Intelligent Control and Automation, 2011, 2, 405-412
doi:10.4236/ica.2011.24046 Published Online November 2011 (
Copyright © 2011 SciRes. ICA
Intelligent Tool Management Strategies for Automated
Manufacturing Systems
D. Ganeshwar Rao*, C. Patvardhan, Ranjit Singh
Faculty of Engineering, Dayalb agh Educational Institute, Dayalbagh, Agra, India
E-mail: *
Received July 9, 2011; revised July 30, 2011; accepted August 7, 2011
With the increase in automation and use of computer control in machine tools, the number of cutting tools
per machining setup is on the increase. On one hand, such multi-tool setups offer the advantages of reduced
down-time and cost of production and require less space and in-process inventory, and on the other hand,
require proper tool management for economic operations. A number of strategies have been devised to solve
the tool selection problems and a number of tool replacement policies have been proposed in the past. These
strategies have been solved in isolation, whereas, a comprehensive algorithm for proper selection of tools out
of those available in the tool magazine for performing operation and for replacement of tools on failure/wear
is necessary. In this paper, taking cue from the computer memory management policies, four tool selection
strategies have been presented and their performance in tandem with various tool replacement policies has
been studied. The effect of important parameters such as reduction of tool life due to regrinding, limited
number of regrindings, catastrophic failures etc. have been considered. Cost has been computed for each
combination of tool selection and replacement policy. Also, the number of machine stoppages has been
worked out in each case. The results indicate that the combination of various selection strategies with suit-
able replacement policies affects the overall cost.
Keywords: Tool Management, Tool Selection, Tool Replacement, Multi-Tool Setup
1. Introduction
The present manufacturing industry requires producing a
large variety of complex components in small batch sizes
and in a cost effective manner. At the same time the num-
ber of tools to be magazined has increased, particularly in
automated machining centres. It is not unusual for there to
be over 300 different tools in a direct storage unit at a
machining centre. Such multi-tool setups offer the advan-
tages of reduced time and cost of production and require
less space and in-process inventory. With the above de-
velopments, the tool management task has become sub-
stantially more demanding because the increase in number
of tools per setup requires a better tool reliability man-
agement, as wearing/failure of any one tool renders the
whole system non-operational. As the number of tools in
the machining setup increases, the frequency of replace-
ment due to wearing out of tool and due to failure of tool
also increases. A tool management system adopted should
be such as to make the right tool available at the right time
in proper sequence for processing a job while keeping
costs down to a reasonable limit. Hence, it becomes nec-
essary to devise strategies which would select the right
tool out of those available in the tool magazine of the ma-
chining centre [1-3].
In a traditional machining environment, a skilled op-
erator, in liaison with the tool store personnel monitors,
maintains, and replenishes the small number of tools as-
sociated with each machine tool. The expertise of the ma-
chine operator is the determinant factor in ensuring that
correct tools are used for each operation and the tools are
replaced before they completely wear out and damage the
work piece. This working pattern is no longer acceptable
in a modern machine shop equipped with the CNC ma-
-chines, as a greater variety, and a large number of tools
are used to machine components on these machines and
the task of determining the correct tool out of the alternate
tools available, and task of replacing the tool on failure or
before failure, is too complicated to be left to the machine
operator. Also, a large number of machine stoppages may
be caused due to incorrect tool selection and replacement.
The tasks of tool selection for performing the operations,
and tool replacement decisions are complex and require
development of sophisticated programs which should be
supported by databases containing information on the
manufacturing resources and company-specific machin-
ing practices [4].
2. Literature Review
Tool management problems have been attempted by re-
searchers since the turn of the century. The detailed sur-
vey of literature is as follows.
2.1. Studies on Tool Replacement
A thorough review of the solution methodologies related
to tool replacement can be found in McCullough [5],
Armarego and Brown [6], Pa’sko [7], Batra and Barash
[8], Bao [9], LaCommare et al. [10], Sharit and Elhence
[11], Zhou et al. [12] and Tak [13]. As has been men-
tioned by Zhou et al. [12], the various authors have clas-
sified tool replacement strategies in different ways. Most
probabilistic optimization models are based on tool life
distribution [10,14]. A major development in the process
of computer integration of automated manufacturing
systems has been the implementation of automated tool
replacement [15,16]. A complete Tool Replacement Stra-
tegy specifies a tool change schedule based upon the
economic service lives of tools and a control policy re-
garding unscheduled tool changes following breakage.
The most realistic replacement strategies have consid-
ered the distributed nature of tool lives under actual ma-
chining parameters, as well as the option to change sev-
eral tools once one fails [9,10], rather than considering
only expected lives and single tool replacement [5,6]. All
of these tool replacement studies have considered one
machine in isolation. Sharit and Elhence [11] have gone
beyond the single machine model to examine tool re-
placement strategy at the system level. Rather than pro-
posing an automated, optimizing strategy, their study
emphasizes the limitations of both human and computer
at making the tradeoff between economic tool replace-
ment costs and system throughput in a real-time, dy-
namic environment. Zhou et al. [12] have proposed an
optimization model for tool replacement based on tool
wear status. The model is capable of utilizing tool wear
status in determining an optimal replacement policy.
They have mentioned three types of tool replacement
strategies employed in the industry: 1) scheduled tool
replacement, 2) preventive planned tool replacement, and
3) failure tool replacement. Under the first strategy, the
cutting tool is replaced either at pre-scheduled time or
upon failure whichever is earlier. The second is also
similar, except that it is based on the number of finished
workpieces. The third strategy is to use the tool until
failure. Research has been done to find the optimum
pre-established time or lot size for replacement and to
compare the different strategies [10].
Two broad categories of the tool replacement policies,
viz. unscheduled tool replacement schemes and sched-
uled tool replacement schemes have been mentioned in
the literature. Under unscheduled tool replacement sche-
me, Series Tool Replacement and Parallel Tool Replace-
ment have been suggested [8]. Under scheduled tool re-
placement schemes, Individual Tool Replacement [7],
Group Tool Replacement [8], Skip Schedule Group Tool
Replacement and Sub Group Tool Replacement [13]
have been suggested. Tak [13] has suggested the Dy-
namic Tool Replacement policy, under which every tool
failure, which essentially results in machine interruption,
is utilized as an opportunity for evaluating every tool of
the setup for its effective utilization and reliability. Un-
der this strategy, whenever a tool fails, all the tools in the
magazine are evaluated for the useful lives that they have
lived and the tools exceeding their warning limits are
also replaced along with the tool which has failed. Hedin
et al. [17] have investigated static and dynamic tooling
policies and presented a comparison between the two in
context of a general flexible manufacturing system. Cur-
rently, many tool replacement models are deficient in
that they ignore the relationship between the processing
rates and the tool replacement policy.
2.2. Studies on Tool Selection
Major research efforts in the area of Tool Selection
started in the early 80’s. A review of solution method-
ologies for optimum tool selection can be found in Ced-
erqvist [18], Giusti et al. [19], Chen et al. [20], Syan [21],
Domazet [22], Maropoulos [23], Hinduja and Barrow
[24], Eversheim et al. [2], Zhang and Hinduja [4], Hedin
et al. [17]. The task of selecting cutting tools which are
not only functionally correct but also optimum, is a com-
plex one. Cederqvist [18] has suggested that when a new
batch is planned, the number of cutting edges required
for each tool in the setup can be calculated and a com-
plete set of block tool heads can then be prepared and
sent out to the machine. Some researchers viz. Giusti et
al. [19], Syan [21] have developed expert systems
wherein the technological knowledge is represented as
production rules which are consulted when selecting a
tool for a given operation. Chen et al. [20] have adopted
a heuristic-deterministic approach to reduce the comput-
ing time to determine the optimum tooling for rough
turning operations. Domazet [22] has proposed a hybrid
approach to automatically select turning tools; the selec-
tion is done in stages and for those stages which require
Copyright © 2011 SciRes. ICA
D. G. RAO ET AL.407
special knowledge and expertise, a non-algorithmic me-
thod is followed. Maropoulos [23] has used an algorithm
approach to automatically determine tools for rough and
finish turning operations. Hinduja and Barrow [24] have
suggested an automatic interactive system. In the interac-
tive part, the user is guided by the system towards the
parameters of the optimum tool. Eversheim et al. [2]
have reiterated the importance of tool selection in a
modern manufacturing environment. They have proposed
an integrated method for tool selection on the basis of
manufacturing features. Zhang and Hinduja [4] have pro-
posed automatic generation of a tool set for a given batch
of components, the optimization criterion being either
the minimum machining cost or minimum number of
machine stoppages or a combination of both. Hong-Bae
Jun et al. [25] have considered a tool provisioning prob-
lem in a flexible manufacturing system (FMS) with an
automatic tool transporter. Their study determines the
number of copies of each tool type for a limited budget
with the objective of minimizing makespan. Two heuris-
tic algorithms have been proposed. One is a composite
search algorithm based on two greedy search methods,
and the other is a search algorithm in which numbers of
tool copies are determined based on tool groupings. In
both algorithms, simulation results are used to find
search directions. Mözbayrak et al. [26] have addressed
the design of an integrated tool management system for
flexible machining facilities (FMFs). Modules with func-
tions ranging from issuing tools according to a tooling
strategy to diagnosing system operation have been de-
veloped and integrated around a centralized manufactur-
ing database to guarantee streamlined manufacture.
Selim Akturk M. et al. [27] have shown that there is a
critical interface between the lot sizing and tool man-
agement decisions, and these two problems cannot be
viewed in isolation. They have proposed the alternative
algorithms to solve lot sizing, tool allocation and ma-
chining conditions optimization problems simultane-
ously. Svinjarević G. et al. [28] have studied the con-
trolled testing and analysis in every phase of tool man-
agement in departments and other services which are
directly involved in the tool management system to
reduce stock and costs. They have identified some dis-
advantages and given a few suggestions for the im-
provement in the tool management system. Haslina Ar-
shad et al. [29] have introduced a virtual cutting tool
management system to reduce or eventually solve many
of the tool management problems. It has the capability
of choosing the right cutting tool from a virtual cutting
tool catalogue. Their system provides the virtual selec-
tion process for cutting tool and a virtual milling proc-
3. Problem Definition
The above survey of literature reveals that several re-
searchers have attempted different tool selection schemes
and tool replacement strategies, but most of the above
models, except for a few, developed for optimizing tool
replacement, do not consider the actual status of the cut-
ting tool. From the above review, it is clear that research
efforts have been mainly directed towards the selection
of optimum tooling for a single machining operation.
Also, the possibility of adjusting the wear rates of indi-
vidual tools and synchronizing tool changes due to
wear/failure in order to reduce the number of machine
stoppages has been suggested. Also, investigations have
been carried out in the past using simulation strategies to
find out the best strategy under different operating situa-
tions. However, it is necessary to investigate how each
selection policy performs in tandem with different re-
placement policies. The reason is that some tool selection
strategy might be better in combination with a particular
replacement strategy but may prove to be worse with
other replacement strategies.
The focus of this paper is to evaluate the performance
of identified tool selection policies in tandem with the
different tool replacement strategies. In developing a
realistic simulation model for evaluating various tool
selection and replacement policies, it is very important to
consider various factors such as remaining tool life of
partially used tools, catastrophic failure of the tools,
number of times the tool has been reground, reduction in
tool life due to regrinding, applicability of tools for vari-
ous operations, operations-profile etc. In the present
work, a simulation model has been developed to investi-
gate the most appropriate tool selection policy with each
replacement strategy considering the above mentioned
4. Adaptations for Tool Selection and
The strategies considered for tool selection and replace-
ment for developing the simulation model have been
explained in the following sections.
4.1. Tool Selection Strategies
The goal of any tool management system is to make the
right tool available at the right time in the right sequence
for processing a job. To achieve this goal, the selection
of the right tool is very important as there may be a
number of similar tools or different tool types in the
magazine with different remaining tool lives, capable of
performing the operation. If proper selection of the tool
Copyright © 2011 SciRes. ICA
is not done then it is possible that a certain tool of one
type may not be used at all or may be lying unused for a
long time in the tool magazine, even if it has a good
amount of remaining tool life. The tool selection logic is
very simple to describe. The system must maintain a
record of all the tools with different tool lives. Some
tools may be having a small value of tool life whereas
some tools may have a large value of tool life. When a
tool request is made, the tool magazine should be
searched for the type of tool required and the tool life
required. Since the magazine has tools with different tool
lives, a procedure for selecting right tool is needed for
the optimum utilization of the tools.
In computer systems, memory management is per-
formed in which a system maintains a list of all the
blocks of memory. Some of these blocks are free at any
time and some currently allocated to a user. When an
allocation request is made, the system must locate a free
block of memory of sufficient size and allocate all or part
of it. In case only a portion of a free block is to be allo-
cated, the allocation is made from the bottom of the
block. When a re-allocation request is made, the system
must recover the re-allocated block of memory. In addi-
tion, the system should be able to find adjacent free
blocks of memory and combine them into a single large
block, to maximize the probability of being able to sat-
isfy a large allocation request. For this reason, a number
of memory management systems have been devised for
different applications. Some of the most common are
known by the name First Fit, Next Fit, Best Fit and
Worst Fit. These policies have been adapted here for
selecting the tools from the tool magazine for performing
various operations on a machining centre. The adaptation
of the policies in context with the tools selection is ex-
plained in the following paragraphs.
1) First Fit
It is a very simple scheme. The available tools are
placed in the magazine in a random manner irrespective
of their remaining lives. When a tool request is received,
the magazine is searched for the first tool with remaining
life, large enough to satisfy the request, and the tool is
used for processing the requesting job.
2) Next Fit
In the First Fit scheme, the search for the tool having
sufficient remaining life to serve the request always be-
gins from the starting position of the magazine. Conse-
quently, it requires longer time for search and also, all
the tools with smaller tool life tend to collect at the be-
ginning. Hence, it is necessary to examine several tools
before allocating a tool for processing. A modification to
the first fit strategy is to start a search for a suitable tool
at the position where the previous search ended. This
approach causes the decrease in search time and also,
tends to distribute tools with lesser tool life uniformly
over the entire magazine rather than concentrating them
near the front. This approach is named Next Fit scheme.
3) Best Fit
The Best Fit approach is to search the entire magazine
for the tool with the smallest tool life which satisfies the
request. This approach tends to save the tools having
larger tool lives until they are needed to satisfy a larger
4) Worst Fit
In this approach, the entire magazine is searched for
the tool with largest tool life that satisfies the request.
The idea is that the tool life remaining after processing
the current request is large enough to process another
request. However, this approach tends to generate large
number of tools with very small tool lives that are in-
adequate to satisfy most subsequent requests.
4.2. Tool Replacement Strategies
A number of tool replacement schemes have been sug-
gested in the past. Tool replacement has its own impor-
tance in the field of manufacturing. If a tool is replaced
too early, the remaining tool capacity is lost and too fre-
quent changes take place. On the other hand, if a tool is
replaced too late, the probability of tool failure goes high.
Since the proposed tool selection schemes are based on
the remaining tool life of the tool, the same factor will be
considered for tool replacement also. Out of the available
schemes in the literature, the following have been
adapted in the present work:
1) Single Tool Replacement
In this replacement scheme, tools are continuously
monitored and as and when a tool exceeds its warning
limit or fails due to some other reason, it is replaced.
This results in full utilization of tool life but results in
higher down-time cost.
2) Multi Tool Replacement
Under this strategy, whenever a tool in the setup ex-
ceeds its warning limit or becomes unusable due to fail-
ure, all the tools of the setup are replaced. Such a Strat-
egy may result in lower down-time cost but the tools are
not utilized to their capacity and hence tooling cost goes
much higher.
3) Dynamic Tool Replacement
Under this scheme, whenever a tool in the setup ex-
ceeds its warning limit or becomes unusable due to fail-
ure, every other tool of the setup is evaluated for useful
life which it has lived. If this life exceeds the critical life
of the tool, this tool is also replaced along with the tool
which has failed.
5. Methodology
A simulation model has been developed to investigate
Copyright © 2011 SciRes. ICA
D. G. RAO ET AL.409
the most appropriate tool replacement policy with several
tool selection schemes. All the relevant information
about the different tools such as, tool material, different
operations that can be performed by the tool, maximum
tool life, cost of the tool, cost of regrinding, warning
limit of tool etc. has been stored in a tool database. Also,
the cutting parameters like depth of cut, cutting speed,
feed, etc. for different combinations of tool and work-
piece material have been stored. The relevant data has
been collected from standard hand-books. Several data
sets have been created and in each data set operations to
be performed, tool material, workpiece material and the
type of surface finish required are stored. In each simula-
tion run, the user has to specify the data set and tool se-
lection and replacement policy to be tried. For each op-
eration, the software calculates the tool life required us-
ing standard Taylor’s tool life equations taking feed,
speed, and depth of cut into consideration. The tools are
selected for the operation using the policy specified and
the relevant data stored. For all the cases the number of
regrindings done, total cost involved, and number of
times the machine was down, are computed.
1) Cost Function
Total Cost = Machining Cost + Down Time Cost
+ Tool Cost + Setter Cost
Total Cost = (MC + CI) + DT + TC
MC = Machining Cost
CI = Labour Rate + Supervision Charges
+ Interest on investment
DT = Down Time
TC = Tool Cost
MTRA = Mean Time for tool replacement and ad-
MTSRA = Mean Time for setter arrival
SR = Setter Rate per Hour
The following factors have been considered in the
simulation model:
2) Catastrophic Failure
A considerable percentage of tool inventory is com-
monly lost due to sudden and unexpected failure or tool
breakage. Therefore this feature has been incorporated in
the simulation model, with the assumption that about 5%
of the new tools and 15% of the reground tools fail due
to catastrophic failures.
3) Tool Regrindings
To prevent tool breakage use of excessively reground
tools is avoided. In the simulation model, the number of
regrindings of tools has been limited to a certain number,
depending on the type of tool. Further, it is considered
that after every regrinding, the tool life reduces by a cer-
tain percentage. For different tools, this percentage varies.
Also, the number of regrindings permissible for each tool
is different.
6. Computational Experience
A realistic simulation model developed in the present
work, evaluates the performance of identified tool selec-
tion policies in combination with the different tool re-
placement strategies. A large number of data sets consist-
ing of the details of different types of tools have been
generated randomly. The realistic values of factors such
as remaining tool life of partially used tools, catastrophic
failure of the tools, number of times the tool has been
reground, reduction in tool life due to regrinding, appli-
cability of tools for various operations, and operations-
profile have also been generated and incorporated in the
In the model, the four tool selection strategies viz.
First Fit, Next Fit, Best Fit and Worst Fit against each
tool replacement policy viz. Single Tool Replacement,
Multi Tool Replacement and Dynamic Tool Replace-
ment have been considered.
For each data set, the extensive simulations have been
carried out and the total cost involved and the number of
times the machine was down due to change of tool have
been computed. The results have been tabulated in Ta-
bles 1 and 2. The results indicate the combination of
most appropriate 'Replacement Policy’ with the appro-
priate “selection policy” for obtaining minimum cost for
each data set. Also, the number of stoppages of machine
is obtained for each combination of tool replacement and
selection strategy. Figures 1-3 show a comparison of
results in the form of bar charts clearly indicating the
trend for some of the data sets used in the simulation.
7. Conclusions
The number of stoppages of machine due to wearing or
failure of tool plays an important role in increasing the
cost of production. In the present work, the tool selection
and replacement strategies have been identified. The
simulation model developed selects the best combination
of these strategies for minimizing stoppages and the cost
of production. It takes into consideration the effect of
important parameters such as reduction of tool life due to
regrinding, limited number of regrindings, a catastrophic
failure etc.
From the results obtained, it can be concluded that the
combination of various selections policies and replace-
ment policies affects the overall cost. The investigations
reveal that the total cost involved is lowest in the case of
Dynamic Replacement Policy in tandem with the worst
fit approach. However, considering the limitations of the
Copyright © 2011 SciRes. ICA
Copyright © 2011 SciRes. ICA
Table 1. Total cost with different strategies for each dataset.
Replacement PolicySelection
First Fit 12,12651667790 7534 4162 3255 7894
Next Fit 11,65147918132 6829 4172 3631 8581
Best Fit 13,246881610,70210,834 7472 5101 10,406 Series Tool
Replacement PolicyWorst Fit 11,50144667432 6829 3451 2546 7111
First Fit 13,03148619757 8269 3887 3406 8901
Next Fit 12,691503610,7429724 4062 3756 9776
Best Fit 13,91192169727 11,084 9632 5231 12,236
Parallel Tool
Replacement PolicyWorst Fit 12,26145318767 8369 3457 2546 8296
First Fit 11,80148317782 7534 3837 3240 7880
Next Fit 11,55147917762 6864 3882 3306 8166
Best Fit 12,64678669667 10,254 7987 4526 10,131
Dynamic Tool
Replacement PolicyWorst Fit 11,50144667432 6829 3451 2546 7111
Table 2. Number of times the machine was down.
Replacement PolicySelection
First Fit 8 3 4 4 2 2 4
Next Fit 6 2 5 2 2 3 6
Best Fit 11 13 12 13 11 7 12 Series Tool
Replacement PolicyWorst Fit 6 1 3 2 0 0 3
First Fit 9 2 8 5 1 2 7
Next Fit 7 2 8 6 1 2 7
Best Fit 8 10 6 10 13 5 12
Parallel Tool
Replacement PolicyWorst Fit 7 1 6 5 0 0 5
First Fit 7 2 4 4 1 2 5
Next Fit 6 2 4 2 1 2 7
Best Fit 9 10 9 11 12 5 11
Dynamic Tool
Replacement PolicyWorst Fit 6 1 3 2 0 0 3
Figure 1. Tool cost on applying first fit tool selection policy with different tool replacement policies.
Figure 2. Tool cost on applying dynamic tool replacement policy with four tool selection policies.
D. G. RAO ET AL.411
Figure 3. Total cost on applying three tool replacement policies with four tool selection policies on dataset 1.
simulation model, these results cannot be directly gener-
alized to any other data set, but the simulation can be
carried out for other data sets also to select the best com-
bination of selection and replacement policies.
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