessment of the above-mentioned relationships, but it is the use of intelligent computational techniques that allows for the construction of an approximate model of the microstructure and, consequently, precise predictions. The formulated prognostic model supports technological decisions during the casting design phase and is considered as the first step in the selection of the appropriate material type.
Go to article

Bibliography

[1] König, M. (2010). Literature review of microstructure formation in compacted graphite iron. International Journal of Cast Metals Research. 23(3), 185-192. https://doi.org/10.1179/136404609X12535244328378.
[2] Dawson, S. & Hang, F. (2009). Compacted graphite iron-a material solution for modern diesel engine cylinder blocks and heads. China Foundry. 6(3), 241-246.
[3] Chen, Y., Pang, J. C., Li, S. X., Zou, C. L. & Zhang, Z. F. (2022). Damage mechanism and fatigue strength prediction of compacted graphite iron with different microstructures. International Journal of Fatigue. 164, 107126, 1-14. https://doi.org/10.1016/j.ijfatigue.2022.107126.
[4] Sandoval, J., Ali, A., Kwon, P., Stephenson, D. & Guo, Y. (2023). Wear reduction mechanisms in modulated turning of compacted graphite iron with coated carbide tool. Tribology International. 178, 108062, 1-13. https://doi.org/10.1016/j.triboint.2022.108062.
[5] Hosadyna-Kondracka, M., Major-Gabryś, K., Warmuzek, M. & Brůna, M. (2022). Quality assessment of castings manufactured in the technology of moulding sand with furfuryl-resole resin modified with PCL additive. Archives of Metallurgy and Materials. 67(2), 753-758. https://doi.org/10.24425/amm.2022.137814.
[6] Mrzygłód, B., Łukaszek-Sołek, A., Olejarczyk-Wożeńska, I. & Pasierbiewicz, K. (2022). Modelling of plastic flow behaviour of metals in the hot deformation process using artificial intelligence methods. Archives of Foundry Engineering. 22(3), 41-52. DOI: 10.24425/afe.2022.140235.
[7] Palkanoglou, E.N., Baxevanakis, K.P. & Silberschmidt, V.V. (2022). Thermal debonding of inclusions in compacted graphite iron: Effect of matrix phases. Engineering Failure Analysis. 139, 106476, 1-13. https://doi.org/10.1016/j.engfailanal.2022.106476.
[8] Patel, M., Dave, K. (2022). An insight of compacted graphite iron (CGI) characteristics and its production: a review. Recent Advances in Manufacturing Processes and Systems: Select Proceedings of RAM 2021, 131-148.
[9] Górny, M., Lelito, J., Kawalec, M. & Sikora, G. (2015). Influence of structure on the thermophisical properties of thin walled castings. Archives of Foundry Engineering. 15(2), 23-26.
[10] Górny, M., Kawalec, M., Witek, G. & Rejek, A. (2019). The influence of wall thickness and mould temperature on structure and properties of thin wall ductile iron castings. Archives of Foundry Engineering. 19(2), 55-59. DOI: 10.24425/afe.2019.127116.
[11] Saka, S.O., Seidu, S.O., Akinwekomi, A.D. & Oyetunji, A. (2021). Alloying elements variant on the development of antimony modified compacted graphite iron using rotary furnace. Annals of the Faculty of Engineering Hunedoara. 19(2), 13-22.
[12] Soiński, M.S., Jakubus, A., Borowiecki, B. & Mierzwa, P. (2021). Initial assessment of graphite precipitates in vermicular cast iron in the as-cast state and after thermal treatments. Archives of Foundry Engineering. 21(4), 131-136.
[13] Domeij, B., Elfsberg, J. & Diószegi, A. (2023). Evolution of dendritic austenite in parallel with eutectic in compacted graphite iron under three cooling conditions. Metallurgical and Materials Transactions B. 1-16.
[14] Ren, Z., Jiang, H., Long, S. & Zou, Z. (2023). On the mechanical properties and thermal conductivity of compacted graphite cast iron with different pearlite contents. Journal of Materials Engineering and Performance. 1-9. https://doi.org/10.1007/s11665-023-07823-7.
[15] Gumienny, G., Kacprzyk, B., Mrzygłód, B. & Regulski, K., (2022). Data-driven model selection for compacted graphite iron microstructure prediction. Coatings. 12(11), 1676, 1-18. DOI: 10.3390/coatings12111676.
[16] Mrzygłód, B., Gumienny, G., Wilk-Kołodziejczyk, D. & Regulski, K., (2019). Application of selected artificial intelligence methods in a system predicting the microstructure of compacted graphite iron. Journal of Materials Engineering and Performance. 28, 3894-3904. DOI: 10.1007/s11665-019-03932-4.
[17] Wilk-Kołodziejczyk, D., Regulski, K., Gumienny, G. & Kacprzyk, B. (2018). Data mining tools in identifying the components of the microstructure of compacted graphite iron based on the content of alloying elements. International Journal of Advanced Manufacturing Technology. 95(9-12), 3127-3139. DOI 10.1007/s00170-017-1430-7.
[18] Wilk-Kołodziejczyk, D., Kacprzyk, B., Gumienny, G., Regulski, K., Rojek, G. & Mrzygłód, B., (2017). Approximation of ausferrite content in the compacted graphite iron with the use of combined techniques of data mining, Archives of Foundry Engineering. 17(3), 117-122. DOI 10.1515/afe-2017-0102.
[19] Kusiak, J., Sztangret, Ł. & Pietrzyk, M. (2015). Effective strategies of metamodelling of industrial metallurgical processes. Advances in Engineering Software. 89, 90-97. DOI: 10.1016/j.advengsoft.2015.02.002.
[20] Sacks, J., Welch, W.J., Mitchel, T. & Wynn, H.P., (1989) Design and analysis of computer experiments. Stat Sci. 4, 409-435. DOI: 10.1214/ss/1177012413.
[21] Fragassa, C. (2022) Investigating the material properties of nodular cast iron from a data mining perspective. Metals. 12(9), 1493, 1-26. DOI: 10.3390/met12091493.
[22] Huang, W., Lyu, Y., Du, M., Gao, S-D., Xu, R-J., Xia, Q-K. & Zhangzhou, J. (2022). Estimating ferric iron content in clinopy-roxene using machine learning models. American Mineralogist. 107, 1886-1900. DOI: 10.2138/am-2022-8189.
[23] Sika, R., Szajewski, D., Hajkowski, J. & Popielarski, P. (2019). Application of instance-based learning for cast iron casting defects prediction. Management and Production Engineering Review. 10(4), 101-107. DOI: 10.24425/mper.2019.131450.
[24] Chen, S. & Kaufmann, T. (2022). Development of data-driven machine learning models for the prediction of casting surface defects. Metals. 12(1), 1-15. DOI: 10.3390/met12010001
[25] Alrfou, K., Kordijazi, A., Rohatgi, P. & Zhao, T. (2022). Synergy of unsupervised and supervised machine learning methods for the segmentation of the graphite particles in the microstructure of ductile iron. Materials Today Communications. 30. 103174. DOI: 10.1016/j.mtcomm.2022.103174.
[26] Vantadori, S., Ronchei, C., Scorza, D., Zanichelli, A. & Luciano, R. (2022). Effect of the porosity on the fatigue strength of metals. Fatigue & Fracture of Engineering Materials & Structures. 45(9), 2734-2747. https://doi.org/10.1111/ffe.13783.
[27] Dučić, N., Jovičić, A., Manasijević, S., Radiša, R., Ćojbašić, Z. & Savković, B. (2020). Application of machine learning in the control of metal melting production process. Applied Sciences. 10(17), 6048, 1-15. DOI: 10.3390/app10176048
[28] Kihlberg, E., Norman, V., Skoglund, P., Schmidt, P. & Moverare, J. (2021). On the correlation between microstructural pa-rameters and the thermo-mechanical fatigue performance of cast iron. International Journal of Fatigue. 145, 106112, 1-10. DOI: 10.1016/j.ijfatigue.2020.106112.
[29] Hernando, J.C., Elfsberg, J., Ghassemali, E., Dahle, A.K. & Diószegi, A. (2020). The role of primary austenite morphology in hypoeutectic compacted graphite iron alloys. International of Metalcasting. 14, 745-754. DOI: 10.1007/s40962-020-00410-9.
[30] Regordosa, A., de la Torre, U., Loizaga, A., Sertucha, J. & Lacaze, J. (2020). Microstructure Changes During Solidification of Cast Irons: Effect of Chemical Composition and Inoculation on Competitive Spheroidal and Compacted Graphite Growth. International of Metalcasting. 14, 681-688. DOI: 10.1007/s40962-019-00389-y.
[31] Ribeiro B.C.M., Rocha F.M., Andrade B.M., Lopes W., Corrêa E.C.S., (2020). Influence of different concentrations of silicon, copper and tin in the microstructure and in the mechanical properties of compacted graphite iron, Materials Research. 23(2), e2019-0678, 1-10. DOI: 10.1590/1980-5373-MR-2019-0678.
[32] Tan, P.-N., Steinbach, M. & Kumar, V. (2005). Introduction to Data Mining. Boston: Pearson Addison-Wesley.
[33] Rokach, L. & Maimon, O. (2005). Top-down induction of decision trees classifiers-a survey. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews). 35(4), 476-487.
[34] Barros, R.C., de Carvalho, A. & Freitas, A.A. (2015). Automatic Design of Decision-Tree Induction Algorithms, Springer International Publishing.
[35] Regulski, K., Wilk-Kołodziejczyk, D. & Gumienny, G. (2016). Comparative analysis of the properties of the Nodular Cast Iron with Carbides and the Austempered Ductile Iron with use of the machine learning and the support vector machine. The In-ternational Journal of Advanced Manufacturing Technology. 87(1), 1077-1093. DOI: 10.1007/s00170-016-8510-y.
[36] Rui, G., Zhiqian, Z., Tao, W., Guangheng, L., Jingyi, Z. & Dianrong, G., (2020) Degradation state recognition of piston pump based on ICEEMDAN and XGBoost, Applied Sciences. 10(18), 6593, 1-17. DOI:10.3390/app10186593

Go to article

Authors and Affiliations

Łukasz Sztangret
1
ORCID: ORCID
Izabela Olejarczyk-Wożeńska
1
ORCID: ORCID
Krzysztof Regulski
1
ORCID: ORCID
Grzegorz Gumienny
2
ORCID: ORCID
Barbara Mrzygłód
1
ORCID: ORCID

  1. AGH University of Science and Technology, Poland
  2. Lodz University of Technology, Poland
Download PDF Download RIS Download Bibtex

Abstract

A new method of creating constitutive model of masonry is reported in this work. The model is not an explicit orthotropic elastic-plastic one, but with an artificial neural network (ANN) giving an implicit constitutive function. It relates the new state of generalised stresses Σ n+1 with the old state Σ n and with an increment of generalised strains ΔE (plane-stress conditions are assumed). The first step is to run a strain- controlled homogenisation, repeatedly, on a three-dimensional finite element model of a periodic cell, with elastic-plastic models (Drucker–Prager) of the components; thus a set of paths is created in (Σ, ΔE) space. From these paths, a set of patterns is formed to train the ANN. A description of how to prepare these data and a discussion on ANN training issues are presented. Finally, the procedure based on trained ANN is put into a finite-element code as a constitutive function. This enables the analysis of arbitrarily large masonry systems. The approach is verified by comparing the results of the developed model basing on ANN with a direct (single-scale) one, which showed acceptable accuracy.
Go to article

Authors and Affiliations

Aleksander Urbański
1
ORCID: ORCID
Szymon Ligęza
2
ORCID: ORCID
Marcin Drabczyk
3
ORCID: ORCID

  1. Cracow University of Technology, Faculty of Civil Engineering, ul. Warszawska 24, 31-155, Kraków, Poland
  2. AGH University of Science and Technology, Faculty of Drilling, Oil and Gas (doctoral student), al. Mickiewicza 30, 30-059 Kraków, Poland
  3. Idealogic Ltd., ul. Kapelanka 26, 30-347 Kraków, Poland
Download PDF Download RIS Download Bibtex

Abstract

In this paper, neural networks are presented to solve the inverse kinematic models of continuum robots. Firstly, the forward kinematic models are calculated for variable curvature continuum robots. Then, the forward kinematic models are implemented in the neural networks which present the position of the continuum robot’s end effector. After that, the inverse kinematic models are solved through neural networks without setting up any constraints. In the same context, to validate the utility of the developed neural networks, various types of trajectories are proposed to be followed by continuum robots. It is found that the developed neural networks are powerful tool to deal with the high complexity of the non-linear equations, in particular when it comes to solving the inverse kinematics model of variable curvature continuum robots. To have a closer look at the efficiency of the developed neural network models during the follow up of the proposed trajectories, 3D simulation examples through Matlab have been carried out with different configurations. It is noteworthy to say that the developed models are a needed tool for real time application since it does not depend on the complexity of the continuum robots' inverse kinematic models.
Go to article

Bibliography

[1] D. Trivedi, C.D. Rahn, W.M. Kier, and I.D. Walker. Soft robotics: Biological inspiration, state of the art, and future research. Applied Bionics and Biomechanics, 5(3):99–117, 2008. doi: 10.1080/11762320802557865.
[2] G. Robinson and J.B.C. Davies. Continuum robots – a state of the art. In Proceedings 1999 IEEE International Conference on Robotics and Automation, volume 4, pages 2849–2854, 1999. doi: 10.1109/ROBOT.1999.774029.
[3] I.D. Walker. Continuous backbone “continuum” robot manipulators. International Scholarly Research Notices, 2013:726506, 2013. doi: 10.5402/2013/726506.
[4] H.-S. Yoon and B.-J. Yi. Development of a 4-DOF continuum robot using a spring backbone. The Journal of Korea Robotics Society, 3(4):323–330, 2008.
[5] M. Li, R. Kang, S. Geng, and E. Guglielmino. Design and control of a tendon-driven continuum robot. Transactions of the Institute of Measurement and Control, 40(11):3263–3272, 2018. doi: 10.1177/0142331216685607.
[6] G. Gao, H. Wang, J. Fan, Q. Xia, L. Li, and H. Ren. Study on stretch-retractable single-section continuum manipulator. Advanced Robotics, 33(1):1–12, 2019. doi: 10.1080/01691864.2018.1554507.
[7] C. Laschi, B. Mazzolai, V. Mattoli, M. Cianchetti, and P. Dario. Design of a biomimetic robotic octopus arm. Bioinspiration & Biomimetics, 4(1):15006, 2009. doi: 10.1088/1748- 3182/4/1/015006.
[8] F. Renda, M. Cianchetti, M. Giorelli, A. Arienti, and C. Laschi. A 3D steady-state model of a tendon-driven continuum soft manipulator inspired by the octopus arm. Bioinspiration & Biomimetics, 7(2):25006, 2012. doi: 10.1088/1748-3182/7/2/025006.
[9] F. Renda, M. Giorelli, M. Calisti, M. Cianchetti, and C. Laschi. Dynamic model of a multibending soft robot arm driven by cables. IEEE Transactions on Robotics, 30(5):1109–1122, 2014. doi: 10.1109/TRO.2014.2325992.
[10] Y. Peng, Y. Liu, Y. Yang, N. Liu, Y. Sun, Y. Liu, H. Pu, S. Xie, and J. Luo. Development of continuum manipulator actuated by thin McKibben pneumatic artificial muscle. Mechatronics, 60:56–65, 2019. doi: 10.1016/j.mechatronics.2019.05.001.
[11] G. Gao, H. Ren, Q. Xia, H. Wang, and L. Li. Stretched backboneless continuum manipulator driven by cannula tendons. Industrial Robot, 45(2):237–243, 2018. doi: 10.1108/IR-06-2017-0124.
[12] R.J. Webster III and B.A. Jones. Design and kinematic modeling of constant curvature continuum robots: A review. The International Journal of Robotics Research, 29(13):1661–1683, 2010. doi: 10.1177/0278364910368147.
[13] S. Mosqueda, Y. Moncada, C. Murrugarra, and H. León-Rodriguez. Constant curvature kinematic model analysis and experimental validation for tendon driven continuum manipulators. In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics ICINCO (2), volume 2, pages 211–218, 2018. doi: 10.5220/0006913502110218.
[14] A. Ghoul, K. Kara, M. Benrabah, and M.L. Hadjili. Optimized nonlinear sliding mode control of a continuum robot manipulator. Journal of Control, Automation and Electrical Systems, pages 1–9, 2022. doi: 10.1007/s40313-022-00914-1.
[15] C. Escande. Towards Modeling of a Class of Bionic Manipulator Robots. PhD Thesis, Lille, France, 2013.
[16] T. Mahl, A. Hildebrandt, and O. Sawodny. A variable curvature continuum kinematics for kinematic control of the bionic handling assistant. IEEE Transactions on Robotics, 30(4):935– 949, 2014. doi: 10.1109/TRO.2014.2314777.
[17] S. Djeffal, A. Amouri, and C. Mahfoudi. Kinematics modeling and simulation analysis of variable curvature kinematics continuum robots. UPB Scientific Bulletin, Series D: Mechanical Engineering, 83:28–42, 2021.
[18] S. Djeffal, C. Mahfoudi, and A. Amouri. Comparison of three meta-heuristic algorithms for solving inverse kinematics problems of variable curvature continuum robots. In 2021 European Conference on Mobile Robots (ECMR), pages 1–6, 2021. doi: 10.1109/ECMR50962.2021.9568789.
[19] O. Lakhal, A. Melingui, A. Shahabi, C. Escande, and R. Merzouki. Inverse kinematic modeling of a class of continuum bionic handling arm. In 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pages 1337–1342, 2014. doi: 10.1109/AIM.2014.6878268.
[20] D. Trivedi, A. Lotfi, and C.D. Rahn. Geometrically exact dynamic models for soft robotic manipulators. In 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1497–1502, 2007. doi: 10.1109/IROS.2007.4399446.
[21] A. Amouri, C. Mahfoudi, A. Zaatri, O. Lakhal, and R. Merzouki. A metaheuristic approach to solve inverse kinematics of continuum manipulators. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 231(5):380– 394, 2017. doi: 10.1177/0959651817700779.
[22] E. Shahabi and C.-H. Kuo. Solving inverse kinematics of a planar dual-backbone continuum robot using neural network. In B. Corves, P. Wenger, and M. Hüsing, editors, EuCoMeS 2018, pages 355–361. Springer, Cham, 2019. doi: 10.1007/978-3-319-98020-1_42.
[23] T.G. Thuruthel, E. Falotico, M. Cianchetti, and C. Laschi. Learning global inverse kinematics solutions for a continuum robot. In V. Parenti-Castelli andW. Schiehlen, editors, ROMANSY 21 - Robot Design, Dynamics and Control, pages 47–54. Springer, Cham, 2016. doi: 10.1007/978-3-319-33714-2_6.
[24] L. Jiajia, D. Fuxin, L. Yibin, L. Yanqiang, Z. Tao, and Z. Gang. A novel inverse kinematics algorithm using the Kepler oval for continuum robots. Applied Mathematical Modelling, 93:206–225, 2021. doi: 10.1016/j.apm.2020.12.014.
[25] D.Y. Kolpashchikov, N.V. Laptev, V.V. Danilov, I.P. Skirnevskiy, R.A. Manakov, and O.M. Gerget. FABRIK-based inverse kinematics for multi-section continuum robots. In 2018 18th International Conference on Mechatronics-Mechatronika (ME), pages 1–8. IEEE, 2018.
[26] R. Köker, C. Öz, T. Çakar, and H. Ekiz. A study of neural network based inverse kinematics solution for a three-joint robot. Robotics and Autonomous Systems, 49(3-4):227–234, 2004. doi: 10.1016/j.robot.2004.09.010.
[27] R.Y. Putra, S. Kautsar, R.Y. Adhitya, M. Syai’in, N. Rinanto, I. Munadhif, S.T. Sarena, J. Endrasmono, and A. Soeprijanto. Neural network implementation for invers kinematic model of arm drawing robot. In 2016 International Symposium on Electronics and Smart Devices (ISESD), pages 153–157, 2016. doi: 10.1109/ISESD.2016.7886710.
[28] N. Bigdeli, K. Afsar, B.I. Lame, and A. Zohrabi. Modeling of a five link biped robot dynamics using neural networks. Journal of Applied Sciences, 8(20):3612–3620, 2008.
[29] Z. Bingul, H.M. Ertunc, and C. Oysu. Comparison of inverse kinematics solutions using neural network for 6r robot manipulator with offset. In 2005 ICSC Congress on Computational Intelligence Methods and Applications, page 5, 2005. doi: 10.1109/CIMA.2005.1662342.
[30] X. Zhang, Y. Liu, D.T. Branson, C. Yang, J. S Dai, and R. Kang. Variable-gain control for continuum robots based on velocity sensitivity. Mechanism and Machine Theory, 168:104618, 2022. doi: 10.1016/j.mechmachtheory.2021.104618.
[31] A. Liegeois. Automatic supervisory control of the configuration and behavior of multibody mechanisms. IEEE Transactions on Systems, Man, and Cybernetics, 7(12):868–871, 1977.
Go to article

Authors and Affiliations

Abdelhamid Ghoul
1
Kamel Kara
1
Selman Djeffal
2
Mohamed Benrabah
3
Mohamed Laid Hadjili
4

  1. Université of Blida 1, Laboratoire des systèmes électriques et télécommande, Faculty of Technology, Blida, Algeria
  2. University of Larbi Ben M’hidi, Faculty of Science and Applied Sciences, Oum El Bouaghi, Algeria
  3. University of Sciences and Technology Houari Boumediene, Laboratoire des systèmes électriques et télécommande, Faculty of Electrical Engineering, Algiers, Algeria
  4. Haute Ecole Bruxelles, Ecole Supérieure d’Informatique, Brussels, Belgium
Download PDF Download RIS Download Bibtex

Abstract

The literature on exchange rate forecasting is vast. Many researchers have tested whether implications of theoretical economic models or the use of advanced econometric techniques can help explain future movements in exchange rates. The results of the empirical studies for major world currencies show that forecasts from a naive random walk tend to be comparable or even better than forecasts from more sophisticated models. In the case of the Polish zloty, the discussion in the literature on exchange rate forecasting is scarce. This article fills this gap by testing whether non-linear time series models are able to generate forecasts for the nominal exchange rate of the Polish zloty that are more accurate than forecasts from a random walk. Our results confirm the main findings from the literature, namely that it is difficult to outperform a naive random walk in exchange rate forecasting contest.

Go to article

Authors and Affiliations

Michał Rubaszek
Paweł Skrzypczyński
Grzegorz Koloch
Download PDF Download RIS Download Bibtex

Abstract

The purpose of the work was to predict the selected product parameters of the dry separation process using a pneumatic sorter. From the perspective of application of coal for energy purposes, determination of process parameters of the output as: ash content, moisture content, sulfur content, calorific value is essential. Prediction was carried out using chosen machine learning algorithms that proved to be effective in forecasting output of various technological processes in which the relationships between process parameters are non-linear. The source of data used in the work were experiments of dry separation of coal samples. Multiple linear regression was used as the baseline predictive technique. The results showed that in the case of predicting moisture and sulfur content this technique was sufficient. The more complex machine learning algorithms like support vector machine (SVM) and multilayer perceptron neural network (MPL) were used and analyzed in the case of ash content and calorific value. In addition, k-means clustering technique was applied. The role of cluster analysis was to obtain additional information about coal samples used as feed material. The combination of techniques such as multilayer perceptron neural network (MPL) or support vector machine (SVM) with k-means allowed for the development of a hybrid algorithm. This approach has significantly increased the effectiveness of the predictive models and proved to be a useful tool in the modeling of the coal enrichment process.

Go to article

Authors and Affiliations

Alina Żogała
Maciej Rzychoń
Download PDF Download RIS Download Bibtex

Abstract

Self-aligning roller bearings are an integral part of the industrial machinery. The proper analysis and prediction of the various faults that may happen to the bearing beforehand contributes to an increase in the working life of the bearing. This study aims at developing a novel method for the analysis of the various faults in self-aligning bearings as well as the automatic classification of faults using artificial neural network (ANN) and deep neural network (DNN). The vibration data is collected for six different faults as well as for the healthy bearing. Empirical mode decomposition (EMD) followed by Hilbert Huang transform is used to extract instantaneous frequency peaks which are used for fault analysis. Time domain and time-frequency domain features are then extracted which are used to implement the neural networks through the pattern recognition tool in MATLAB. A comparative study of the outputs from the two neural networks is also performed. From the confusion matrix, the efficiency of the ANN has been found to be 95.7% and using DNN has been found to be 100%.
Go to article

Authors and Affiliations

Abhishek Rakesh
Arun Aravind
Babu T. Narendiranath
Mohamed Jahzan
Rama Prabha D.

This page uses 'cookies'. Learn more