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

Wilk-Kołodziejczyk, D.; Regulski, K.; Gumienny, G.

doi:10.1007/s00170-016-8510-y

Cited by 47 publications

(23 citation statements)

References 30 publications

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“…Their low density causes them to flow to the surface layer of the casting, thus reducing its quality. It thus seems obvious that sulfur in cast iron should be eliminated or reduced to a minimum during the nodularization process in mold; however, authors [9][10][11][12][13][14] noted that there is a certain minimum amount of sulfur in cast iron that is required to produce cast iron of an adequate quality due to the correct morphology of graphite precipitates. The authors [1] showed the production process of cast iron castings with vermicular graphite precipitates, where sulfur compounds were deliberately added into the liquid alloy.…”

Section: Discussionmentioning

confidence: 99%

Selected Phenomena of the In-Mold Nodularization Process of Cast Iron That Influence the Quality of Cast Machine Parts

2017

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This paper discusses a problem connected with the production process of ductile iron castings made using the in-mold method. The study results are presented showing that this method compromises the quality of the cast machine parts and of the equipment itself. Specifics of the nodularization process using the in-mold method do not provide the proper conditions for removal of chemical reaction products to the slag, i.e., the products stay in the mold cavity and they also decrease the quality of the casting. In this work, corrosion-type defects were diagnosed mostly on the surface of the casting and some compounds in the near-surface layer-i.e., fayalite (Fe 2 SiO 4 ) and forsterite (Mg 2 SiO 4 )-which cause discontinuities in the metal matrix. The results presented here were selected based on experimental melts of ductile iron. The elements of the mold used in this study, the shape of the mixing chamber, charge materials, method of melting, temperature of liquid metal, etc. were directly related to the production conditions. An analysis was conducted of the chemical composition using a Leco GDS500A spectrometer and a carbon and sulfur Leco CS125 analyzer. Metallographic examinations were conducted using a Phenom-ProX scanning electron microscope with an EDS system.

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Section: Discussionmentioning

confidence: 99%

Selected Phenomena of the In-Mold Nodularization Process of Cast Iron That Influence the Quality of Cast Machine Parts

2017

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“…In this case, SVM introduces the kernel function [35], ( , ), which transforms the original data space into a new space with a higher dimension; this process includes the transformation function with dot product ( ) (Equation (6)). The aim is the data, which already transformed into a higher dimension, can be separated easily.…”

Section: Kernel Functionmentioning

confidence: 99%

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

et al. 2018

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Abstract:Termites are the most destructive pests and their attacks significantly impact the quality of wooden buildings. Due to their cryptic behavior, it is rarely apparent from visual observation that a termite infestation is active and that wood damage is occurring. Based on the phenomenon of acoustic signals generated by termites when attacking wood, we proposed a practical framework to detect termites nondestructively, i.e., by using the acoustic signals extraction. This method has the pros to maintain the quality of wood products and prevent higher termite attacks. In this work, we inserted 220 subterranean termites into a pine wood for feeding activity and monitored its acoustic signal. The two acoustic features (i.e., energy and entropy) derived from the time domain were used for this study's analysis. Furthermore, the support vector machine (SVM) algorithm with different kernel functions (i.e., linear, radial basis function, sigmoid and polynomial) were employed to recognize the termites' acoustic signal. In addition, the area under a receiver operating characteristic curve (AUC) was also adopted to analyze and improve the performance results. Based on the numerical analysis, the SVM with polynomial kernel function achieves the best classification accuracy of 0.9188.

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“…Classification methods were discussed in many articles by many researchers [43][44][45][46][47][48][49][50][51][52]. Neural networks were often used for classification problems [53][54][55][56][57][58].…”

Section: Bayes Classifiermentioning

confidence: 99%

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Głowacz¹,

Głowacz²,

Głowacz³

et al. 2017

Archives of Metallurgy and Materials

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A degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies -Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

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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

Cited by 47 publications

References 30 publications

Selected Phenomena of the In-Mold Nodularization Process of Cast Iron That Influence the Quality of Cast Machine Parts

Selected Phenomena of the In-Mold Nodularization Process of Cast Iron That Influence the Quality of Cast Machine Parts

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

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