A Big Data Perspective - ISPR 2018 · 2017-12-06 · Smart Manufacturing: Contributing Computing...
Transcript of A Big Data Perspective - ISPR 2018 · 2017-12-06 · Smart Manufacturing: Contributing Computing...
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The University of Iowa Intelligent Systems Laboratory
Andrew KusiakIntelligent Systems Laboratory
The University of IowaIowa City, Iowa
USA
[email protected]://research.engineering.uiowa.edu/kusiak/
Smart Manufacturing: A Big Data Perspective
ISPR 2017, Wien, Austria
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The University of Iowa Intelligent Systems Laboratory
Introduction Data-driven modeling Pillars of smart manufacturing Hypothesizing the future Data science in manufacturing Optimization in a data-reach environment Conclusion
Outline
ISPR 2017, Wien, Austria
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The University of Iowa Intelligent Systems Laboratory
The Future is Promising
In 2001 R. Kurzweil (Director of Engineering at Google) in an essay The Law of Accelerating Returnspredicted that the 21st century may experience 20,000 years of progress (at today’s rate)
D. Butler, Nature, Vol. 530, Feb 2016
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Smart Manufacturing Concept
Interface Standard connectivity
Cyberspace System intelligence
Data Decisions
Data Decisions
Manufacturing equipment Local intelligenceA. Kusiak, Smart Manufacturing, IJPR 2017 (published online)
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Pillars of Smart Manufacturing
Materials
SustainabilityResource
sharing and networking
Predictive engineering
Data
Smart manufacturingManufacturing technology and
processes
A. Kusiak, Smart Manufacturing, IJPR 2017 (published online)
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Making Manufacturing Smart with Data
Bottom up modeling
No limits on the type and number of parameters
High model accuracy
DataMining
Decision Making/Optimization
~½ Solution ~½ Solution
Dat
a Sc
ienc
e
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a rP Tω=
=Classical science
Data science
Example: Wind Power Balancing
2 31 ( , )2a pP R C vρπ λ β=
Pictures courtesy of Danish Wind Energy Association
=
A. Kusiak, Share Data on Wind Energy, Nature, Vol. 529, No. 7584, 2016, pp. 19-21.
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Classical Control
Industrial process
Controller
PP0Knownset point
Adjustable input
Today’s manufacturing: Known set point = Production output
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Wind Turbine ControlA
ntic
ipat
ory
Con
trol Wind
Turbine
Controller
PP0Unknownset point
Non adjustable input
Tomorrow’s manufacturing: Predicted set point = Production output
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Intelligent Manufacturing: ‘History’
1990
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Common Manufacturing Models of the Last Four Decades
Flexible manufacturing systems (late 1970s) Computer-integrated manufacturing systems Reconfigurable manufacturing systems Holonic manufacturing systems Bionic manufacturing systems Intelligent manufacturing Smart manufacturing
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International Activities in Intelligent Manufacturing
12
IMS Program (Japan, 1995) NGMS, IMS (CAM-I, USA) IMS EU
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Smart Manufacturing: Contributing Computing Concepts
Service-oriented architectures Cloud computing Cyber-physical systems Internet of things (and everything) Sensor networks
A. Kusiak, Smart Manufacturing Must Embrace Big Data, Nature, Vol. 544, No. 7648, 2017, pp. 23-25.
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New Manufacturing Initiatives
Industrie 4.0 (Germany) Factories of the Future (EU)Made in China 2025
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Characteristics of Smart Manufacturing (1)
Expanded condition monitoring Self-diagnosis Self-correction, repair, self-healing Self-organization
Increased adaptation and scalability Variable batch size (from 1 to large) Reduced production ramp-up time Reduced change-over time
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Characteristics of Smart Manufacturing (2)
Polarization of coupling between manufacturing enterprise and manufacturing assets Corporations with a weak coupling, e.g.,
sharing and leasing of mfg equipment and facilities Corporations with a strong coupling, e.g., material,
product, and process created to serve the same purpose
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Smart Factory
Primary differentiators: Predictive engineering Seeing the future
Sustainability (including energy and transportation) From product conception to the end-of-life
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Product End-of-Life
Reuse (most preferred) Remanufacture Recycle Disposal (should disappear)
Restored 1949 VW Bug
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Emerging PrioritiesNew materials, processes, and products Quick path from material design meeting
customer needs and production Material-process-product paradigm
Engineering biology and bio-products Developments in biology and genetics to benefit
manufacturing chemicals, materials, fuel, and cells Integrated manufacturing E.g., integration of manufacturing medication
substances and medications into a single integrated process
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Bio-based Materials: Examples
20
Petro-based products replaced with bio-based products E.g., rubber from dandelions; Fraunhofer Institute for Molecular Biology,
Munster, Germany By 2020 IKEA plans to manufacture all plastic
products and toys from renewable/recycled materials
Lightweight plastics from agave Ford Motor Corporation
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Additive Manufacturing: A Game Changer (1)
The success hinges on manufacturing of artifacts: having the right properties (e.g., strength, surface quality, material shrinkage) viability in providing unattainable features (e.g., materials of different elasticity in one)by the progress in: component and product design materials, and processes
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Additive Manufacturing: A Game Changer (2)
Big Area Additive Manufacturing (BAAM) E.g., car chasees, molds for wind turbine blades
Small Area Additive Manufacturing (SAAM) E.g., medical implants
Material-Process-Product Design Paradigm
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New Business Models
Each is the largest in its category
None of them owns or produces any assets it is known for
What these companies have in common?
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What Have we Learned from Them?
Using customers to design products
Innovation
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Smart Transportation
Traditional vehicleFossil fuel
Electric vehicleNon-renewable electricity
Electric vehicleRenewable energy
Sustainable vehicle designRenewable energy
Semi-autonomous
Autonomous
Connected
Shared
Traditional
Vehicle
type
/
Fuel ty
pe
Vehicle
autom
ation
/
Use m
ode
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Integration of Manufacturing and Transport
Internal and external material handling and transport E.g., wind energy supply chain
Globally distributed production Transportation in supply, distribution, and maintenance
Meeting changing market needsTransport sharing
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The Future of Smart Manufacturing
Imagining the future of smart manufacturing
Ten conjectures
A. Kusiak, Smart Manufacturing, IJPR 2017 (published online)
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Conjecture 1
Manufacturing Digitalization Manufacturing will increasingly depend on data
Justification Manufacturing could benefit from wind energy and process
industry where supervisory control and data acquisition (SCADA) systems have been used to capture, store, and sharedata
A. Kusiak, Smart Manufacturing Must Embrace Big Data, Nature, Vol. 544, No. 7648, 2017, pp. 23-25
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Conjecture 2
Increased Need for Modeling, Optimization, and Simulation Delivery of value from manufacturing data
Justification Data flow across different domains (e.g., product,
process, and logistics) Dynamic and predictive models Virtual and augmented reality
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Conjecture 3
Product-Material-Process Phenomenon Growing instances with the material, process, and product
developed simultaneously
Justification Design of a part that for which a new material
and a 3D printing process have been developed
A. Kusiak, Innovation Science, Nature, Vol. 530, No. 7590, February 2016
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Conjecture 4
Vertical Separability of the Physical Assets and the Cyberspace The physical and the logistics layers to be designed
for ease and speed of connecting and disconnecting
Justification The need to reconfigure physical assets driven by
the changing product needs
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Conjecture 5
Enterprise Dichotomy Two extreme smart enterprise models may emerge, one where
the physical and logistics layers are tightly horizontally connected and the other with vertical separability of the two layers
Justification The horizontal connectivity and the vertical separabilty models
may emerge as the result of Conjecture 3 and Conjecture 4, respectively
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Conjecture 6
Horizontal Connectivity and Interoperability Increase of horizontal internal and external connectivity
and interoperability
Justification The growing volume and flow rate of data across
an enterprise will naturally lead to greater horizontal connectivity and interoperability
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Conjecture 7
Resource Sharing Sharing manufacturing and transportation resources
across manufacturing chains will become a common practice
Justification Horizontal connectivity combined with dynamic
markets will facilitate sharing manufacturing equipment, transportation, and other resources
Expanding globalization and competition form emerging markets may enhance resource sharing
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Conjecture 8
Equipment Monitoring, Diagnosis, and Repair Autonomy Diagnosis and prediction of equipment faults will become
routine. Autonomous repair will occur.
Justification Sensors will provide data to monitor and predict health status
of equipment and systems.
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Conjecture 9
Cybersecurity and Safety Cybersecurity and safety issues will remain a challenge
Justification Increasing degree of automation, system autonomy, and
connectivity will raise the importance of cyber protection and human safety
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Conjecture 10
Standardization and Collaboration Collaborative development of standards may naturally emerge to
meet the emerging needs of integration and interconnectivity
Justification Growing reliance on data (Conjecture 1), resource sharing
(Conjecture 7), and the need for vertical separabilty (Conjecture 4) and horizontal connectivity and interoperability (Conjecture 6) will drive the need for standardization and collaboration
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New Platforms
Three practical steps need to be taken to accelerate progress in smart manufacturing
A. Kusiak, Smart Manufacturing Must Embrace Big Data, Nature, Vol. 544, No. 7648, April 2017
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Establishment of Cyber-platforms for Modeling, Sharing, and Innovation Online or physical spaces are needed enabling interaction
among experts and practitioners to develop models and technical solutions
Such platforms could mirror maker spaces or innovation hubs
Transparency and openness as well as diverse ideas and cultures should be supported
Schemes for modelers to access data are needed
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Enact Smart Manufacturing Policies
Government should fill the gaps lacking ownership or thatare too risky to pursue by private companies
The 2016 Report by the Information & Technology Innovation Foundation called upon the U.S. Congress to expand federal resources for training and to assist small and medium-sizebusinesses to adopt smart manufacturing technologies
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Data-Driven Manufacturing
Modeling from data
Solving data-derived models
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Modeling from Data
Model building
Model solving
Data
~½ Solution ~½ Solution
Application
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Extreme Learning
What is extreme learning?
Extreme learning machines involves feedforward neural networks for classification or regression with a single layer of hidden nodes
The value of the weights connecting inputs to hidden nodes are randomly assigned and never updated
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Extreme Learning Machine
Extreme Learning Machine (ELM)
Single hidden layer feedforward neural network
A three-step learning model
Offers favorable generalization and quick learning
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Deep Learning
What is deep learning?
Deep learning involves a class of machine learning algorithms that: Use multiple layers of nonlinear processing units for feature extraction
and transformation Learn multiple levels of representations corresponding to different levels
of abstraction May be supervised or unsupervised
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Algorithms
Deep Neural Networks (DNNs)
Involve of many hidden layers
Suitable for modeling complex non-linear problems
Used in both classification and regression
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Algorithms
Deep Auto-encoder
Intended for dimensionality reduction
Same number of input and output nodes
Unsupervised learning
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AlgorithmsDeep Belief Network (DBN)
Involves Restricted BoltzmannMachines (RBMs) where a sub-networkhidden layer serves as the visible layer forthe next layer
Has undirected connections at the top twolayers
Supports unsupervised and supervised learning
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Algorithms
Convolutional Neural Network
Inspired by the neurobiological model of the visual cortex
Well suited for 2D data such as images
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Model Solving
50
Evolutionary computation Particle swarm optimization Ant colony optimization Artificial immune system
Algorithms
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Innovation
Creation
Invention
Innovation
Market indicator 1
Mar
ket i
ndic
ator
2
Low High
High
High risk
Low success
rate path
A. Kusiak, Innovation Science, Nature, Vol. 530, No. 7590, Feb 2016
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Conclusion
Materials, products, and processes are becoming smarter, sustainable, energy aware, and innovation driven
Growing importance of data collection, analytics, modeling, and knowledge deployment
Co-dependence of materials, manufacturing processes, and products
Emergence of new manufacturing domains, e.g., healthcare
ISPR 2017