RECONFIGURABLE MANUFACTURING SYSTEMS.pdf

8/14/2019 RECONFIGURABLE MANUFACTURING SYSTEMS.pdf

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CONTENTS

ABSTRACT..................................................................................................... ........................2

1 INTODUCTION....................................................................................................................3

2 RECONFIGURABLE MANUFACTURING SYSTEMS................................................4

2.1 CHARACTERSISTCIS OF RECONFIGURATION......................................................5

2.2 PRINCIPLES OF RECONFIGURATION......................................................................6

2.3 CLASSIFICATION OF CONFIGURATION.................................................................6

2.4 COMPARING RMS WITH CELL CONFIGURATION................................................102.5 CALCULATING THE NUMBER OF RMS CONFIGURATION.................................11

3 DEFINING SYSTEM SCALABILITY.................................................................................12

4 FORMULATION OF SCALABILITY PLANNING..........................................................14

4.1 ASSUMPTIONS ................................................................................................................14

4.2 INPUTS ..............................................................................................................................15

4.3 DECISION VARIABLES .................................................................................................16

4.4 OPTIMIZATION MODEL ................................................................................................16

4.5 CONSTRAINTS .................................................................................................................17

5 OPTIMAL SOLUTION APPROACH USING GENETIC ALGORITHM....................14

5.1 STRING REPRESENTATION..............................................................................................18

6 CASE STUDY..........................................................................................................................19

7 ADVANTGES..........................................................................................................................23

8 CONCLUSIONS......................................................................................................................24

9 REFERENCES........................................................................................................................25



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ABSTRACT

This study explains the rationale for the development of reconfigurable manufacturing

systems, which posses the advantages both of dedicated lines he and of flexible systems. The

paper defines the core characteristics and design principles of reconfigurable manufacturing

systems and describes the structure recommended for practical RMS characteristics. It also

presents a scalability planning methodology for reconfigurable manufacturing systems that

can incrementally scale the system capacity by reconfiguring an existing system.

An optimization algorithm based on Genetic Algorithm is developed to determine the most

economical way to reconfigure an existing system. Adding or removing machines to matchthe new throughput requirements and concurrently rebalancing system for each configuration,

accomplishes the system reconfiguration. The proposed approach is validated through a case

study.



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1. INTODUCTION

Henry Ford’s invention of the moving assembly line in 1913 marked the beginning of the

mass production paradigm. Mass production was made possible only through the invention of

dedicates machining lines that produces the engines, transmissions and main components of

automobiles. Such dedicated manufacturing lines have a very high rate of production for the

single part type they produce, and they are very profitable when demand for this part is high.

These dedicated transfer lines were the most profitable systems for producing large quantities

of products until the mid-1990s.

Manufacturers today face more challenges than ever before due to high volatile market,

which creates large fluctuations in product demand. In order to remain competitive,

companies must design manufacturing systems that not only produce high-quality products at

low cost, but also respond to market changes in an economical way. A responsive

manufacturing system is one whose production capacity is adjustable to fluctuations in

product demand, and whose functionality is adaptable to new products. Responsiveness

enables manufacturing systems to quickly launch new products on existing systems, and to

react rapidly and cost-effectively to:

1. Market changes, including changes in product demand.

2. Product changes, including changes in current products and introduction of new products.

3. System failures (ongoing production despite equipment failures).

Traditional manufacturing systems - both dedicated lines and FMS are ill suited to meet therequirements dictated by the new competitive environment. Dedicated manufacturing lines

(DMLs) are based on inexpensive fixed automation that produces a company’s core products

or parts over a long period and at high volume. Each dedicated line is typically designed to

produce a single part at a high rate of production achieved by utilizing all tools

simultaneously. When product demand is high, the cost per part is particularly low. DMI.s are

cost effective as long as they can operate at full capacity, but with increasing pressure from

global competition and over- capacity worldwide, dedicated lines usually do not operate at

full capacity. Flexible manufacturing systems (FMS) can produce a variety of products with a



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changeable mix on the same system. Typically, FMS consist of general-purpose computer-

numerically-controlled (CNC) machines and other programmable forms of automation.

Because CNC machines are characterized by single-tool operation, FMS throughput is

much lower than that of a DML The combination of high equipment cost and low

throughput makes the cost per part using FMS relatively high.

1. RECONFIGURABLE MANUFACTURING SYSTEMS

A cost effective response to market changes requires a new manufacturing approach. Such an

approach not only must combine the high throughput of a DML with the flexibility of FMS,

but also be capable of responding to market changes by adapting the manufacturing system

and its elements quickly and efficiently. These capabilities are encompassed in

reconfigurable manufacturing systems (RMS), whose capacity and functionality can be

changed exactly when needed.

Three features capacity, functionality, and cost - are what differentiate the three types of

manufacturing systems - RMS, DML and FMS. While DML and FMS are usually fixed at the

capacity-functionality plane. RMS are not constrained by capacity or by functionality, and are

capable of changing over time in response to changing market circumstances. Reconfigurable

Manufacturing Systems (RMS) constitutes a new class of systems characterized by adjustable

structure and design focus. The flexibility of RMS, though really only "customized

flexibility", provides all the flexibility needed to process that entire part family.

Highly productive, cost effective systems are created by (i) part-family focus, and (ii)

customized flexibility that enables the simultaneous operation of different tools[12].RMSsystems are designed to cope with situations where both productivity and system

responsiveness are of vital importance. Each RMS system is designed to produce a particular

family of parts.

Therefore, Reconfigurable manufacturing system can be defined as follows: Reconfigurable Manufacturing Systems (RMS) are designed at the outset for rapid change in structure, aswell as in hardware and software components, in order to quickly adjust production capacityand functionality within a part family in response to sudden changes in market or regulatoryrequirements.



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2.1 CHARACTERISTIC S OF RECONFIGURATION

• Customization (flexibility limited to part family)

System or machine flexibility limited to a single product family, thereby obtaining

customized flexibility.

• Convertibility (design for functionality changes)

The ability to easily transform the functionality of existing systems and machines to suit new

production requirements

• Scalability (design for capacity changes)

The ability to easily modify production capacity by adding or subtracting manufacturing

resources (e.g. machines) and/or changing components of the system

• Modularity (components are modular)

The compartmentalization of operational functions into units that can be manipulated

between alternate production schemes for optimal arrangement

• Integrability (interfaces for rapid integration)

The ability to integrate modules rapidly and precisely by a set of mechanical, informational,

and control interfaces that facilitate integration and communication.

• Diagnosability (design for easy diagnostics)

The ability to automatically read the current state of a system to detect and diagnose the rootcauses of output product defects, and quickly correct operational defects.



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2.2 PRINCIPLES OF RECONFIGURATION

1. An RMS system provides adjustable production resources to respond to unpredictable

market changes and intrinsic system events:

• RMS capacity can be rapidly scalable in small increments.

• RMS functionality can be rapidly adapted to new products.

• RMS built-in adjustment capabilities facilitate rapid response to unexpected equipment

failures.

2. An RMS system is designed around a product family, with just enough customized

flexibility to produce all members of that family.

3. The RMS core characteristics should be embedded in the system as a whole, as well as in

its components (mechanical, communications and control).

2.3 CLASSIFICATION OF CONFIGURATIONS

Classifying configurations requires determining the number of possible configurations when

the daily demand, Q . {parts/day), and the total machining time for the part, t (min/part), are

given. In reality, machining times vary widely depending on the equipment involved, but, to

begin we assume these are given.

The minimum number of machines, N, needed in the system is calculated by the equation

N= Q* t / ( Min/day available x Machine reliability)...............................(1)

The following calculations assume 100% reliability of all pieces of equipment (i.e., machine

reliability = 1). The resulting number of machines calculated by Eq. (1) must be rounded to

the next larger integer.



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For example, if 500 parts per day are needed and the processing time for each part is 9.5 min,

at least five machines are needed in the system assuming working time of 1000 min/day.

First, configurations are classified either as symmetrical or as asymmetrical, based on

whether a symmetric axis can be drawn along the configuration. A configuration is then

evaluated by its machine arrangement and connections. For example, configurations a and b

have identical machine arrangements {one in stage 1, two in stage 2, and two in stage 3), but

they differ because of different connections among the machines-configuration b uses cross-

coupling between stages 2 and 3. The type of material handling system determines the

connections of a configuration.

Fig: 1 Configurations with five machines

Asymmetric Configurations add immense complexity and are not viable in real

manufacturing lines, as explained below. Asymmetric configurations may be sub-classified as

(a) variable-process configurations or (b) single-process configurations with non-identical

machines in at least one of the stages. Corresponding examples are shown in Fig.2 (a) and

(b), respectively.

Variable-process configurations are characterized by possible non-identical flow-paths for the

part. They therefore need several process plans and corresponding setups. For example, the

system depicted in Fig. 6 has a number of possible flow-paths: a-b-c-d-e, g-c-f, g-c-d-e, etc.

The process plan to be executed depends on the flow-path of the part being processed in the

system.





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Fig 3: Three classes of symmetric configurations

Fig 4: Symmetric configurations of Class 1 and Symmetric configurations of Class II

Fig 5: A practical reconfigurable manufacturing system.



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2.4 COMPARING RMS CONFIGURATIONS WITH CELL CONFIGURATIONS

We compare the two main practical configurations according to four criteria: investment cost,

line-balancing ability, scalability options, and productivity when machines fail. For a more

general analysis of the impact of configuration on system performance.

Capital investment. The configurations shown in Figs. 4 have identical machine

arrangements- three stages with two machines in each stage - but the connections are quite

different in that they use different part handling devices, each requiring a different capital

investment. The entire part handling system in Fig.4 is simpler and has a smaller number of

handling devices compared to the RMS system shown in Fig. 5.Thus, the capital investmentin the RMS configuration is higher.

Line balancing. A major drawback of cell configuration is that it imposes severe limitations

when balancing the system. For example, if just one product is produced, the processing time

in all stages of the cell configuration must be exactly equal to be perfectly balanced. By

contrast, to achieve a balanced RMS configuration only the following relationship needs to be

satisfied

Ts1/ Ns1 = Ts2/Ns2 = Tsi/Nsi ............................. (2)

where Nsi is the number of machines in stage i, and Tsi is the processing time per

machine in stage i. Therefore, in RMS configurations the number of machines per stage is not

necessarily equal in all stages. The number of machines in the various stages of RMS

configurations may be adjusted to provide accurate line balancing, which consequently yields

improved productivity.

System scalability. RMS configurations are far more scalable than cell configurations.

Adding one machine to one of the stages and rebalancing the system enables adding a small

increment of capacity. In the cell configuration, a complete additional parallel line must be

added to increase the overall system capacity. In markets with unstable demand, scalability

represents an important advantage of RMS configurations.



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Productivity. If machine reliability is low due to crossovers at each stage, an RMS

configuration offers higher productivity than that of a cell configuration. As shown in Fig. 4,

if machines in two different lines and at two different stages (marked with x) are down, the

entire system is down (i.e., throughput = zero). For RMS, in contrast, under the same

conditions - two machines not working (marked with x in Fig. 4) - throughput is still at50%.

So, RMS are more productive systems from the perspective of machine downtime.

Nevertheless, the RMS material handling system is more complex, with its so-called "cell

gantries" that enable crossovers (see Fig. 5).If one of the cell gantries is down, the entire

RMS system will not work. In contrast, cellular systems with parallel lines do not contain cell

gantries and are therefore more reliable from the material handling system perspective.

2.4 CALCULATING THE NUMBER OF RMS CONFIGURATIONS

Closed equations for calculating the number of configurations with N machines exist only for

RMS-type configurations . The basic equations for calculating the number of possible RMS

configurations are given below. K, the number of possible RMS configurations with N

machines arranged in up to m stages is calculated by:

X = ∑ N m=1 ( N-1

M-1 )=2 N-1 ......................................(3)

K, the number of possible configurations with N machines arranged in exactly m stages iscalculated by:

X= (N-1)! / (N-m)!*(m-1)! ..........................................(4)

For example, for N = 7 machines arranged in up to 7 stages, Eq.(3) yields K = 64

configurations, and if arranged in exactly 3 stages, Eq.(4) yields K =15 RMS configurations.

The mathematical results of these two equations for any N and m may be arranged in a

triangular format, known as a Pascal triangle, shown in Fig.6 .The numerical value of each

cell in the Pascal triangle is calculated as follows. The numerical value corresponding to N

machines arranged in m stages is calculated by:

The value for N machines in m stages = (the value for N - 1 machines in m - 1 stages)+ (thevalue for N - 1 machines in m stages).



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For example, in Fig.14, the cell of N = 5 and m = 3 shows6, which is the sum of 3 +3of the

previous line of N -1 = 4 machines with 2 and 3 stages. The triangle also allows the designer

to immediately visualize the number of possible RMS configurations for N machines

arranged in m stages. For example, there are 15 RMS configurations when 7 machines are

allowed to be arranged in exactly 3 stages. In addition, the Pascal triangle allows the

designer to immediately calculate the number of possible RMS configurations for N machines

arranged between i stages and j stages (i,j < N).

Fig 6: The Pascal triangle to calculate the number of RMS configuration.

3 DEFINING SYSTEM SCALABILITY

To adapt the throughput of manufacturing systems to the fluctuations in product demand, the

system capacities must be adjusted quickly and cost-effectively. Dedicated lines do not have

scalable capacity and cannot cope with large fluctuations in product demand. This challengecan only be met by flexible or reconfigurable manufacturing systems, which are composed of

CNC machines or reconfigurable machines,as these systems are scalable in small

increments accomplished by adding or removing individual machines as a need arises. Note

that the number of stages always remains unchanged during the scalability process. We

define system scalability, in percentage, as:

System scalability = 100 - smallest incremental capacity in percentage



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If the minimal capacity increment by which the system output can be adjusted to meet

new market demand is small, then the system is highly scalable. For example, if a serial line

(Fig. 7a) needs to increase its production capacity to satisfy a larger market demand, an

entire new line must be added. The step-size of this addition doubles the production capacity

of the system. Mathematically, the minimum increment of adding production capacity in a

serial line is 100% of the system, i.e., adding a whole new line, making the scalability of a

serial line 0%.. Doubling the line capacity will be expensive because there is no guarantee

that the extra capacity will ever be fully utilized, risking thereby a substantial financial loss.

Thus, zero scalability means that in order to increase the system capacity, the entire

production line must be duplicated. When markets are volatile, designing a manufacturing

system with zero scalability is not a good engineering solution.

Similar scalability calculations for the other systems in fig. 7 show: Configuration b has a

scalability of 50% and Configuration c has67%.Configurations d and e have a scalability

of84%; the highest possible for 6-machine configurations. A minimum increment of only

one sixth of the system{16%)- in these cases, one machine- can be added to increase system

capacity; for example, a machine can be added to Stage 2 of Configuration d as shown in

Fig. 7d.

In this example, the configuration depicted in Fig. 7c of a two stage system with three

machines per stage, might be a compromise between reasonable scalability and investment

cost. In this case, if a product requires machining on both the upper and side surfaces, the

three machines in the first stage might be 3-axis vertical milling machines, and the three

machines in the second stage might be 3-axis horizontal milling machines. Conversely, in a

parallel system, all six machines in Fig. 7e must be 5-axis milling machines making the

system much more expensive. In the system in fig. 7c, capacity scalability must be performed

in steps of 33.3% by adding one vertical machine and one horizontal machine, rather than insteps of 16.6% as with the parallel configuration. Adding a step of 16.6% in fig.7e in practice

means adding one S-axis machine with a large tool magazine that contains every tool needed

for the whole part processing - an expensive addition.



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Fig:7 Five scalable configurations

4 FORMULATION OF SCALABILITY PLANNING PROBLEM

We propose below a practical method to determine the most cost-effective system

reconfiguration to meet a new market demand. To perform system scalability planning,

many factors need to be taken into consideration. These include a detailed process plan, setup

plan, machine capability, and the number of spots reserved in the original system at each

stage for adding machines, if needed. When reconfiguring an existing manufacturing system,

Simultaneous reconfiguration planning and system rebalancing attempts are required tomaximize the capacity of systems, and therefore, minimize the number of machines needed to

be added. In this section an optimization model for scalability planning is introduced.

4.1 ASSUMPTIONS

The following assumptions are made based on the current manufacturing practice in the

power train industry.

- A multi-stage system with a configuration similar to that shown in Fig.1(or Fig.3) is

considered. Parts are moved from one stage to another through conveyors and delivered to

different machines within a stage using gantries.



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- The number of stages must remain unchanged during any reconfiguration process. This

is to keep the system setup plan unchanged in order to avoid major adjustments of process

plans, thereby minimizing the impact of system reconfiguration on product quality.

- All the machines within the same stage perform exactly the same sequence of tasks.

- There are reserved spaces for adding new machines in the stages and material handlers can

be extended to deliver parts to the newly added machines.

4.2 INPUTS

Scalability planning requires the following five types of inputs:

• Configuration information

Number of stages- L; number of machines in each stage –Ni , Where i =1,2, ..., L; maximum

number of machines allowed in each stage Mi, i =1,2, ..., L

• Stage characteristics

Each manufacturing stage has capabilities that are defined by a set of characteristics of the

stage. These include machine tool characteristics such as functionality, power, accuracy and

machining ranges, as well as fixture features such as face accessibility, which defines thefaces that are accessible by the cutting tool. When a set of tasks is assigned to a stage, the

necessary capabilities must fall into the set of characteristics of the stage. Assuming that the

number of characteristics of each stage is K, a capability matrix S stores all possible

characteristics of each stage.

S[i, j) = d: d is jth key characteristic of stage i, where 1<=i<=L

• Manufacturing tasks

- Task precedence: manufacturing tasks must be performed at a certain order. Each task can

only be performed after all its parent tasks have been completed. A two-dimension binary

matrix

Pre[l·· ·N, 1..·N), where N is the number of tasks to be processed, is used to represent the precedence tree.

Pre[i,j] = { 1, if task i must be performed before task j

0, otherwise



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- Task characteristics : these include task type, access direction, dimension, accuracy and

power needed to perform the task. For a task to be assigned to a stage, its characteristics must

be included in the set of characteristics of that stage. Assuming the number of characteristic

of each task is R, a task characteristic matrix K is used to store the characteristic of each task.

K(i,j)=f:f is the j th key characteristic of task i. Where 1<=i<= N and 1 <= j <=R.

• Machine reliability information

Machine reliability can be expressed by two parameters: MFBF (Mean Time Between

Failure), and MTTR(Mean Time to Repair).

• Demand

The system must be reconfigured so its new capacity will fulfil the new demand D new.

4.3 DECISION VARIABLES

Two decision variables need to be determined:(1) machine allocation array, M[i), which

determines how many machines are to be added to the system and where to add them, and (2)

task allocation array, T[i], which describes how the tasks should be reallocated when the new

machines are added to or removed from the system.

M[i] = number of machine being added to stage i, 1<=i<= L;

M[i] >0 for adding machines, and M[i] < 0 for removing machines from systems.

T[i] = s ,s is an index of stage to which the task i is assigned, 1<=s<= L

4.4 OPTIMIZATION MODEL

The objective of scalability planning is to minimize the number of machines needed to meet anew market demand. This can be expressed by Eq.(1):

Minimize (∑ i=1

L

(Ni +M [i])) ............................... (5)



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4.5 CONSTRAINTS

1. Precedence constraints:

Pre[i,j]· T[i]<= T[j), ¥ i= 1, 2, ..., N, ¥ j = 1, 2, ..., N ............... (6)

For two tasks i and j, if task i must be performed before j, it will be assigned to the same or a

preceding stage as j.

2. Characteristic constraints:

..........(7)

Eq.(7) means that if a task i is assigned to Stages, all its characteristics must be contained in

the set of characteristics of Stage s.

1. Space constraints :

The number of machines added to, or removed from each stage must not exceed the

maximum limit.

M[i]<= M max[i], ¥ i= 1, 2, ..., L ............................... (4)

4 . Throughput constraints:

After the new machines are added, the system throughput must be equal to or larger than the

new market demand.



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5 .AN OPTIMAL SOLUTION APPROACH USING GENETIC ALGORITHM

A Genetic Algorithm (GA) is a structured heuristic that searches for good solutions using a

mechanism that mimics "survival of the fittest." Mixed integer problems with complex

functions and combinatorial explosion on feasible space, like the scalability planning

problem, can be solved efficiently by GA.

In a GA, populations of solutions are randomly initiated first. These are represented in a

string format and are evaluated to provide some measure of their fitness (performance).This

initial population is then propagated into future generations by applying genetic operators to

its members. After a number of generations, the algorithm converges to the near-optimal

solution to the problem.

5.1 STRING PRESENTATION

For a given task precedence tree, for example in Fig.8, the first step of applying a GA is to

represent one possible solution to the problem by the format of a string, namely encoding (forexample in Fig.4b). A scalability scheme is encoded by an integer string which consists of

two portions, corresponding to task allocation scheme and system configuration, respectively.

Fig.8 depicts examples of string representation of manufacturing tasks designed for task

allocation. The length of each string is equal to the number of manufacturing tasks that need

to be assigned to the manufacturing system. Each digit of the string contains two kinds of

information: the position, gene, and the value, allele. The position indicates the sequential

number of a task, and the value indicates the stage index which decides what stage the task

will be assigned to. In this example, nine tasks are needed to complete a part in a system with

four stages. They must be performed in the sequence depicted in Fig. 8a.

An integer string with nine digits is used to present the nine tasks. For example, the first digit

represents task 1 and the second digit represents task 2, and soon.

To ensure all the constraints are satisfied when assigning tasks to stages, a task-to-stage index

table, or T2S as shown in Fig. 8c, is first built by comparing the key task characteristic to thekey stage characteristic.



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The stage number can then be found in the T2S table according to the task number (gene)

and the stage index number (allele). For example, the value of Task 6 in the string is 2,

signifying it will be allocated to stage T2S [2,6] which is OP 3 in the manufacturing system.

Table T2S also determines the value range of each allele Ai which is the number of available

stages that this task i can be assigned to. In the example of Fig.4, the range of gene 1 is At

=1: the range of gene 2 isA2 =3: the range of gene 9 is A 9 =2 and so on.

Fig 8: String representation of task allocation

During this process, it is made sure that the precedence constraints are not violated.

6 .CASE STUDY

The case selected is the rough machining process of an automotive V6 cylinder head

provided by an industrial partner of the NSF Engineering Research Centre for the

Reconfigurable Manufacturing Systems. There are 141 features on the part, which can be

grouped into 43 machining tasks, including milling, drilling, boring, spot facing, and tapping.

The total time needed for the rough machining is 1019 s. Because of its complexity, this part

is ideal for the study as it permits many process design solutions for different system

configurations. The machines used for all stages are four-axis CNC machining centres which

are capable of completing all the machining tasks. Fig. 6a shows the part and Fig. 9b shows

the machine.



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6.1 BASELINE CONFIGURATIONS

There are four feasible setups that depend on the part process plan, as depicted in Fig. 10.To

achieve the best system throughput, each system configuration needs its corresponding setup

plan. Three system configurations, 3 x 4, 4 x 3 and 6 x 2, are used as baselines to study the

scalability planning.Fig.11 gives three system configurations and their line balancing results.

The letter under the OP number represents the setup scheme defined in Fig.10.

6.2 SCALABILITY PLANNING RESULTS

Assume a 4 x 3 configuration (Fig. 14b) is currently being used to fulfil a production demand

of 30JPH jobs per hour). Also assume that a maximum of two machines can be added to

each stage while the setup plan remains unchanged. When the new production demand

changes to 35JPH, the proposed scalability planning algorithm found that 2 new machines

need to be added to the system, as is shown in Fig. 15a. The rebalancing results per machine

and per stage are shown in Fig. 15b and c, respectively. After adding two machines system

capacity increased to 36.6JPH. Compared to duplicating a four machine serial line, the new

configuration only needs two new machines to fulfil the new production demand.

Fig. 15a also shows that instead of being added to two different stages, the two machines are

added to the same stage. This is because the machining tasks are not evenly distributed on

each accessible face in the selected setup plan. Machines tend to be added to the stages with

a setup which allow access to more tasks. This way system throughput can be maximized and

the number of required machines can be minimized.

For each configuration in Fig11, reconfigurations for adding up to 5 machines to existing

systems are calculated. Figs. 13-15 show the reconfigurations for three-stage system, four-

stage system and six- stage system respectively. Again, Figs.13-15 show that for a given case,

machines are not evenly added to each stage.

Some stages tend to require more machines than others to maintain the work load balance of

the system. The number of machines and their locations to be added to the system can be

optimized by the proposed method.



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From the cost-effective point of view, we suggest scalability planning be performed

concurrently with the design of a new manufacturing system. This way, optimal locations

where future machines should be installed can be identified in advance. Thus, material

handling systems can be optimized for future scalability planning to reduce the life-time

investment cost.

Fig 13,14: Reconfigurations for scalability planning for 3*4 and 4*3 systems

Fig 15: Reconfigurations for scalability planning for 6*2 systems



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7 . ADVANTAGES

It can be modular

Easy to integrability;

Customization;

Convertibility obtained within reasonable cost to manufacturers;

Rapid scalability to the desired volume

Diagnosability.



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8. CONCLUSION

The study elaborates in details on the scalability concept and presents a systematic

approach for scalability planning- adding or subtracting the exact capacity needed to

fulfil market demand. The approach utilizes a scalability planning process that

simultaneously changes the system configuration and rebalances the configures system.

Till now, the reconfigurable manufacturing systems are not yet a

matured area; this is why the industry feedback is not satisfactory.

Further research is necessary at the machine level, where the technicaland economic consequences are very important, at entire manufacturing

level.

The reconfiguration science will form the basis for a vital production

technology in this era of global market competitiveness – that it will

involves into entirely new manufacturing field with enduring benefits for

the economy and society.



9. REFERENCES

1. Scalability planning for reconfigurable manufacturing systems by, Wencai Wang andYoram Koren—Journal on Manufacturing Engineering

2. Design of reconfigurable manufacturing systems by, Yoram Koren and Moshe Shpitalni —Journal on Manufacturing Engineering

3. A Glance at Reconfigurable Manufacturing Systems (RMS): A Possible Connotation on aPath To High Performance by, César H. Ortega Jiménez and Ignacio Eguía Salinas

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