Artificial Cognitive Architecture with Self-Learning and Self-Optimization Capabilities: Case Studies in Micromachining Processes

216 Pages·2019·6.29 MB·English

by  Gerardo Beruvides

Springer Theses
Recognizing Outstanding Ph.D. Research
Gerardo Beruvides
Artificial Cognitive
Architecture with
Self-Learning and
Self-Optimization
Capabilities
Case Studies in Micromachining
Processes
Springer Theses
Recognizing Outstanding Ph.D. Research
Aims and Scope
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theses from around the world and across the physical sciences. Nominated and
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includeanextendedintroduction,aswellasaforewordbythestudent’ssupervisor
explainingthespecialrelevanceoftheworkforthefield.Asawhole,theserieswill
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and for other scientists seeking detailed background information on special
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contributions made by today’s younger generation of scientists.
Theses are accepted into the series by invited nomination only
and must fulfill all of the following criteria
(cid:129) They must be written in good English.
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Gerardo Beruvides
fi
Arti cial Cognitive
Architecture
with Self-Learning
and Self-Optimization
Capabilities
Case Studies in Micromachining Processes
Doctoral Thesis accepted by
ó
Universidad Aut noma de Madrid, Madrid, Spain
123
Author Supervisors
Dr. Gerardo Beruvides Dr. RodolfoE. Haber
Centrefor Automation and Robotic Centrefor Automation and Robotic
(CAR-CSIC) (CAR-CSIC)
Madrid,Spain Madrid,Spain
Dr. RamonQuiza
Research CenteronAdvanced
andSustainable Manufacturing
University of Matanzas
Matanzas, Cuba
ISSN 2190-5053 ISSN 2190-5061 (electronic)
SpringerTheses
ISBN978-3-030-03948-6 ISBN978-3-030-03949-3 (eBook)
https://doi.org/10.1007/978-3-030-03949-3
LibraryofCongressControlNumber:2018961212
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We learn from the greatest sages of the past,
… that there is no force greater than
willingness,
… that there is no success without hard work,
… that in your dreams you’ll find way to your
goals,
Thank you Mamucha for believe in my
dreams,
Thank you Peluchina for supporting me in
them.
’
Supervisor s Foreword
It isagreat pleasuretointroducethethesis workof Dr.GerardoBeruvides López,
accepted for publication within Springer Theses and awarded with the 2017 prize
for outstanding Ph.D. in the field of Intelligent Control by the Comité Español de
Automática(CEA)in2018.InMarch2013,Dr.BeurvidesLópezreceiveda4-years
Ph.D. grant by the Ministry of Economy and Competitiveness in the frame of the
research project DPI2012-35504 entitled “Artificial Cognitive Control for
Micromachining Processes. Method and Application” that I led and thereby I
acknowledge for.
The work presents outstanding research results in a multidisciplinary research
approach where methods in the conjunction of Control Engineering, Computer
Sciences and Artificial Intelligence are fed by Cognitive Sciences, Neurosciences
and Psychology to yield an artificial cognitive control architecture. From signal
processing up to real time learning and control, a comprehensive approach is
presented with the added relevance of the implementation in low cost computing
platforms and the real-time application to micro-scale manufacturing processes,
enriching the originality and innovative aspects of the proposed methods and
reportedresults.ThestructureofthePh.D.dissertationallowsthereadertofollow,
in a comprehensive way, from basic initial steps in measurement sciences and
signal processing, modelling approaches using different techniques up to the
foundation of the proposed artificial cognitive control computational architecture.
Each theoretical contribution is validated by experimental studies enabling the
reader to understand the advantages and limitations of the proposed methods.
From engineering viewpoint, the theoretical framework inspired in the shared
circuitsmodelforemulatingsocio-cognitiveskillsandexperimentalproceduresare
harmonized in order to demonstrate the suitability of the developed method for
micromachining processes optimization, mainly micro-milling and micro-drilling
processes. Indeed, the efficiency of the proposed system and the set of developed
methods is verified through extensive experimentation in an industrial setup of
GAMHE4.0 Lab (www.gahme.eu) inmore than 300 experiments with state-of-art
sensors and computing platforms. Programming issues have been also considered
and reported in order to enable real time communication, learning and control.
vii
viii Supervisor’sForeword
Multidisciplinary roots of the work are endorsed by scientific and technical
resultsindifferentfields.Oneresult,inthedomainofsensorsandsignalprocessing
tomonitortoolwearinmicro-drillingoperations,istheresultthatdemonstratehow
the combination of wavelet transform and neural network systems yields the most
suitablepredictionoftheuseoftool.AnothercontributionreportedintheThesisis
thedesignandimplementationofartificialcognitivecontrolarchitectureisfocused
onfourkeyareas:(i)self-optimizationandself-leaningcapabilitiesbyestimationof
distribution and reinforcement-learning mechanisms; (ii) portability and scalability
based on low-cost computing platforms; (iii) connectivity based on middleware;
and (iv) model-driven approaches. The results of simulation and real-time appli-
cationtoforcecontrolofmicro-manufacturingprocessesarepresentedintheThesis
as a proof of concept.
From industrial viewpoint, very promising results for modelling, optimization
andcontrolofmicro-millingandmicro-drillingprocessesarepresented,pavingthe
way for future technology transfer of the reported work, supported on low-cost
computing platforms.
Madrid, Spain Dr. Rodolfo E. Haber
October 2018 Centre for Automation and Robotic
(CAR-CSIC)
Foreword from the Intelligent Control Group
of the Automation Spanish Committee (CEA)
ThePh.D.dissertationofDr.GerardoBeruvidesLópezwasawardedwiththe2018
prizeforoutstandingPh.D.inthefieldofIntelligentControlbytheComitéEspañol
de Automática (CEA) in 2018.
The sustainability and efficiency are essential issues for the future of the man-
ufacturing industry. Nowadays, the main scientific and technical challenges of the
manufacturing industry are only partially addressed in isolated fields such as
Computer Science, Artificial Intelligence, Mechanical Engineering and System
Engineering, all relevant to achieve high levels of modularity, connectivity,
autonomyanddigitization.Themanufacturingindustryneedstoincreaseefficiency
with shorter delivery times, where the production optimization through cyber-
physical systems, self-learning and self-adaptation are essential.
ThreekeyissueshavebeenaddressedinthisThesis,asfollows:thedevelopment
andrefinementofagradient-freemethodtoenablemulti-objectiveself-optimization,
thedevelopmentofa reinforcementlearning strategytocarry outself-learning and
finally,theexperimentalevaluationandvalidationintwomanufacturingprocessesat
themicro-scale (i.e., micro-milling and micro-drilling).
Themethodologyisbasedonthecombinationandintegrationoftheoreticaland
experimental methods by selecting, modifying, adapting and integrating the most
convenient paradigms within the Computer Sciences, Control Engineering and
Artificial Intelligence. In addition, the choice of micromachining processes as case
study is based on scientific-technical and economic reasons. The micromachining
hasagreatimpactontheworldeconomy,insectorssuchas:electronics,aerospace
and biomedicine, among others.
The design and implementation of a computational architecture (modular, net-
workandreconfigurableforreal-timemonitoringandcontrol)takeintoaccountthe
analysis of different types of sensors, processing strategies and methodologies for
extracting behavior patterns from representative signalsin complex processes. The
reconfiguration capability and the portability of this architecture are supported by
two major levels: the cognitive level (core of architecture) and the executive level
(directexchangewiththeprocess).Atthesametime,thisiscomposedbydifferent
operating modes that interact with the process to be monitored and/or controlled.
ix
x ForewordfromtheIntelligentControlGroup…
These procedures (briefly described below) depend on the models and algorithms
integratedinthearchitecture.Thecognitiveleveliscomposedbythreefundamental
modes: modeling, optimization and learning, necessary for decision-making (con-
trol signals) from the computational point of view, as well as the real-time exper-
imental characterization of complex processes.
Finally, a large number of these partial results has been submitted to consider-
ation by the international scientific community in international conferences and
publications in prestigious journals. Referees from different knowledge areas have
positively assessed the heterogeneous, multidisciplinary character and interdisci-
plinaryofthecontributionsreported.Thecontemporaneityoftheseresultsarefully
alignedwiththefutureresearchinthefieldofcyber-physicalsystems,Industry4.0
and the Factory of the Future, in which cognitive systems, self-reconfiguration,
self-optimization and self-learning capabilities are the key to the sustainable pro-
duction systems in the 21st century.
Madrid, Spain Prof. Dr. Carlos Balaguer
President of CEA
Dr. Eloy Irigoyen
Coordinator of the Intelligent Control
Group (CEA)
Dr. Antonio José Calderón
Coordinator of the Intelligent Control
Group (CEA)
Prof. Dr. Juan Albino Mendez
Coordinator of the Intelligent Control
Group (CEA)