TitleMethodologies of Knowledge Discovery from Data and Data Mining Methods in Mechanical Engineering
Journal titleManagement and Production Engineering Review
KeywordsWydział IV Nauk Technicznych
Divisions of PASWydział IV Nauk Technicznych
PublisherProduction Engineering Committee of the Polish Academy of Sciences, Polish Association for Production Management
SourceManagement and Production Engineering Review; 2016; No 4
ReferencesJansen (1996), Exploratory Data Analysis of Production Data of Permian Basin Oil and Gas Rec, Proc, 84. ; Zhou (2006), means algorithm for manufacturing process anomaly detection and recognition of st on, Proc Int Symp Dig, 67, 1. ; Wang (2007), Review on Application of Data Mining in Product Design and Manufacturing Fourth on And Know, Int Conf Fuz Dis, 56. ; Ignaszak (2013), Sensivity of Models Applied in Selected Simulation System with Respect to Database Quality for Resolving of Casting Problems Def and, Diff Forum, 20, 334. ; Perzyk (2014), Comparison of data mining tools for significance analysis of process parameters in applications to process fault diagnosis, Inf, 77, 259. ; Murtagh (2012), Algorithms for hierarchical clustering : an overview : Data Mining and Know Disc, Rev, 50, 86. ; Jia (2013), The Fault Diagnosis for Warship s Power Plant Based on Association Rules in and II, Adv Mech Cont Eng, 59, 433. ; Rohanizadeh (2009), A proposed Data Mining Methodology and its Application to Industrial Procedures of, Ind Eng, 46, 37. ; Kotsiantis (2006), Machine learning : A review of classification and combining techinques Art, Int Rev, 49, 159. ; Sobh (2015), Unsupervised clustering of materials properties using hierarchical techniques J of, Int Coll, 64, 74. ; Lu (2015), Application of Support Vector Machine and Genetic Algorithm Optimization for Quality Prediction within Complex Industrial Process of IEEE th on pp, Proc Int Conf Ind, 78, 98. ; Diering (2015), New method for assessment of raters agreement based on fuzzy similarity in and, Adv Intell Comp, 368. ; Trojanowska (2015), Shortening changover time an industrial study of the th Iberian Conf on and, Proc Inf Tech, 10. ; Zhang (2011), Identifying Mapping Relationships between Functions and Technologies : an Approach based on Association Rule Mining of on and pp, Proc Int Conf Ind Eng Eng, 61, 1596. ; Hu (2015), Research on knowledge mining for agricultural machinery maintenance based on association rules Proc of Inter Conf on and pp, Ind Elect, 58, 901. ; Yiakopoulos (2011), Rolling element bearing fault detection in industrial environments based on a K - means clustering approach with, Exp Appl, 68, 2888, doi.org/10.1016/j.eswa.2010.08.083 ; Brezak (2012), Tool wear estimation using an analytic fuzzy classifier and support vector machines of, Int Man, 75, 797. ; Pandilov (2016), Virtual Modelling And Simulation Of A CNC Machine Feed Drive System of FAMENA, Trans, 39, 37. ; Yasa (2014), Classification and Regression Trees Approach for Predicting Current - Induced Scour Depth Under Pipelines Off Mech, Arct Eng, 76, 136. ; Ignaszak (2013), Example of New Models Applied in Selected Simulation System with Respect to Database Arch of, Found Eng, 19, 45. ; Marban (2009), A Data Mining & Knowledge Discovery Process Model Dat Min and Know Disc INTECH Open Science, Proc, 43. ; Hamrol (2000), Process diagnostics as a means of improving the efficiency of quality control Plan and, Prod Con, 11, 797, doi.org/10.1080/095372800750038409 ; Popat (2014), Review and comparative study of clustering techniques of and, Int J Comp Inf Tech, 51, 805. ; Muralidharan (2012), A comparative study of Naive Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis App Soft, Comp, 71, 2023. ; Hayajneh (2005), Fuzzy clustering modelling for surface finish prediction in fine turning process Mach and, Tech, 65, 437. ; Jing (2011), A Fast Retrieval Method Based on K - means Clustering for Mechanical Product Design, Adv Manuf Tech, 66, 156. ; Jegadeeshwaran (2015), Brake fault diagnosis using Clonal Selection Classification Algorithm CSCA ) a statistical learning approach and, Eng Tech, 72, 14. ; Yang (2008), Association rule mining for affective product design of on and pp, Proc Int Conf Ind Eng Eng, 62, 748. ; Lesany (2014), Fatemi Ghomi Recognition and classification of single and concurrent unnatural patterns in control charts via neural networks and fitted line of samples of, Int J Prod Res, 74, 1771, doi.org/10.1080/00207543.2013.848483 ; Shahbaz (2006), Product design and manufacturing process improvement using association rules of the Int of Part J of, Proc Mech Eng Eng, 63, 243. ; Grajewski (2015), Improving the skills and knowledge of future designers in the field of ecodesign using virtual reality technologies, Proc, 75, 348. ; Pashazadeh (2014), Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi - objective genetic algorithm of, Int Man, 80. ; Perzyk (2007), Statistical and Visualization Data Mining Tools for Foundry Production Arch of, Foun Eng, 34, 111. ; Fayyad (1996), From Data Mining to Knowledge Discovery in Databases Art, Int Mag, 41, 37. ; Chen (2015), Data Mining review for the Internet of Things : Literature Review and Challenges of Net vol Art ID pages, Int J Dis, 14. ; Hamrol (2011), Impact of selected work condition factors on quality of manual assembly process Fact and in and, Hum Erg Man Serv, 21, 156, doi.org/10.1002/hfm.20233 ; Verbert (2011), Multivariate Adaptive Regression Splines as a Tool to Improve the Accuracy of Parts Produced by FSPIF Key, Eng Mat, 81, 473. ; Manikandan (2013), Achieving Privacy in Data Mining Using Normalization of And, Ind Sc Tech, 33, 4268. ; Zawadzki (2011), An Automatic System for D Models and Technology Process Design of FAMENA, Trans, 35, 69. ; Frawley (1992), Knowledge Discovery in Databases : An Overview Art, Int Mag, 39, 57. ; Jin (2012), Reconfigured piecewise linearregressiontree for multistage manufacturing process control IIE, Trans, 79, 249. ; Djatna (2015), Muharram An application of association rule mining in total productive maintenance strategy : an analysis and modelling in wooden door manufacturing industry of Inter Conf on and pp, Proc Ind Eng Serv, 57, 336. ; Abonyi (2007), Application of Exploratory Data Analysis to Historical Process Data of Polyethylene Production Hung of and, Ind Chem, 85, 85. ; Mareci (2013), Evaluation of the corrosion resistance of new ZrTi alloys by experiment and simulation with an adaptive instance - based regression model, Corros, 82, 106, doi.org/10.1016/j.corsci.2013.03.030 ; Harding (2006), Data Mining in Manufacturing : A Review of and, Man Eng, 55. ; Martinez (2012), de Martinez - de Mining association rules from time series to explain failures in a hot - dip galvanizing steel line Comp and, Ind Eng, 60, 22, doi.org/10.1016/j.cie.2012.01.013 ; Ma (2011), Defects Classification of Steel Cord Conveyor Belt Based on Rough Set and Multi - Class v - SVM, Adv Mat Res, 69, 328. ; Górski (2015), Immersive City Bus Configuration System for Marketing and Sales Education, Proc, 75, 137. ; Choudhary (2009), Data mining in manufacturing : a review based on the kind of knowledge of pp, Int Man, 54, 500. ; Perzyk (2010), Application of rough sets theory in control of foundry processes Arch of and, Metall Mat, 83, 889. ; Starzyńska (2013), Excellence toolbox : Decision support system for quality tools and techniques selection and application Tot Qual Man and, Bus, 3, 5. ; Hamrol (1997), Intelligent components for quality control in manufacturing of rd IFAC Symp on Comp and for pp, Proc Intel Instr Con, 1, 613.
Aims and scopeMISSION STATEMENT Management and Production Engineering Review (MPER) is a peer-refereed, international, multidisciplinary journal covering a broad spectrum of topics in production engineering and management. Production engineering is a currently developing stream of science encompassing planning, design, implementation and management of production and logistic systems. Orientation towards human resources factor differentiates production engineering from other technical disciplines. The journal aims to advance the theoretical and applied knowledge of this rapidly evolving field, with a special focus on production management, organisation of production processes, manage- ment of production knowledge, computer integrated management of production flow, enterprise effectiveness, maintainability and sustainable manufacturing, productivity and organisation, forecasting, modelling and simu- lation, decision making systems, project management, innovation management and technology transfer, quality engineering and safety at work, supply chain optimization and logistics. Management and Production Engineering Review is published under the auspices of the Polish Academy of Sciences Committee on Production Engineering and Polish Association for Production Management. The main purpose of Management and Production Engineering Review is to publish the results of cutting- edge research advancing the concepts, theories and implementation of novel solutions in modern manufacturing. Papers presenting original research results related to production engineering and management education are also welcomed. We welcome original papers written in English. The Journal also publishes technical briefs, discussions of previously published papers, book reviews, and editorials. Letters to the Editor-in-Chief are highly encouraged.
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Open Access PolicyThe non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on https://creativecommons.org/licenses/by/4.0/.
Abstracting & IndexingIndex Copernicus
Web of Science - Clarivate (ESCI)
Scopus - Elsevier
(CiteScore 2020 - 2.5
SJR 2020 - 0.332
SNIP 2020 - 1.061)