big data in manufacturing

8/10/2019 Big Data in Manufacturing

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C O V E R F E T U R E I P U T T I N G B IG D T T O W O R K

P U T T I N G B I G D A T A ~ W O R K

B Y H M E D N O O R


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I M A G I N E

W H A T F A C T O R I E S W I L L D O W H E N

T H E Y C A N T A K E F U L L A D V A N T A G E O F

A L L T H E I N F O R M A T I O N

A T

H A N D

Technologies ranging from supervisory

control and data acquisition to enterprise

resource planning have made factories smart

and they are getting smarter every day.

A

ta missile plant in Hun tsville,Ala.,for instance, R aytheon

Corp. monitorsi tsassembly operations down to the turn of a

screw.If ascrew is supposed to turn 3 times after itis

insertedin a missile com ponen t, but it turns only

2

times,

an error messageflashes, and productio n is halted until th e anomalyis

understood and rectified.

Just how m uch sm arter factories can become will be determined

largelyby the resea rch and d evelopm ent of systems to manage informa-

tion. Smartfactories, afterall, are part of the phenomenon popularly

knownasBig Data.

The challenge of Big Dataisthat it requires management tools to make

senseof large sets of heteroge neous information. In the case of

a

factory,

sources of data include CAD models, sensors, instruments, internet

transactio ns, simulationspotentially, re cords of

a ll

the digital sources

of information in the enterpr ise. The d ata bank is large, complex, and

often fast-moving, and so it becom es difficult to process

using

traditional

database analysis and management tools.

Raj^heon's monitoring technology

is

often called manufacturing execu-

tion software, and several manufacturers are currently using M E S to collect

and analyze factory-fioor data. The systems enab le the real-tim e control of

multipleelements of the production process.

Industry stands toreapmany benefits from

B ig

ata

a s

more sophisticated

and automateddataanalj^tics technologies are developed. These technologies


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will help extract value and hidden knowledge from large,

diverse data streams.

For example, the vehicle sensors in the Ford Focus electric

car produc e vast amoun ts of data while th e car is driven, and

even wh en it is parked. Wh ile the vehicle is in motion, th e

driver is constantly updated with information ahout accelera-

tion, braking, state of battery charge, distance to charge po ints,

and location. And while th e vehicle is at rest, the sensors con-

tinue to stream data about the tire pressure and battery system

The driver gets useful, up-to-the-second data, while Ford

engineers in Detroit use the car s communication modules

and remo te application m anagemen t software to aggregate

the information about driving behaviors in

order to gain custome r insights and plan prod-

uct improvem ents. In addition, third-party

vend ors, like General Electric, AeroViron-

men t, and ECOtality, analyze millions of miles

worth of driving data to decide where to lo-

cate new charging stations, and how to protect

the fragile utility grids from overloading.

Smart manufacturing has the potential of

fundamentally changing how products are

invented, manufactured, shipped, and sold.

A tjqsical factory uses information

technology, sensors, motors and actu ators,

computer ized controls, production manage-

me nt software, and the like to manage each

specific stage or operation of a man ufactur-

ing process. However, each plan t is an island

of efficiency.

RAYTHEON CO

www raytheon com

H E D Q U R T E R S W a l th a m , M a s s .

F O U N D E D

1 9 2 2 , a s th e A m e r ic a n

A p p l i a n c e C o .

E M P L O Y E E S 6 8 , 0 0 0 .

R E V E N U E S 2 4 b i l l i o n .

R a y t h e o n i s a n i n t e r n a t i o n a l a e r o s p a c e

a n d d e f e n s e c o m p a n y , w h o s e p r o d u c t s

r a n g e fr o m l a r g e - s c a l e r a d a r s t o c y h e r

s e c u r i t y te c h n o l o g y . I t s fo u r p r i n c i p a l

h u s i n e s s u n i t s a r e In t e g r a t e d D e f e n s e

S y s t e m s : I n t e l l i g e n c e , I n f o r m a t i o n ,

a n d S e r v ic e s : M i s s il e S y s t e m s :

S p a c e a n d A i r b o r n e S y s t e m s .

Smart manufacturing aims at integrating these islands,

enabling data shar ing throug hout the p lant and among plants

in a company.

A new wave of inexpensive electronic sensors, micropro-

cessors, and other components enables more automation in

factories, and huge amou nts of data to be collected along the way.

Managers can get instant alerts about potential problems or study

the data to find ways to boost efficiency and im prove perfor-

mance. In addition, enhanced knowledge of

the workplace promises to improve worker

safety and to protect the environment because

it can make zero-emission, zero-incident

manufacturing possible.

In automated manufacturing. Big Data

can help reduce defects and control costs

of products. By tracking every detail about

every part that goes into a product, from

its original manufacturer, to wh ere it was

stored, to when it was installed, the data

bank lets manufacturers retrace problems for

better resolution. Monitoring defect ratios

and on-time delivery can help with supplier

selection and performance assessment.

Inside the factory, having the ability to

utilize the mass of data on orders and m a-

chine status allows production m anagers to


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P 3 6 I O C T O B E R 2 0 1 3 I M E C H A N I C A L E N G I N E E R IN G

optimize operations, factory

scheduling, maintenance, and

workforce deployment.

A number of developments und er way

now aim to realize the greater potential of

Big Data in sma rt manu facturing.

The Smart Manufactur ing Leadership

Coalition, officially incorporated as a non-

profit o rganization in July

2 0 1 2

aims to

overcome the barriers to the development

and deployment of smart manufacturing

systems. The coalition's members include

stakeholders from industry, academia,

government, and manufacturing. The

organization supports development of

approaches, standards, platforms, and

shared infrastructure that will encourage

adoption of smart manufacturing by provid-

ing easy access, low ering cost, and resolv-

ing serious technological barriers found in

today's manufacturing environment.

Teams of mem bers are currently develop-

ing a shared, open-architecture infrastructure

called the Smart Manufacturing Platform that

allows manufacturers to assemble a combi-

nation of controls, models, and productivity

metrics to customize modeling and control

systems, mak ing use of Big Data fiow s from

fully instrum ented plants in real-dme. The

development of a smart manufacturing

platform is intended to help man ageme nt

across the manufacturing ecosystem, and to

forecast activities that, for example, can be

used to slash cycle times and reduce th e risks

of produ ct developm ent.

Th e infi astmcture will enable third parties

to develop networked industrial applications

that provide tools for resource metering,

automated data generation,

intuitive user interfaces, and

Big Data analjftics. Enhanced

computing power, an enabling

open-architecture infrastructure,

and software advances can make

data management invisible to the

engineer, the bulk of it happening

in the background.

Applications do not end at

the factory. Future products can

be outfitted with sensors that

connect to the cloud, enabling

after-sales service offerings.

Th ere could be an option in cars,

for instance, that will alert drivers

FOUNDED: 1956

REV ENU ES: 489 4 bi l l ion

or service centers when maintenance is needed. Data showing how customers use

products can suggest improvements in design or manufacturing.

mar t manu facturing is likely to evolve into the new

paradigm of cognitive manufacturing, in which

machining and measurem ents are merged in order

to form more flexible and controlled environments. When

unforeseen changes or significant alterations happen,

machining process p lanning systems receive on-line

measurement results, make decisions, and adjust machining

operation s accordingly in real time.

It is conceivable that one day machin es and pro cesses in a

factory will be equipp ed w ith capabilities that allow them to

assess and increase their scope of opera tion autonomo usly.

This changes the manufactur ing system from a determin-

istic one, wh ere all plannin g is carried out off-line, to a dy-

namic one that can determine and reason about processes,

plans, and operations.

Because of sma rt and dextero us robots, and novel

FANUC CORP

www fanuc co jp

HEADQUA RTERS: Japan:

O s h i n o - m u r a Y a m a n a s h i P r e fe c t o re .

U n i t e d S t a t e s ; F AN O C A m e r i c a

F AN U C d e s i g n s a n d m a n u f a c t u r e s

a o t o m a t io n t e c h n o l o g y f o r a l l s t a g e s

o f f a c t o r y p r o d u c t i o n l in e s .


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L E R N M O R E

For more information

on Big Data go to:

http://www aee odu edu/bigdata

The webs ite created

asa companiontothis

Mechanical ngineering

magazine fea ture

contains links to material

on Big Data inform ation

current activities

technologies and tools

educational and research

programs includingBig

Data University.

FORD MOTORCO

corporate ford com

H E A D Q U A R T E R S : D e arb o m , M ic h .

F O U N D E D : 19 03

E M P L O Y E E S : 1 7 5 ,0 0 0

R E V E N U E S : 1 3 4 .3 b i l l io n .

F o r d , o n e o f t h e t r a d i t i o n a l B ig

T h r e e a u t o m a k e r s i n t h e U n i t e d

S t a t e s , h a s w o r l d w i d e o p e r a t io n s

t h a t i n c l u d e 6 5 f a c t o r i e s . T h e

c o m p a n y s e l l s m o r e th a n 5 . 5

m i l li o n v e h i c l e s

a

y e a r .

manufacturing technolo-

gies many new produc-

tion methods will require

fewer people working

on the factory floor. For

example FANUC Robot-

ics a Japanese producer

of industrial rohots has

automated its newest tool

plant to the point where it

can run mostly unattended

for 700 hours. Many other

factories use processes

such as laser cutting and

injection molding that

operate without human intervention.

Manufacturing will still need people. This is because

automated machines require someone to service them.

Some machine operators will become machine minders

which often calls for a broader range of skills.

Throughout history new tools which make possible

new kinds of measurements and observations have led

to technological and scientific revolutions. Microbiology

was born in 1676 when the microscope discovered the

existence of microorganisms in a drop of water.

The emerging tools being developed to process and

manage the Big Data generated by mjriads of sensors and

other devices can lead to the next scientific technological

and management revolutions. The revolutions will enable

an interconnected efficient global industrial ecosystem

that will fundamentally change how products are invented

manufactured shipped and serviced.ME

AHMEDK NOOR

is

Eminent Scholar and William E. Lobeck Professor

of Modeling S imulatio n and Visualization EngineeringatOld Dominion

UniversityinNorfolk Va.

T O O L S N D T E C H N O L O G I E S

Tradit ionaldatawarehouses are not able

to

handle

the processing demands posedbyBig Data. Asa

result

a

new class

of

technology hasemerged.

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m i x tu r e o f d a t a , in c l u d i n g l o g f i le s , p i c t u re s ,

a u d i o f i le s , c o m m u n i c a t i o n s r e c o r d s , or e - m a i l,

r e g a r d le s s o f n a t iv e f o r m a t . H a d o o p k e e p s

a l l t h e d a t a o n l i n e fo r r e a l - t i m e i n t e r a c t i v e

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i n d u s t r i e s , i n c l u d i n g t e l e c o m , h e a l t h c a r e

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u s e s H a d o o p s o ft w a r e t o m a n a g e ti m e -

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t i p l e n o d e s , h e l p i n g t o m a n a g e th e i n f lu xo f t i n y

p i e ce s o f d a t a t h a t c o m e i n a l m o s t c o n s t a n t l y .

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f o r B i g D a t a a p p l ic a t i o n s , i n c l u d i n g M i c r o s o f t

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IT .

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d a t a m i n i n g , d is c o v e r y ,a n d r e p o r t in g fo r d i v e r s e

i n fo r m a t i o n c o n s u m e r s .

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a n d r e s u l t s t o c r e a t e a n d r e t a i n i n f o r m a t i o n

r e l a t io n s h i p s , h i s t o r y , a n d c o n t e x t .

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fo r

a

v a r i e ty o f a p p l ic a t i o n s . T h e IB M p l a t f o r m

c o v e rs s y s t e m m a n a g e m e n t , a p p l ic a t io n

d e v e l o p m e n t , a n d v i s u a l i z a t io n a n d d i s c o v e r y .

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t o d e v e l o p a n d d e p l o y c o n n e c t e d c a r s o l u t i o n s

t h a t e n a b l e v e h i c l e - t o - v e h i c l e , v e h i c l e - t o - d r i v e r ,

a n d v e h i c le - t o - in f r a s t r u c t u r e c o m m u n i c a t io n

i n r e a l ti m e .

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v i c e ,d e v e l o p e d b y G o o g l e fo r m a s s iv ed a t a s e t s .

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S o l u t io n is a

P R O C E S S I N G H R D W R E

c h i p d e s i g n e d

by E T N

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s y s t e m s t o e n t e r p r is e b u s i n e s s s y s te m s .

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a

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f o r m e r l y k n o w n a s N u m e n t a , is b u i ld i n g a

n e u r o s c i e n c e - in s p i r e d e n g i n e t o f i n d p a t te r n s

i n la r g e d a t a s t r e a m s a n d t o g e n e r a t e p r a c t i c a l

p r e d i c t i o n s inr e a l t im e .

A

n u m b e r o f a t t e m p t s a re b e i n g m a d e t o

d e v e l o p c o g n i t iv e c o m p u t e r s th a t c a n e f f e c t i v e l y

p r o ce s s B i g D a ta , t h r o u g h e m u l a t in g t h e h u m a n

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P r o j e c t A g e n c y 's S y s t e m s o f N e u r o m o r -

p h i c A d a p t i v e P l a s t i c S c a l a b l e E l e c t r o n i c s

I S y N A P S E ) P r o j e c t . T h e g o a l o f th e p r o j e c t i s

t o d e v e lo p e l e c t r o n i c n e u r o m o r p h ic m a c h i n e

t e c h n o l o g y t h a t s c a l e s t o b i o l o g i c a l l e v e l s .

R e s e a r c h e rs i n c l u d e t e a m s f r o m I B M , H u g h e s

R e s e a r ch L a b s ,a n dA c a d e m i c in s t i t u t i o n s .


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C o p y r i g h t o f M e c h a n i c a l E n g i n e e r i n g i s t h e p r o p e r t y o f A m e r i c a n S o c i e t y o f M e c h a n i c a l

E n g i n e e r s a n d i t s c o n t e n t m a y n o t b e c o p i e d o r e m a i l e d t o m u l t i p l e s i t e s o r p o s t e d t o a l i s t s e r v

w i t h o u t t h e c o p y r i g h t h o l d e r ' s e x p r e s s w r i t t e n p e r m i s s i o n . H o w e v e r , u s e r s m a y p r i n t ,

d o w n l o a d , o r e m a i l a r t i c l e s f o r i n d i v i d u a l u s e .

big data in manufacturing

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