a big data perspective - ispr 2018 · 2017-12-06 · smart manufacturing: contributing computing...

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


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


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


Smart Manufacturing Concept

Interface Standard connectivity

Cyberspace System intelligence

Data Decisions

Data Decisions

Manufacturing equipment Local intelligenceA. Kusiak, Smart Manufacturing, IJPR 2017 (published online)


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)


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


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.


Classical Control

Industrial process

Controller

PP0Knownset point

Adjustable input

Today’s manufacturing: Known set point = Production output


Wind Turbine ControlA

ntic

ipat

ory

Con

trol Wind

Turbine

Controller

PP0Unknownset point

Non adjustable input

Tomorrow’s manufacturing: Predicted set point = Production output


Intelligent Manufacturing: ‘History’

1990


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


International Activities in Intelligent Manufacturing

12

IMS Program (Japan, 1995) NGMS, IMS (CAM-I, USA) IMS EU


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.


New Manufacturing Initiatives

Industrie 4.0 (Germany) Factories of the Future (EU)Made in China 2025


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


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


Smart Factory

Primary differentiators: Predictive engineering Seeing the future

Sustainability (including energy and transportation) From product conception to the end-of-life


Product End-of-Life

Reuse (most preferred) Remanufacture Recycle Disposal (should disappear)

Restored 1949 VW Bug

http://upload.wikimedia.org/wikipedia/en/c/c9/Plastic_recycle.jpg

http://upload.wikimedia.org/wikipedia/commons/5/59/1949_VW_Beetle.jpg


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


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


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


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


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?


What Have we Learned from Them?

Using customers to design products

Innovation


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


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


The Future of Smart Manufacturing

Imagining the future of smart manufacturing

Ten conjectures

A. Kusiak, Smart Manufacturing, IJPR 2017 (published online)


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


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


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


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


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


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


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


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.


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


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


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


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


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


Data-Driven Manufacturing

Modeling from data

Solving data-derived models


Modeling from Data

Model building

Model solving

Data

~½ Solution ~½ Solution

Application


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


Extreme Learning Machine

Extreme Learning Machine (ELM)

Single hidden layer feedforward neural network

A three-step learning model

Offers favorable generalization and quick learning


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


Algorithms

Deep Neural Networks (DNNs)

Involve of many hidden layers

Suitable for modeling complex non-linear problems

Used in both classification and regression


Algorithms

Deep Auto-encoder

Intended for dimensionality reduction

Same number of input and output nodes

Unsupervised learning


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


Algorithms

Convolutional Neural Network

Inspired by the neurobiological model of the visual cortex

Well suited for 2D data such as images


Model Solving

50

Evolutionary computation Particle swarm optimization Ant colony optimization Artificial immune system

Algorithms


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


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

a big data perspective - ispr 2018 · 2017-12-06 · smart manufacturing: contributing computing...

Documents