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Machine Tool Automation 48.4 Technical Innovation in NC Machine Tools 855

Fig. 48.30a–d Example of real-time tool-path generation.

(a) Bilateral zigzag paths; (b) contouring paths; (c) change

of stepover;

(d) change of cutting depth 

Cutting torque (Nmm)

1104

Feed rate (mm/min)

Cutting depth (mm)

RD 4 AD 2.7

650

Fig. 48.31 Adaptive milling on AIMac

S = 1680 rpm, F = 241 mm/min

RD = 4.5 mm, AD = 2.1 mm

S = 2450 rpm, F = 346 mm/min

RD = 1.6 mm, AD = 3.8 mm

S = 1000 rpm, F = 230 mm/min

RD = 8 mm, AD = 5 mm

S = 2450 rpm, F = 346 mm/min

RD = 1.6 mm, AD = 3.8 mm

S = 3539 rpm, F = 413 mm/min

RD = 1.2 mm, AD = 2.1 mm

S = 3539 rpm, F = 413 mm/min

RD = 1.2 mm, AD = 2.1 mm

S = 2450 rpm, F = 346 mm/min

RD = 1.6 mm, AD = 3.8 mm

S = 848 rpm, F = 180 mm/minS = 1154 rpm, F = 232 mm/min

S = 2580 rpm, F = 335 mm/min

RD = 1.3 mm, AD = 3.2 mm

100

End mill Ø16 mm

Scanning-line mode

End mill Ø10 mm

Contour-line mode

Face mill Ø80 mm

Scanning-line mode

Raw material shape Finished shape

Face

End mill Ø10 mm

Scanning-line mode

Closed pocket

Open pocket

Closed slot

End mill Ø6 mm

Scanning-line mode

End mill Ø6 mm

Scanning-line mode

End mill Ø10 mm

Scanning-line mode

Closed slot

Ball end mill Ø10 mm

Scanning-line mode

Closed slot

Drill Ø10 mm

Drilling mode

Center drill Ø3 mm

Drilling mode

Blind hole

Open slot

Free form

Fig. 48.32 Results of machining process planning on AIMac

Change of cutting depth

Change of

cutting depth

Change of stepover

a) b)

c) d)

Part F 48.4

856 Part F Industrial Automation

Flexible Process

and Operation Planning System

A ﬂexible process and operation planning system has

been developed to generate cutting parameters dynami-

cally for machining operation. The system can generate

the production plan from the total removal volume

(TRV). The TRV is extracted from the initial and

ﬁnished shapes of the product and is divided into ma-

chining primitives or machining features. The ﬂexible

process and operation planning system can generate

cutting parameters according to the machining features

detected. Figure 48.32 shows the operation sequence

and cutting tools to be used. Cutting parameters are

determined for the experimental machining shape. The

digital copy milling system can generate the tool paths

or CL data dynamically according to these results and

perform the autonomous milling operation without re-

quiring any NC program.

48.5 Key Technologies for Future Intelligent Machine Tool

Several architectures and technologies have been pro-

posed and investigated as mentioned in the previous

sections. However, they are not yet mature enough to

be widely applied in practice, and the achievements of

these technologies are limited to speciﬁc cases.

Achievements of key technologies for future in-

telligent machine tools are summarized in Fig.48.33.

Process and machining quality control will become

more important than adaptive control. Dynamic tool-

path generation and in-process cutting parameters

modiﬁcation are required to realize ﬂexible machining

operation for process and machining quality control.

Additionally, intelligent process monitoring is needed

to evaluate the cutting process and machining quality

Motion control

Adaptive control

Process and quality control

Monitoring (sensing)

Intelligent process monitoring

Open architecture concept

Process planning

Operation planning

Utilization of knowhow

Learning of knowhow

Network communication

Distributed computing

Key technologies Conceptual >>>>> >>>>>Confirmed Practical

Fig. 48.33 Achievements of key technologies for future intelligent machine tools

for process and machining quality control.A reasonable

strategy to control the cutting process and a reasonable

index to evaluate machining quality are required. It is

therefore necessary to consider utilization and learning

of knowledge, knowhow, and skill regarding machining

operations.

A process planning strategy with which one can

generate ﬂexible and adaptive working plans is re-

quired. An operation planning strategy is also required

to determine the cutting tool and parameters. Prod-

uct data analysis and machining feature recognition are

important issues in order to generate operation plans

autonomously.

Sections 48.4.2–48.5 are quoted from [48.14].

Part F 48.5

Machine Tool Automation References 857

48.6 Further Reading

•

Y. Altintas: Manufacturing Automation: Metal Cut-

ting Mechanics, Machine Tool Vibrations, and CNC

Design (Cambridge Univ. Press, Cambridge 2000)

•

J.G. Bollinger, N.A. Dufﬁe: Computer Control of

Machines and Processes (Addison-Wesley, Boston

1988)

•

E.P. DeGarmo, J.T. Black, R.A. Kosher: DeGarmo’s

Materials and Processes in Manufacturing (Wiley,

New York 2007)

•

K. Evans: Programming of CNC Machines (Indus-

trial, New York 2007)

•

Y. I t o : Modular Design for Machine Tools

(McGraw-Hill, New York 2008)

•

K.-H. John, M. Tiegelkamp: IEC 61131-3: Pro-

gramming Industrial Automation Systems (Springer,

Berlin Heidelberg 2001)

•

S. Krar, A. Gill, P. Smid, P. Wanner: Machine

Tool Technology Basics (Industrial, New York

2003)

•

I.D. Marinescu, C. Ispas, D. Boboc: Handbook

of Machine Tool Analysis (CRC, Boca Raton

2002)

•

B.W. Niebel, A.B. Draper, R.A. Wysk: Modern

Manufacturing Process Engineering,(McGraw-

Hill, London 1989)

•

G.E. Thyer: Computer Numerical Control of

Machine Tools, (Butterworth-Heinemann, London

1991)

•

K.-H. Wionzek: Numerically Controlled Machine

Tools asa SpecialCase ofAutomation(Didaktischer

Dienst, Berlin 1982)

References

48.1 Y. Koren: Control of machine tools, ASME J. Manuf.

Sci. Eng. 119, 749–755 (1997)

48.2 G. Pritschow, Y. Altintas, F. Jovane, Y. Koren,

M. Mitsuishi, S. Takata, H. Brussel, M. Weck,

K. Yamazaki: Open controller architecture – past,

present and future, Ann. CIRP 50(2), 463–470 (2001)

48.3 Y. Altintas, N.A. Erol: Open architecture modular

tool kit for motion and machining process control,

Ann. CIRP 47(1), 295–300 (1998)

48.4 M. Mori, K. Yamazaki, M. Fujishima, J. Liu, N. Fu-

rukawa: A study on development of an open servo

system for intelligent control of a CNC machine tool,

Ann. CIRP 50(1), 247–250 (2001)

48.5 K.Yamazaki,Y.Hanaki,Y.Mori,K.Tezuka:Au-

tonomously proﬁcient CNC controller for high

performance machine tool based on an open ar-

chitecture concept, Ann. CIRP 46(1), 275–278 (1997)

48.6 H.Ng,J.Liu,K.Yamazaki,K.Nakanishi,K.Tezuka,

S. Lee: Autonomous coordinate measurement

planning with work-in-process measurement for

TRUE-CNC, Ann. CIRP 47(1), 455–458 (1998)

48.7 X. Yan, K. Yamazaki, J. Liu: Extraction of milling

know-how from NC programs through reverse en-

gineering, Int. J. Prod. Res. 38(11), 2443–2457 (2000)

48.8 M. Mitsuishi, T. Nagao, H. Okabe, M. Hashiguchi,

K. Tanaka: An open architecture CNC CAD-CAM ma-

chining system with data-base sharing and mutual

information feedback, Ann. CIRP 46(1), 269–274

(1997)

48.9 M. Mitsuishi, T. Nagao: Networked manufacturing

with reality sensation for technology transfer, Ann.

CIRP 48(1), 409–412 (1999)

48.10 K. Morishige, Y. Takeuchi, K. Kase: Tool path gener-

ation using C-space for 5-axis control machining,

ASME J. Manuf. Sci. Eng. 121, 144–149 (1999)

48.11 K. Morishige, Y. Takeuchi: Strategic tool atti-

tude determination for ﬁve-axis control machining

basedonconﬁgurationspace,CIRPJ.Manuf.Syst.

31(3), 247–252 (2003)

48.12 K. Shirase, T. Kondo, M. Okamoto, H. Wakamatsu,

E. Arai: Trial of NC programless milling for a basic

autonomous CNC machine tool, Proc. 2000 JAPAN-

USA Symp. Flex. Autom. (JUSFA2000) (2000) pp. 507–

513

48.13 K. Shirase, K. Nakamoto, E. Arai, T. Moriwaki: Dig-

ital copy milling – autonomous milling process

control without an NC program, Robot. Comput.

Integr. Manuf. 21(4-5), 312–317 (2005)

48.14 T. Moriwaki, K. Shirase: Intelligent machine

tools: current status and evolutional architecture,

Int. J. Manuf. Technol. Manag. 9(3/4), 204–218

(2006)

Part F 48

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859

Digital Manuf

49. Digital Manufacturing

and RFID-Based Automation

Wing B. Lee, Benny C.F. Cheung, Siu K. Kwok

Advances in the Internet, communication tech-

nologies, and computation power have accelerated

the cycle of new product development as well

as supply chain efﬁciency in an unprecedented

manner. Digital technology provides not only an

important means for the optimization of produc-

tion efﬁciency through simulations prior to the

start of actual operations but also facilitates man-

ufacturing process automation through efﬁcient

and effective automatic tracking of production

data from the ﬂow of materials, ﬁnished goods,

and people, to the movement of equipment and

assets in the value chain. There are two major

applications of digital technology in manufactur-

ing. The ﬁrst deals with the modeling, simulation,

and visualization of manufacturing systems and

the second deals with the automatic acquisition,

retrieval, and processing of manufacturing data

used in the supply chain. This chapter summarizes

the state of the art of digital manufacturing which

is based on virtual manufacturing (VM) simulation

and radio frequency identiﬁcation (RFID)-based

automation. The associated technologies, their key

techniques, and current research work are high-

lighted. In addition, the social and technological

obstacles to the development of a VM system and

in an RFID-based manufacturing process automa-

tion system, and some practical application case

studies of digital manufacturing based on VM and

RFID-based automation, are also discussed.

49.1 Overview.............................................. 859

49.2 Digital Manufacturing

Based on Virtual Manufacturing (VM)...... 860

49.2.1 Concept of VM............................ 860

49.2.2 Key Technologies Involved in VM .. 861

49.2.3 Some Typical Applications of VM... 862

49.2.4 Beneﬁts Derived from VM ............ 863

49.3 Digital Manufacturing

by RFID-Based Automation.................... 864

49.3.1 Key RFID Technologies................. 865

49.3.2 Applications

of RFID-Based Automation

in Digital Manufacturing ............. 867

49.4 Case Studies of Digital Manufacturing

andRFID-BasedAutomation.................. 867

49.4.1 Design of Assembly Line

and Processes for Motor Assembly 867

49.4.2 A VM System for the Design

and the Manufacture

of Precision Optical Products........ 868

49.4.3 Physical Asset Management (PAM) 869

49.4.4 Warehouse Management............. 872

49.4.5 Information Interchange

in Global Production Networks..... 874

49.4.6 WIP Tracking.............................. 876

49.5 Conclusions.......................................... 877

References .................................................. 878

49.1 Overview

The industrial world is undergoing profound changes

as the information age unfolds [49.1]. The competi-

tive advantage in manufacturing has shifted from the

mass-production paradigm to one that is based on fast

responsiveness and on ﬂexibility [49.2]. One of the

important issues in manufacturing is related to the in-

tegration of engineering and production activities. This

includes integration of developers, suppliers, and cus-

Part F 49

860 Part F Industrial Automation

tomers through the entire production cycle, involving

design, production, testing, servicing, and marketing.

The scope of digital manufacturing includes manifest-

ing physical parts directly from three dimensional (3-D)

computer-aided design (CAD) ﬁles or data, using addi-

tive fabrication techniques such as 3-D printing, rapid

prototyping from virtual manufacturing (VM) models,

and the use of radiofrequency identiﬁcation (RFID)

for supporting manufacturing process optimization and

resources planning. Examples can be found in Red-

Eye [49.3], Stratasys [49.4], and Autodesk [49.5]. To

achieve the integration, digital manufacturing, which

covers all the engineering functions, information ﬂow,

and the precise characteristics of a manufacturing sys-

tem are needed. Manufacturing enterprises are now

forced to digitize manufacturing information and accel-

erate their manufacturinginnovation in order to improve

their competitive edge in the global market.

There are two major applications of digital tech-

nology in manufacturing. One is based on virtual

manufacturing, which deals with the modeling, simu-

lation, and visualization of manufacturing systems. The

second is based on the automation of the manufactur-

ing process and deals with the automatic acquisition,

retrieval, and processing of manufacturing data en-

countered in the supply chain. In this chapter, the state

of the art of digital manufacturing based on recent

advances in VM and RFID-based automation is sum-

marized. The concept and beneﬁts of VM and RFID

are presented, while the associated technologies, their

key techniques, and current research work are also

highlighted. The social and technological obstacles in

the development of a VM system and an RFID-based

manufacturing process automation system together with

some case studies are discussed at the end of the

chapter.

49.2 Digital Manufacturing Based on Virtual Manufacturing (VM)

49.2.1 Concept of VM

Digital manufacturing based on VM integrates man-

ufacturing activities dealing with models and sim-

ulations, instead of objects and their operations in

the real world. This provides a digital tool for the

Comparison

and model

maintenance

Virtual

prototyping

Product

model

Manufacturing

resource model

Manufacturing environment model

Manufacturing environment

Prototype and product Manufacturing resources

Monitoring

Virtual world

Real world

Manufacturing

preparation

Product

design

Production

management

Task organization

Engineering activity

Fig. 49.1 Conceptual view of a virtual manufacturing system (after

Kimura et al. [49.6])

optimization of the efﬁciency and effectiveness of

the manufacturing process through simulations prior

to actual operation and production. A VM system

can produce digital information to facilitate physical

manufacturing processes. The concept, signiﬁcance,

and related key techniques of VM were addressed

by Lawrence Associate Inc. [49.7] while the contri-

bution and achievements of VM were reviewed by

Shukla [49.8]. As mentioned by Kimura [49.6], a typ-

ical VM system consists of a manufacturing resource

model, a manufacturing environment model, a prod-

uct model, and a virtual prototyping model. Some

active research work is found in the study of both

conceptual and constructive VM systems. Onosato and

Iwata [49.9] developed the concept of a VM system,

and Kimura [49.6] described the product and process

model of a VM system. Based on the concept and the

model, Iwata et al. [49.10] proposed a general modeling

and simulation architecture for a VM system. Gause-

meier et al. [49.11] has developed a cyberbike VM

system for the real-time simulation of an enterprise that

produces bicycles. With the use of a VM system, people

can observe the information in terms of the structure,

states, and behaviors equivalent to a real manufactur-

ing environment, in a virtual environment [49.12, 13].

Various manufacturing processes can be integrated and

realized in one system so that manufacturing cost and

Part F 49.2

Digital Manufacturing and RFID-Based Automation 49.2 Digital Manufacturing Based on Virtual Manufacturing (VM) 861

time to market can be reduced and productivity can be

signiﬁcantly improved.

A conceptual view of a VM system according to

Kimura [49.6] is shown in Fig.49.1. The manufactur-

ing activities and processes are modeled before, and

sometimes in parallel with, the operations in the real

world. Interaction between the virtual and real worlds is

accomplished by continuous monitoring of the perfor-

mance of the VM system. Since a VM model is based

on real manufacturing facilities and processes, it pro-

vides realistic information about the product and its

manufacturing processes, and also allows for their eval-

uation and validation. Since no physical conversion of

materials into products is involved in VM, this helps

to enhance production ﬂexibility and reduce the cost of

production as the cost for making the physical proto-

types can be reduced.

Basically, the classiﬁcation of a VM system is based

on the type of system integration, the product and

process design, and the functional applications. Ac-

cording to the deﬁnitions proposed by Onosato and

Iwata [49.9], every manufacturing system can be de-

composed into four different subsystems: a real and

physical system(RPS), a real information system (RIS),

a virtual physical system (VPS), and a virtual infor-

mation system (VIS). A RPS consists of substantial

entities such as materials, parts, and machines that ex-

ist in the real world, while a RIS involves the activities

of information processing and decision making such as

design, scheduling, controlling, and prediction, etc. On

the other hand, a computer system that simulates the re-

sponses of a real physical system is a virtual physical

system, which can be represented by a factory model,

product model, and a production process model. The

production process models are used to determine the in-

teractions between the factory model and each of the

product models. A VIS isa computersystem which sim-

ulates a RIS and generates control commands for the

RPS.

VM can be subdivided into product-design-centered

VM, production-centered VM, and control-centered

VM according to the product design and process de-

sign functions. The product-design-centered VM makes

use of different virtual designs to produce the produc-

tion prototype. The relevant information about a new

product (product features, tooling, manufacturability,

etc.) is provided to the designer and to the manufac-

turing system designers to support decision making in

the product design process. Production-centered VM

is based on the RPS or VPS to simulate the activities

in process development and alternative process plans

while the control-centered VM aims at optimizing the

production cycles based on dynamic control of process

parameters.

Production-centered VM, on the other hand, is

based on the functional use so as to provide interactive

simulation of various manufacturing or business pro-

cesses such as virtual prototyping, virtual operational

system, virtual inspection, virtual machining, virtual as-

sembly, etc. Virtual prototyping (VP) mainly deals with

the processes, tooling, and equipment such as injec-

tion molding processes, while virtual machining mainly

deals with cutting processes such as turning, milling,

drilling and grinding, etc. Finally, control-centered VM

technology is used to study the factors affecting the

quality, machining time, and costs based on the mod-

eling and simulation of the material removal process as

well as on the relative motion between the tool and the

workpiece. Virtual inspection makes use of VM tech-

nology to model and simulate the inspection process,

and the physical and mechanical properties of the in-

spection equipment. The aim of this is to study the

inspection methodologies,inspection plans,and the fac-

tors affecting the accuracy ofthe inspection process,etc.

In assembly work, VM is mainly used to investigate

the assembly processes, the mechanical and physical

characteristics of the equipment and tooling, and the in-

terrelationship between different parts so as to predict

the quality of an assembly or product cycle. It will also

examine the costs as well as evaluating the feasibility

of the plan of the assembly process. VM can also be

used for virtual operational control, the aim of which is

to evaluate the design and operational performance of

the material ﬂow and information ﬂow system, etc. VM

technology is also used for investigating the human be-

havior of the different workers who handle the various

tasks such as assembling parts, queuing, handling doc-

uments, etc. The human factors affecting the operation

of a manufacturing or business system can be predicted

and evaluated.

49.2.2 Key Technologies Involved in VM

The development of VM demands multidisciplinary

knowledge and technologies related to computer hard-

ware and software, information technology, microelec-

tronics, manufacturing, and mathematical computation.

The key technological areas related to VM are:

1. Visualization technologies

VM makes useof directgraphic interfaces todisplay

highly accurate, easily understandable, and accept-

Part F 49.2

862 Part F Industrial Automation

Fig. 49.2 Evaluation of product design

able input and output information for the user. This

demands advanced visualization technologies such

as image processing, virtual reality (VR), multime-

dia, design of graphic interfaces, animation, etc.

2. Techniques for the establishment of a virtual manu-

facturing environment

A computerized environment for VM operations is

vital. This includes the hardware and software for

the computer, modeling and simulation of the in-

formation ﬂow, support of the interface between the

real and the virtual environment, etc. This needs the

research and development of devices for VM oper-

ational environment, interface and control between

the VM system and the real manufacturing (RM)

system, information and knowledge integration and

acquisition, etc.

The selected block

with accident error

is highlight

a) b)

Identify the errors and

give suggestions for

correcting them

Fig. 49.3a,b NC program validation

3. Information-integrated infrastructure

This refers to the hardware and software develop-

ment for supporting the models and the sharing

of resources, i.e., information and communication

technologies (ICT) among dispersed enterprises.

4. Methods of information presentation

The information about product design and man-

ufacturing processes and related solid objects are

represented using different data formats, languages,

and data structureso asto achievethe data sharingin

the information system. There is a need for research

into advanced technologiesfor 3-D geometrical rep-

resentation, knowledge-based system description,

rule-based system description, customer-orientated

expert systems, feature-based modeling, physical

process description, etc.

5. Model formulation and reengineering techniques

In order to deﬁne, develop, and establish meth-

ods and techniques which are capable of realizing

the functions and interrelationships among various

models in the VM system, various techniques are

employed, including model exchange, model man-

agement, model structure, data exchange, etc.

6. Modeling and simulation techniques

These refer to processes and methods used to mimic

the real world, on the computer. Further research

on the technologies related to dispersed network

modeling, continuous system modeling, model

databases and their management optimization anal-

ysis, validation of simulation results, development

of simulation tools, and software packaging tech-

nique are much needed.

7. Veriﬁcation and evaluation

To ensure that the output from the VM system is

equivalent to that from the RM system, related tech-

nologies such as standards of evaluation, decision

tools, and evaluation methods are needed. These

methods are useful for verifying and evaluating the

performance and reliability of different models in

VM systems and their outputs.

Some of these technologies are comparatively ma-

ture. However, most of them have to be further

developed before they can be used to form an integrated

VM platform.

49.2.3 Some Typical Applications of VM

1. VM can be used in the evaluation of the feasibil-

ity of a product design, validation of a production

and business plan, and optimization of the product

Part F 49.2

Digital Manufacturing and RFID-Based Automation 49.2 Digital Manufacturing Based on Virtual Manufacturing (VM) 863

Interactive user interface

NC program

Control

command

Tooling

information

Workpiece

information

Stereo sound

3-D graphics

Animation

Errors/warning

messages

Data

buffer

Internal

database

Information

module

Control panel

(control screen)

Virtual objects

(e.g. virtual machine and

inspection equipment)

Virtual

environment

simulator

Trainee

a) b)

Event processing

module

Fig. 49.4a,b Virtual training workshop (VTW) for ultraprecision machining center

design and of the business processes. Fine-tuning

of these will result in reduction of the cost of the

product throughout its lifecycle.

2. As shown in Figs. 49.2 and 49.3, VM can be used

to test and validate the accuracy of the product

and business process designs, for example, the de-

sign of the appearance of a product, analysis of

its dynamic characteristics, checking for the tool

path during machining process, numerical control

(NC) program validation, checking for possible

collision problems in machining [49.14] and assem-

bly [49.15].

3. Withthe useof VM, itis possible toconduct training

(Fig.49.4) in a distributed virtual environment for

the operators, technicians, and management people

on the use of manufacturing facilities. The costs of

training and production can thus be reduced.

4. As a knowledge-acquisition vehicle, VM can be

used to acquire continuously the manufacturing

or business knowhow, traditional manufacturing or

business processes, production data, etc.

49.2.4 Beneﬁts Derived from VM

1. VM can be used not only to predict the costs of

product or process development but can also be

used to provide the information about the process

capability [49.16]. It allows for the modeling and

simulation of the activities involved in process de-

velopment and also for alternative process plans.

It also enables the rapid evaluation of a produc-

tion plan, the evaluation of the operational status

of a manufacturing system as well as of the objec-

tives of the designof the physical system such as the

degree of optimization of manufacturing resources

and facilities, etc. The information generated from

VM system is useful for improving the accuracy of

the decisions made by the designer and the man-

agement. Possible problems in product or process

development can be predicted and resolved prior to

the actual operation.

2. VM can support VP, which simulates the materials,

processes, tooling, and equipment in the fabrica-

tion of prototypes. The factors affecting the process,

product quality, and hence the material properties,

processing time, and manufacturing costs can be

analyzed with the use of modeling and simula-

tion techniques. As more computer-based product

models are developed and prototyped upstream in

the product development process using VM,the

number of downstream physical prototypes tradi-

tionally made to validate the product models and

new designs can be reduced. Hence, the product

development time can be shortened.

3. With the virtual environment provided by VM

[49.17], customers can take part in the product de-

velopment process. Design engineers can respond

more quickly to customer queries and hence pro-

vide better solutions to the customers. Discussion,

manipulation, and modiﬁcation of the product data

model directly among personnel with different tech-

nical backgrounds arealso facilitated. As a result the

competitive edge of an enterprise in the market can

be enhanced.

Part F 49.2

864 Part F Industrial Automation

49.3 Digital Manufacturing by RFID-Based Automation

Another major application of digital manufacturing

deals with the automatic acquisition and processing of

manufacturing data in the supply chain. This is due

to the fact that the keen competition in global manu-

facturing has rekindled interest in lean manufacturing,

reducing inventory, and efﬁciency in production con-

trol. There has been growing interest worldwide in the

use of RFID to digitalize the manufacturing information

so as to automate the manufacturing process. As techni-

cal problems are slowly being overcome and the cost of

using RFID is decreasing, RFID is becoming popular in

manufacturing industries. According to industrial statis-

tics, the worldwide market for RFID technology was

US$1.49billion in 2004. In the ﬁrst 6 months of 2008,

6.8billion tags were sold for these applications as well

as 15.3billion tags for pallets and cases. There is great

market demand, which is ever increasing, for various

RFID applications. It is predicted that RFID industry

ﬁgures will increase from US$ 1.95 billion in 2005 to

US$26.9billion in 2015 [49.18].

The rapid increase in use of RFID technology in the

retail industry has been driven by major players such as

Gillette, Tesco, Wal-Mart, and Metro AG in Germany.

Wal-Mart, the world’s largest retailer, has started

deploying RFID applications and implementing new

procedures in some ofits distribution centers and stores.

By January 1st, 2005, Wal-Mart required its top 100

suppliers to put RFID tags on shipping crates and pal-

lets, and by January 1st, 2006, this was expanded to

its next 200 largest suppliers. The aim of the applica-

tions is to reduce out-of-stock occurrences by providing

visibility of the location of goods by using RFID tags.

Out-of-stock items that are RFID-tagged have been

found to be replenished three times faster than before,

and the amount of out-of-stock items that have to be

manually ﬁlled has been cut by 10%.

Gillette and Tesco implemented an item-level RFID

project in the UK. Gillette razor blade cartridges were

tagged with RFID tags and Tesco, the retailer, used an

RFID reader embedded smart shelf system to search for

items in the ﬁeld, in order to take on-shelf inventories.

Metro AG has implemented an item-level RFID trial

for use in its future stores. RFID tags are attached to

each pack of Gillette razor blades, Proctor and Gamble

(P&G) shampoos, Kraft cream cheese, and digital ver-

satile disks (DVDs). In addition to enhancing stock re-

plenishment operations, consumers also beneﬁt. Shop-

ping trolleys which automatically update shopping lists

and self-check-out systems have also been implemented

using this technology. This demonstrates how RFID can

revamp the retail industry and provide new customer

experiences. With better tag and reader technology, de-

clines in the cost of RFID tagging, and the release of

information-sharing platforms, it is likely that RFID

will be widely adopted across the entire supply chain.

In recent years, the use of RFID has enabled real-

time visibility and increased the processing efﬁciency

of shop-ﬂoor manufacturing data. RFID also supports

information ﬂow in process-linked applications. More-

over, it can help to minimize the need for reworking,

improve efﬁciency, reduce line stoppages, and replenish

just-in-time materials on the production line. RFID can

assist in automating assembly-line processes and thus

reduce labor and cost, and minimize errors on the plant

ﬂoor. The integration of RFID with various manufac-

turing systems is still a challenge to many corporations.

As most large retailers will gradually demand the use

of RFID in the goods from their suppliers, this cre-

ates both pressure and the opportunity for small and

medium sized enterprises (SMEs) to adopt this tech-

nology in their logistics operations and extend it to the

control of their manufacturing processes. Some previ-

ous work [49.19] has discussed the point that RFID

can be more cost effective in bridging the gap between

automation and information ﬂow by providing better

traceability and reliability on the shop ﬂoor.

Traditional shop-ﬂoor control in a production envi-

ronment, although computerized, still requires manual

input of shop-ﬂoor data to various systems such as

the enterprise resources planning (ERP) for production

planning and scheduling. Such data includes product

characteristics, labor, machinery, equipment utilization,

and inspection records. Companies such as Lockheed

Martin, Raytheon, Boeing [49.20], and Bell Helicopter

have installed lean data-capture software and technolo-

gies and are in the process of converting barcodes to

RFID. Honeywell was using barcodes to collect data

related to part histories, inventories, and billing, and

sharing data with its clients, and is accelerating its plan

to switch from barcodes to RFID. The use of RFID has

several advantages over barcodes, as tags that contain

microchips that can store as well as transmit dynamic

data have a fast response, do not require line of sight,

and possess high security. RFID offers greater scope

for the automation of data capture for manufacturing

process control. In recent years, the cost of RFID tags

has continuously decreased [49.21,22] while their data

capability hasincreased, whichmakes practicalapplica-

Part F 49.3