Table Of ContentStudies in Systems, Decision and Control 183
Przemyslaw Grzegorzewski
Andrzej Kochanski
Janusz Kacprzyk Editors
Soft Modeling
in Industrial
Manufacturing
Studies in Systems, Decision and Control
Volume 183
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
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Przemyslaw Grzegorzewski
Andrzej Kochanski Janusz Kacprzyk
(cid:129)
Editors
Soft Modeling in Industrial
Manufacturing
123
Editors
Przemyslaw Grzegorzewski Andrzej Kochanski
Faculty of Mathematics andInformation Faculty of ProductionEngineering, Institute
Science ofManufacturing Technologies
Warsaw University of Technology Warsaw University of Technology
Warsaw,Poland Warsaw,Poland
and JanuszKacprzyk
Systems Research Institute
Systems Research Institute Polish Academy ofSciences
Polish Academy ofSciences Warsaw,Poland
Warsaw,Poland
ISSN 2198-4182 ISSN 2198-4190 (electronic)
Studies in Systems,DecisionandControl
ISBN978-3-030-03200-5 ISBN978-3-030-03201-2 (eBook)
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Contents
Part I Theory
Data and Modeling in Industrial Manufacturing . . . . . . . . . . . . . . . . . . 3
Przemyslaw Grzegorzewski and Andrzej Kochanski
From Data to Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Przemyslaw Grzegorzewski and Andrzej Kochanski
Data Preprocessing in Industrial Manufacturing . . . . . . . . . . . . . . . . . . 27
Przemyslaw Grzegorzewski and Andrzej Kochanski
Part II Applications
Tool Condition Monitoring in Metal Cutting . . . . . . . . . . . . . . . . . . . . . 45
Krzysztof Jemielniak
Assessment of Selected Tools Used for Knowledge Extraction
in Industrial Manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Marcin Perzyk and Artur Soroczynski
Application of Data Mining Tools in Shrink Sleeve Labels
Converting Process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Krzysztof Krystosiak
Study of Thickness Variability of the Floorboard
Surface Layer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
Agnieszka Kujawińska, Michał Rogalewicz, Magdalena Diering,
Krzysztof Żywicki and Adam Hamrol
Applying Statistical Methods with Imprecise Data
to Quality Control in Cheese Manufacturing . . . . . . . . . . . . . . . . . . . . . 127
Ana Belén Ramos-Guajardo, Ángela Blanco-Fernández
and Gil González-Rodríguez
v
vi Contents
Monitoring Series of Dependent Observations Using the sXWAM
Control Chart for Residuals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Olgierd Hryniewicz and Katarzyna Kaczmarek-Majer
Diagnosis of Out-of-Control Signals in Complex Manufacturing
Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
Marcin Perzyk, Jacek Kozlowski and Agnieszka Rodziewicz
Introduction
Mathematical modeling seems to be a fundamental device for doing science and
engineering. Indeed, mathematics has provided suitable concepts and tools, an
appropriate language and a way of thinking that enables an awesome progress in
various fields, including physics, chemistry, biology, ecology, economics, sociol-
ogy, as well as many disciplines of engineering. Nowadays, in the era of overar-
ching computer technology the need and importance of mathematical modeling
have become indisputable.
Mathematical models of processes, objects, or systems are frequently divided
intohardandsoftmodels.Intheso-calledhardmodeling,thephysicalnatureofthe
system(processorobject)istakenintoconsiderationthroughknownphysicallaws
and characterized in terms of principles of mechanics, thermodynamics, acoustics,
electromagnetism, material constants, as well as the geometry of the system under
analysis, supplemented with selected boundary conditions. As a result, a hard
model, as long as we succeed in developing one, makes possible a deep under-
standing of the nature of the phenomenon or system under analysis and, in par-
ticular, of the cause–effect mechanisms which are at work in the considered
situation. Although hard models typically treat variability in a simplified manner,
they exhibit stability, especially over long periods of time.
In turn, in the so-called soft modeling, the physical nature of phenomena may
notbeknownandmaynotbetakenintoconsideration.Softmodelsusuallyemploy
simplified mathematical equations derived directly from the data obtained as
observations or measurements of the given system. Hence, the system (process, or
object) which is being modeled is frequently treated as the so-called black box
whoseinternalfunctioningweknownothingabout.Andalthoughasoftmodelmay
not explain the nature of the phenomenon or system under analysis, it will, nev-
ertheless, point to its significant features or properties. Moreover, models of this
kind are usually capable of explaining the observed variability.
Of course, as long as it is possible to construct an adequate hard model which
satisfactorilyexplains aphenomenon,objectorsystem,suchamodelwill bemore
desirable than a soft one. However, when a little is known about the observed
reality,asoftmodelmaybetreatedasanintroductorysteptowardthedevelopment
vii
viii Introduction
ofahard model.It may also happen that obtaining anacceptable hardmodel turns
out impossible and a soft model is the only sensible model of the phenomenon
under study. Finally, sometimes the optimal solution is a compromise which
combines hard and soft modeling leading thereby to a model which is satisfactory
both from the perspective of understanding the process under analysis and the
utilitarian gains connected with the simplicity of solutions, implementations, pre-
dictions, etc.
In the case of industrial data, widely used in hard modeling, we have to face a
pervasiveuncertaintyofdifferentorigin.Consequently,weshouldexaminetowhat
extent various factors, like material parameters, which are established with a high
degree of uncertainty, influence the final results and analyzes. We should also
assess sensitivity of the considered models for diverse simplifications used in
modeling. And therefore, keeping all this in mind, it seems well motivated to talk
about different degrees of softness in mathematical modeling.
Thestartingpointforanyreasoninginempiricalsciencesisthedata.Itsquality
has essential influence on the quality of the reasoning itself. Moreover, even the
mostsophisticatedreasoningmethodsmayturnoutuselesswhentheinputdatawill
beofdoubtfulquality.Itisthesourceofthedatawhichexertsadecisiveinfluence
on data quality. In particular, industrial data are different from social, medical, or
economicdataand,infact,eachoftheabovekinds ofdatahasitsown specificity.
A characteristic property of industrial data is its high degree of uncertainty. This
uncertaintymayresult,amongothers,fromthelackofconfidenceinthecorrectness
of the observations made, means and methods of taking measurements, required
diligence in analysis, and the quality of control and measuring instruments. No
matterwhatproductionprocesswearedealingwith,thedatagatheredindatabases
arefrequentlyincorrect,duetocarelessnessorinaccuracyofameasurement,where
carelessnessrelatestoactionsofthepersontakingmeasurements,whileinaccuracy
pertainstothemeasuringorcontrolinstrumentsofalowquality.Inthecaseofdata
coming from production processes, we may also deal with inaccuracy resulting
from a weakness of the adopted measuring method (for instance, temporal
restrictionsontheuseofmoreadvancedmethods)or,directly,fromtheverynature
of the object or process under analysis. It may also happen that uncertainty an
additional variability produced by some undesired causes may superpose on the
relevant natural variability, typical of the process.
The book Soft Modeling in Industrial Manufacturing discusses the problems of
industrial data in their overall complexity, that is, the problems of data sources,
causes and types of data uncertainty, as well as methods of dealing with preparing
thistypeofdataforreasoningintheengineeringpractice.Thereaderwillalsohave
a chance to learn about the wide spectrum of soft modeling methods, with illus-
trations pertaining to specific cases, coming from diverse kinds of industrial
processes.
The book consists of two main parts: Part I: Theory—containing three chapters
and Applications with seven chapters. In Part I, Chapter “Data and Modeling in
Industrial Manufacturing”, can be perceived as the starting point for any modeling
andfor furtherscientificanalysis. Herewe discussthespecificityofindustrial data
Introduction ix
and its impact on scientific modeling. Following some general remarks on math-
ematicalmodeling,theideaofahardmodelingandsoftmodelinginengineeringis
developed.
The main goal of Chapter, “From Data to Reasoning”, is to discuss and clarify
the meaning of such basic notions like data, information, knowledge to indicate
theirinterrelationsandtosettheminthebroadframeworkofthecognitionoriented
activity. Finally, three basic types of reasoning used both in science and in appli-
cations are briefly characterized.
InChapter“DataPreprocessinginIndustrialManufacturing”,theimportanceof
data preparation in the whole process of data analysis is discussed. Firstly, the
structure of the analytical process is discussed and the data preparation stage is
located withinthis structure.Part IIcontains acritical discussionofthedifferences
between datum quality and data quality. Finally, the methodology of data prepa-
ration dedicated specifically to industrial data is considered.
Part II: Applications consists of seven independent chapters. These chapters
representbothvariousareasofapplicationsanddiversemethodologieswhichhave
to do with a wide range of soft modeling methods. In addition to a detailed dis-
cussionoftheproposedmodelingmethod,eachchaptercontainsalsoananalysisof
a specific practical problem.
In accordance with a classification offered in Chapter “Data and Modeling in
Industrial Manufacturing”, which distinguishes two groups of soft modeling
methods, both groups are illustrated in the book. And thus, artificial neural net-
works,decisionandregressiontrees,boostedtrees,supportedvectormachines,and
rough set theory, which are instances of computational intelligence methods, are
discussed and employed in Chapters “Assessment of Selected Tools Used for
Knowledge Extraction in Industrial Manufacturing”, “Application of Data Mining
Tools in Shrink Sleeve Labels Converting Process” and “Diagnosis of Out-of-
ControlSignalsinComplexManufacturingProcesses”.Inturn,modifiedShewhart
chartsandstatistical hypothesistestingtools,whichexemplifystatistics andexpert
systems,areconsideredinChapters“ToolConditionMonitoringinMetalCutting”,
“Study of Thickness Variability of the Floorboard Surface Layer”, “Applying
Statistical Methods with Imprecise Data to Quality Control in Cheese
Manufacturing” and “Monitoring Series of Dependent Observations Using the
sXWAM Control Chart for Residuals”.
The diversity of topics and themes in Part II is also manifested in the range of
practical applications discussed in the respective chapters. They include applica-
tions of soft modeling in such areas of industrial production as food processing,
machining, printing, metallurgy, casting, floorboard production, or economic
analyzes.
Theveryaimsforwhichtheselectedtoolswereemployedarewidelydiverseas
well. Modeling was used to predict process parameters (Chapter “Application of
Data Mining Tools in Shrink Sleeve Labels Converting Process”), to create rules
(Chapter “Assessment of Selected Tools Used for Knowledge Extraction in
IndustrialManufacturing”),tomonitoraprocess(Chapters“AssessmentofSelected
Tools Used for Knowledge Extraction in Industrial Manufacturing”, “Monitoring
