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Embedded fuzzy-control system for machining processes

Results of a case study

R.E. Habera,b,*, J.R. Aliquea, A. Aliquea, J. Hernándezc,R. Uribe-Etxebarriac

a Instituto de Automá tica Industrial (CSIC), N-III km 22,800, La Poveda 28500, Madrid, Spain

bEscuela Té cnica Superior de Informá tica, Ciudad Universitaria de Cantoblanco Ctra, km 15, 28049 de Colmenar Viejo, SpaincCentro Tecnoló gico IDEKO, Arriaga kalea 2, Apartado de correos 80, E-20870 Elgoibar, Gipuzkoa, Spain

Received 8 September 2002; accepted 1 February 2003

Abstract

In this paper a fuzzy-control system has been designed, implemented and embedded in an open CNC. The integration process,

design steps and results of applying an embedded fuzzy-control system are shown through the example of real machining

operations. The controller uses internal CNC signals (i.e. spindle-motor current) that are gathered and mathematically processed

by means of an integrated application. The results show that, at least in rough milling operations, internal CNC signals can

double as an intelligent, sensorless control system. Actual industrial tests show a higher machining efficiency (i.e. in-process

time is reduced by 10% and total estimated savings the system would provide are about 78%).

# 2003 Elsevier Science B.V. All rights reserved.

Keywords: Fuzzy control; Nonlinear systems; Machining processes

1. Introduction

Today’s manufacturing industry is characterized by

an increase in the demand for just-in-time production

and global manufacturing. Manufacturing has more

stringent productivity and profitability requirements

that can be satisfied only if production systems arehighly automated and extremely flexible. One of the

main activities the manufacturing industry has to deal

with is machining, a process that includes operations

that range from rough milling to finishing. There are a

number of angles from which to view the optimization

of the machining process, angles where minimum

production cost, maximum productivity and maxi-

mum profit are significant factors [1].

There are also various different ways of implement-

ing machization. The implementation on which we

will focus here attains optimal goals via automaticcontrol of the machining process. The spectrum of

‘‘conventional’’ methods (so named to distinguish

them from intelligent methods) available for designing

control systems is enormous. In the incessant pursuit

of better performance, newer approaches have been

also tested such as model reference adaptive control

(MRAC) [2], which incorporates an on-line estimation

scheme to tune controller parameters for time-varying

process dynamics. Another recently applied method

is robust control based on the quantitative feedback

Computers in Industry 50 (2003) 353–366

* Corresponding author. Tel.: þ34-91-871-19-00;fax: þ34-91-871-70-50.E-mail addresses: [email protected] (R.E. Haber),

[email protected] (J.R. Alique), [email protected] (A. Alique),

[email protected] (J. Hernández), [email protected]

(R. Uribe-Etxebarria).

0166-3615/03/$ – see front matter # 2003 Elsevier Science B.V. All rights reserved.

doi:10.1016/S0166-3615(03)00022-8


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theory (QFT) [3]. The results of these tests, however,

have not lived up to expectations, because all these

approaches have the indispensable design requisite of

an accurate (traditional) process model, e.g. differen-tial equations, transfer functions and state equations.

Unfortunately, accurate models of this sort cannot yet

be attained for the machining process.

The complexity and uncertainty of processes like

the machining process are what make the realm known

as intelligent systems technology a feasible option to

classical control strategies. Indeed, artificial-intelli-

gence techniques have roused considerable interest in

the scientific community and have been applied to

machining (e.g. [4,5]). Two interesting AI approaches

are the neural network and expert rule based on

adaptive-control constraints (ACC) [6] and evolution-

ary algorithms based on adaptive-control optimization

(ACO) [7].

However, the main disadvantage in the above-men-

tioned approaches is that neural-network and evolu-

tionary algorithm-based computation requires time

and therefore limits the performance of the intelligent

control system. Nowadays, open CNC is powerful

enough to be built into an intelligent CNC. However,

there are constraints in terms of real-time signal

processing and the implementation of complex control

algorithms.On the other hand, the majority of the work in

machining optimization is devoted to the issue of

adaptive techniques. Adaptive controllers are highly

expensive considering the time requirements, because

they estimate parameters on-line and adjust controller

gains accordingly. Also, adaptive-controller systems

must be carefully tuned, and they exhibit complex and

sometimes undesirable behavior.

In order to improve machining ef ficiency, the current

study focuses on the design and implementation of an

intelligent controller in an open CNC system. Fuzzylogic (FL) is selected from all the available techniques

because it has proven useful in control and industrial

engineering as a highly practical optimizing tool. To

the best of our knowledge, the main advantage of the

present approach is that it includes: (i) an embedded

fuzzy controller in an open CNC to deal with a real-life

industrial process; (ii) a simple computational proce-

dure to fulfill the time requirements; and (iii) no

restrictions in terms of sensor cost (it is a sensorless

application), wiring or synchronization with the CNC.

The results of the fuzzy-control strategy in actual

industrial tests show higher machining ef ficiency.

This paper is organized as follows. In Section 2 we

present a brief study of the machining process,explaining why it is considered a complex process

and setting up the milling process as a case study. In

Section 3 we design the fuzzy controller to optimize

the milling process. Next, in Section 4 we describe

how the fuzzy controller can be embedded in open

CNCs, and we discuss the key design and program-

ming stages. In Section 5 we share the experimental

results and explore some comparative studies. Finally

we give some concluding remarks.

2. The machining process

This section introduces the milling process, which

is characterized by both nonlinear characteristics and

varying process parameters. The task of controlling

this complex process is performed with the help of a

fuzzy controller that will be introduced in Section 3.

A panoramic view of a typical machining center is

given in Fig. 1.

The machining process, also known as the metal-

removal process, is widely used in manufacturing. It

consists of four basic types of operations: turning,drilling, milling, and grinding, performed by different

machine tools. One of the most complex of the four

operations is the milling process [8].

One important study of cutting-process monitoring

and control by means of current measurement shows

Fig. 1. Full view of a typical machining center.

354 R.E. Haber et al. / Computers in Industry 50 (2003) 353 – 366


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the feasibility of motor-current measurement for adap-

tive control [9]. The direct relationship between the

current consumed and the cutting force is fair enough

for a real industrial implementation of the controlsystem to be made on the basis of a main spindle’s

drive current (e.g. [10]).

Nowadays the development of open control systems

in the NCKoffers more facilities for using digital drive

signals without the need to install additional sensors.

Indeed, cutting processes show significant effects on

drive signals such as actual drive current and drive

torque. Digital drive signals have many limitations

for process monitoring alone because of the ratio

between process-related components of the signal

and non-process-related disturbances. However, main

spindle-drive current can be used to optimize cutting

speed. Signal behavior is complex because of specific

design considerations such as star-triangle switching

in the drive configuration. Nevertheless, correcting

the current offsets during the signal-processing stage

(before entering the fuzzy algorithm) solves the pro-

blem.

In terms of control-system design, the most impor-

tant aspects are the variables, parameters and typical

measures of performance that we use to characterize

the system. After a preliminary study we selected:

the spatial position of the cutting tool in terms of Cartesian coordinate axes ( x, y, z) (mm);

spindle speed (s) (rpm); relative feed speed between tool and worktable

( f , feed rate) (mm/min);

cutting power invested in removing metal chipsfrom the workpiece (Pc) (kW);

current consumed at the main spindle during theremoval of metal chips ( I S) (A);

radial depth of cut (a, cutting depth) (mm); and cutting-tool diameter (d ) (mm).

In order to evaluate system performance, we needed

to select certain suitable performance indices. The

milling process basically consists of two operations,

rough milling and finishing. The differences in these

operations’ objectives are what will decide which

performance index is useful in each operation. The

quality and geometric profile of the cutting surface

is paramount in finishing operations, whereas the

quantity of metal removed from the workpiece is

the main issue in rough-milling operations.

This work dealt essentially with rough milling, so

the main index was the metal-removal rate (MRR).

3. Fuzzy-logic controller for machining

processes

The fuzzy controller follows rather classic lines.

The controller’s actual core performs on-line actions

to control the feed rate. The three basic tasks known

as fuzzification, decision-making and defuzzification

were implemented on-line. The main design steps for

this controller and their corresponding implementa-

tion were as follows.

1. Defining input and output variables, fuzzy parti-tioning and building the membership functions.

The input variables included in the error vector

e are the current-consumed error (D I S in A) and

the change in current-consumed error (D2 I S in A).

The manipulated (action) variable we selected was

the feed-rate increment (D f in percentage of the

initial value programmed into the CNC), whereas

the spindle speed is considered constant and preset

by the operator.

eT ¼ KE D I S; KCE D

2 I S ; u ¼ ½GC D f (1)

where KE, KCE and GC are scaling factors for

inputs (error and change in error) and output

(change in feed rate), respectively.

The fuzzy partition of universes of discourse

and the creation of the rule base were drawn from

the criteria of skilled operators, although we did

have to apply a ‘‘cut and trial’’ procedure as well.

Fig. 2 shows the fuzzy partition thus obtained.

2. Constructing the rule base and generating crisp

output.We will consider a set of rules consisting of

linguistic statements that link each antecedent

with its respective consequent, having, for in-

stance, the following syntax if

D I S is positive and D2 I S is positive; then D f is

positive big

A matrix arrangement of the rule basis is shown

in Table 1.

R.E. Haber et al. / Computers in Industry 50 (2003) 353 – 366 355


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The controller output is inferred by means of the

compositional rule. The sup-product compositional

operator was selected for the compositional rule of

inference.

mðD

I S;D

2

I S; D

f Þ

¼ S 1mn

i¼1½T 2½mD I Si ð

D I SÞ;mD2 I SiðD2 I SÞ;mD f i ðD f Þ (2)

where T 2 represents the algebraic-product operation

and S 1 represents the union operation (max), m n ¼ 9. The crisp controller output, which is used tochange the machine-table feed rate, is obtained by

defuzzification employing the center-of-area (COA)

method [11] defined as

D f ¼

PimRðD f iÞ D f i

PimRð

D f iÞ

(3)

where D f is the crisp value of D f i for a given crisp

input (D I Si , D2 I Si ).

The crisp-control action (generated at each sam-

pling instant) defines the final actions that will be

applied to the CNC set points. The strategy used to

compute f determines what type of fuzzy regulator isto be used. In this case it can be a PI-FLC

f ðk Þ ¼ f ðk 1Þ þ D f ðk Þ (4)

or a PD-FLC

f ðk Þ ¼ f 0 þ D f ðk Þ (5)

Feed-rate values ( f ) were generated on-line by the

controller and fed in with the set point for the spindle

current ( I Sr) and measured value ( I S) provided by

the CNC from the internal spindle-drive signal, as

explained in Section 4.The static input–output mapping can be represented

by the nonlinear control surface shown in Fig. 3, con-

sidering that D I S 2 ½1:5; 1:5, D2 I S 2 ½1:5; 1:5 and

D f 2 ½10; 10.These nonlinearities are essential in order to achieve

good performance. Recently it has been proved that

when trapezoidal membership functions are used,

the resulting system is the sum of a global nonlinear

controller (static part) and a local nonlinear PI con-

troller (dynamically changing with regard to the input

Fig. 2. Fuzzy partitions and membership functions for (a) D I S, D2 I S and (b) D f .

Table 1

FLC rule base

D2 I S D I S

N Z P

N NB NS ZE

Z NS ZE PS

P ZE PS PB



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space) [12]. Therefore, it is expected that this kind of

membership function can be relevant for dealing with

nonlinear process behavior.

3.1. Experiments for evaluating control-system

performance

The preliminary experiments were conducted using

a 5.8 kW, 4-axis milling machine, the ANAK-MATIC-

2000-CNC, equipped with a Fagor 8025 CNC, which

was interfaced with a personal computer over a 9600-

baud RS-232 communications link (see Fig. 4a).

The objective for this fuzzy-control system was to

manipulate the machine tool’s cutting conditions, with

some further possibilities in addition to those provided

by the CNC-PLC alone. In this new context, while the

CNC-PLC guided the sequence of tool positions and/or

the path of the tool during machining, the fuzzy con-troller determined the proper feed rate f . KE ¼ KCE ¼ 1and GC ¼ 1 were used as scaling factors. We added apersonal computer to the control scheme in order to

measure the spindle-motor current I S on the basis of a

Sandvik Coromant TMCS-001 sensor and to perform

the generation of f values. Other measurement devices

such as a PCL-718 acquisition card were also installed.

The set point I Sr was fixed manually so that the

MRR would reach the maximum allowable value under

the rest of the technological constraints. The control

scheme was implemented on the basis of these defini-

tions. We developed the real-time control system in

C/Cþþ programminglanguageon a Pentium PC.Feed-rate values ( f ) were generated on-line by the controller

and fed in with the set point I Sr and measured value I S.

In order to evaluate the performance of the algorithm,

two tests were performed using a 2011 aluminum-alloy

Fig. 3. Fuzzy control surface.

Fig. 4. (a) Experimental setup and (b) cross-section of the

workpiece used in experiments.



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Fig. 5. Real-time control dynamics for (a) test 1 and (b) test 2.



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workpiece. A cross-section of the workpiece used

is shown in Fig. 4b. The profile consisted of one ramp

and three step-shaped disturbances (12 mm maximum

depth of cut).

The spatial position of the cutting tool was set man-

ually by the operator, and the tool was held at a constant

vertical position during the experiments. The machin-

ing was defined to proceed in one direction only. New

milling cutters were used in this experimental work.

The control system’s behavior was evaluated by

assessing accuracy and control-loop stability. In orderto evaluate the accuracy of the control system, the

integral absolute error (IAE), the integral square error

(ISE) and the integral of time-weighted absolute error

(ITAE) were calculated. The transient response was

analyzed to evaluate control-loop stability as well.

Moreover, the overshoot M pt was also computed

because it should be tracked as a warning of dangerous

situations and proximity to control-loop instability.

Fig. 5 shows the real-time control dynamics of two

different tests. First of all, the fuzzy-controlled milling

process is robustly stable under real-time conditions.As clearly shown in Fig. 5, the spindle-motor current

remains constant during machining, as is most clearly

visible when the cutting depth is one-third to one-half

of tool diameter, regardless of disturbances and non-

linearities in the milling process. Only in the first step

(small depth of cut) is the set point not reached. Hence

the real-time experiment verifies the applicability of

fuzzy-control systems designed using the methods

herein introduced. All the experimental data including

performance indices are summarized in Table 2.

4. Open CNC and new control functions

The demand for open control systems is on the rise

as the result of the current need for enhanced control

functionality in industry. To date, only a small group

of machine-tool manufacturers has accepted adaptive

controllers. One main reason for their reluctance is the

need for an accurate model for calculating controller

parameters. Additionally, adaptive controllers cannot

be applied reliably to various combinations of machin-

ing processes, materials and tools without majorchanges in the control algorithm and its parameters.

Some European CNC manufacturers do not include

any adaptive functions in their products [13], while

others offer an ‘‘ac control’’ whose performance is very

restricted [14]. The adaptive module is rarely used in a

flexible machining environment containing many types

and combinations of materials and tools, where rigid

CNCs would actually constitute a major handicap.

There are also some commercially available add-on

systems (e.g. OPTIMIL by OMAT control technolo-

gies) that can perform real-time adaptive control [15].OPTIMIL is based on an expert system that combines

tool and material information. However, the underlying

technology has some limitations, such as the annoying

calibration procedure (a learning stage or ‘‘teach-in’’ is

required), plus synchronization problems with other

applications in open CNCs.

The way of embedding the new generation of

intelligent controllers in CNCs is now ready for

use. Nowadays open CNCs enable internal control

signals to be gathered and mathematically processed

Table 2

Features and results of two laboratory tests

Test 1 Test 2

Nominal command feed rate f 0 (mm/min) 300 200

Nominal spindle speed s0 (rpm) 2000 1500

Maximum cutting depth amax (mm) 10 12

Cutting tool F1205-10 Vallorbe F1205-25 Vallorbe

Diameter of the cutting tool (mm) 10 25

Number of teeth 2 2

Workpiece material Aluminum alloy 2011 Aluminum alloy 2011

Setpoint I Sr (A) 1.4 2.2

Overshoot M pt (%) 13.46 17.59

Settling time t s in the last step (s) 8.05 9.45

IAE index 233.14 511.54

ISE index 74.68 484.41

ITAE index 1105.8 1498.6



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by means of integrated applications such as additional

functions embedded right in the control core. By

this means control systems’ original features can be

improved upon, with the addition of machine-toolmanufacturers’ know-how and new control-system

developments incorporated in the CNC.

At present two levels of opening are available from

machine-tool and CNC manufacturers. The degree of

opening varies, however, depending on the CNC part

involved. There are two categories of opening/open-

ness, differentiated by the importance of that feature

from the control-system viewpoint: opening of man/

machine communication (MMC) and opening of the

CNCs numerical control kernel (NCK). The first cate-

gory is the one responsible for interaction with the user

(e.g. of fice applications can be developed using the

DDE dynamic data exchange protocol). Use of a bus

(e.g. a multi-point interface bus) enables communica-

tion with low-level data. The more important level

of opening from the control-system viewpoint can be

found in the NCK, where real-time critical tasks (e.g.

axis control) are scheduled and performed.

4.1. Embedding the fuzzy controller

This section explains how the fuzzy controller

designed in Section 3 is embedded in the open CNC.Nowadays the integration of any control system in the

CNC is a complex task that requires the use of various

software utilities, technologies and development tools.

Three classical technologies can be used: a software-

developing tool (Cþþ, Visual Basic), an open- and

real-time CNC and communications technology.The general outline of the control system is depicted

in Fig. 6. First the fuzzy controller was programmed in

C/Cþþ and compiled, and as a result a dynamic-link library (DLL) was generated. Other tools were used as

well. A Sun Workstation, the UNIX operating system

and Cþþ were used to program the NCK. A PC, theWINDOWS 9X operating system and Visual Basic

were used for programming the MMC. Inter-module

communications between the MMC and the NCK

were established through DDE. Finally, and for the

sake of simplicity, the user interface was programmed

in Visual Basic.

The application was developed on the basis of a

Sinumerik 840D CNC. The process of integrating the

software application into the NCK involved a series of

steps. Development, including editing, compiling and

code linking, was run at a workstation. After that, the

file was transferred to the PC, where OEM software

ran debugging routines. Finally, the code was copied

on a PCMCIA card and inserted into the CNC [16].

An experimental internal data-acquisition system

was developed and used to record the control internal

spindle-drive signal. The system enables a selecteddrive signal to be recorded and provides stored data on

the hard drive of the Sinumerik 840D user-interface

Fig. 6. Control system diagram.



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PC (MMC). Internal data-acquisition software was

developed to obtain control information. The maxi-

mum sampling frequency could not be any longer than

500 Hz, defined by the servosystems’ control cycle.Therefore the acquisition-signal software works at the

same time for which servosystems are configured

in the CNC (i.e. 2 ms). The software consists of a

data-acquisition module in the NCK that records the

selected data into an internal buffer and an MMC

background task that receives the completed measure-

ment and stores it in the hard drive of the user-inter-

face PC. Data transfer is performed by splitting the

recorded data into a number of fragments, due to

limitations in the Sinumerik 840D’s file systems.

The signals to be recorded (in our case the current

consumed at the spindle) are configured using spe-

cially added machine data. Recording can be started

and stopped either manually through an MMC appli-

cation or under the NC program’s control.

5. Industrial tests: evaluation

Tests were carried out in the SL400 SORALUCE

machining center, which was equipped with a SIE-

MENS open CNC Sinumerik 840D. The SL400 model

SORAMILL milling machine has a traveling columnand a fixed table configuration. It possesses high-

precision slideways that allow all three axes to

reach a speed of up to 15 m/min. The machine has

high-rigidity, high-precision features, and the work-

piece has no influence whatsoever on the moving part.

The cutting tool was a Sandvik D38 R218.19-

2538.35-37.070HA (020/943) face mill 25 mm in dia-meter with four inserts of the SEKN 12 04 AZ (SEK

43A, GC-A p25) type. The workpiece material used

for testing was F114-quality steel. The maximum depth

of cut was 20 mm, the nominal spindle speed was S 0 ¼850 rpm, and the nominal feed rate, f 0 ¼ 300 mm/min.The actual dimensions of the profile were 334 mm 486 mm. The profile is depicted in Fig. 7b.

We used an internal spindle-drive signal as our

control. The processing-signal analysis was done

on-line. Signal processing focused on correcting cur-

rent offsets. Current offsets were corrected on the basis

of the model that relates spindle speed to current [17].

This model was created using linear regression. More-

over, further filtering was performed using the mean

value of the signal in the sampling period.

The set point was estimated according to a/the

constraint given by the power available at the spindle

motor, the material and the tool characteristics.

In order to compare the performance of the PI and

PD-FLC, a reference value of 18 A was set during the

experiments. In the second study, the set point used

was 22 A in order to make an appropriate comparison

with a commercial product.The results of applying two fuzzy controllers (PI and

PD-FLC) to machine an irregular profile (see Fig. 7b)

are depicted in Fig. 8. Fig. 8a shows the behavior of

Fig. 7. (a) Tool used for industrial tests and (b) workpiece for industrial tests.



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the current consumed at the main spindle, and Fig. 8b

depicts the variation in the feed rate. The negative

effect of integral components is reflected in the tran-

sient response.

An illustrative example was inserted to uphold the

validity of the theoretical approaches examined in this

paper. The second study compared an embedded PD-

FLC, the OPTIMIL [15], and the CNC working alone.

Fig. 8. (a) Behavior of the controlled variable and (b) action variable (feed rate) when machining an irregular workpiece.



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The results are shown in Fig. 9. The reduction in

machining time was very similar for the OPTIMIL

and the embedded PD-FLC (10%), yet the overshoot

was bigger for the embedded PD-FLC (23.7%).

From the technological viewpoint, this overshoot is

allowable for rough milling operations. It is important

to note that in some approaches the goal of controller

design is to limit the overshoot percentage to 20%

[3].

The OPTIMIL, when used as external equipment,

requires synchronization steps and a tedious calibra-

tion procedure including a learning phase. On the

other hand, the embedded PD-FLC does not require

any calibration procedures or external sensors, and the

Fig. 9. (a) Time responses of the three control systems and (b) behavior of the controlled variable for the three control systems analyzed.



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application can be supplied with just a CNC and no

other external equipment. The workpiece after the

rough milling operation is depicted in Fig. 10.

This study did not consider linear controllers for

this application, because the fuzzy controller has been

demonstrated to yield better results than linear controlloops [18]. The issue of stability is not dealt with here

either. Some strategies for checking the stability of

control systems of the type herein introduced are

suggested in [19].

5.1. Scientific and technological prospect

The embedded fuzzy-control system will have an

immediate application in milling machines and

machining centers. The methodology and algorithm

can, however, be applied in any cutting process such asturning or drilling. Therefore, the results shown in this

paper will be applicable to a large sector of the

equipment-production industry. Certainly, in spite of

the fact that the software was developed for one kind

of open CNC, the architecture ensures flexibility and

allows the integration of a variety of control strategies

in order to embrace the largest possible number of

applications.

Furthermore, the solution is highly competitive,

since it uses the CNCs own resources and requires

no additional equipment such as sensors, acquisition

and signal-processing cards or other external devices.

This is a key issue if the proposed solution is to be

integrated in an industrial environment.

On the basis of the economic study provided by

SORALUCE S. Coop., this system may be expected toboost machine-tool suppliers’ competitive capacity

considerably [20]. The economic benefits for

machine-tool manufacturers would come basically

from the increase in sales resulting from the avail-

ability of an easily installable, configurable, program-

mable solution that would lead to an increase in

minimum machine price and an immediate pay-off

of the manufacturer’s investment. Moreover, machine-

tool users could enhance their machine-tool use ratio,

machining-operation safety (due the automatic selec-

tion of cutting speeds) and the useful life of theircutting tools (due to a reduction of improper cutting

conditions).

SORALUCE S. Coop. has run a major study to

evaluate the savings provided by installing this sys-

tem. An approximate calculation was made for the

case of milling machines, considering the mean cost of

other commercial solutions. The estimates of cost and

savings are based on data on the standard machining

operations involved in milling. The estimate of cut-

ting-tool costs, however, takes only rough-milling

Fig. 10. The workpiece after eight operations using the PD-like FLC.

364 R.E. Haber et al. / Computers in Industry 50 (2003) 353–366


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operations into account. The total estimated savings

are about 78%.1

6. Conclusion

The results of transferring technology to a machine-

tool manufacturer through cooperation with a techno-

logical center show the effectiveness of a fuzzy-con-

trol system for dealing with the nonlinear behavior of

the machining process. The embedded fuzzy control-

ler is able to work using only internal CNC signals.

Moreover, it can run in parallel with other applications

without posing any synchronization problems. How-

ever, future work is necessary in order to refine the

fuzzy controller’s performance and so to improve the

transient response.

All this work, from hypothesis to implementation

and experimentation, including design and analysis,

is done following the classic patterns. Our focus at

all times lay on the practical implementation, so the

results we have presented in this paper are for a real-

life industrial plant. The results show that internal

CNC signals can double as an intelligent, sensorless

control system. Actual industrial tests show a higher

machining ef ficiency; in-process time is reduced by

10% and the total estimated savings the system wouldprovide are about 78%.

Acknowledgements

This work has been partially supported by two

Spanish-funded projects, PETRI 95-0336-CT

COCOM and TAP99-1122-C02-01 MHECOS. The

authors would like to thank referees for their helpful

suggestions and comments. Moreover, the authors

express their deepest gratitude to all the people whocontributed to the implementation of the control sys-

tem at SORALUCE S. Coop. and Ideko Technology

Center. Likewise, the authors gratefully acknowledge

the contributions of S. Ros, G. Arrate and J. Aranceta

in assisting with the implementation presented in this

work.

References

[1] Y. Koren, Control of machine tools, Journal of Manufacturing

Science and Engineering 119 (1997) 749–755.

[2] L.K. Lauderbaugh, A.G. Ulsoy, Model reference adaptive

force control in milling, ASME Journal Engineering of

Industry 111 (1989) 13–21.

[3] S.J. Rober, Y.C. Shin, O.D.I. Nwokah, A digital robust

controller for cutting force control in the end milling process,

Journal of Dynamic Systems, Measurement and Control 119

(1997) 146–152.

[4] R.E. Haber, R.H. Haber, S. Ros, A. Alique, J.R. Alique,

Dynamic model of the machining process on the basis

of neural networks: from simulation to real time applica-

tion, Lecture Notes in Computer Science 2331 (2002) 574–

583.

[5] L.-C. Lin, G.-Y. Lee, Hierarchical fuzzy control for C -axis of

CNC turning centers using genetic algorithms, Journal of Intelligent and Robotic Systems: Theory and Applications 25

(3) (1999) 255–275.

[6] Y. Liu, L. Zhuo, C. Wang, Intelligent adaptive control in

milling process, International Journal of Computer Integrated

Manufacturing 12 (5) (1999) 453–460.

[7] Y. Liu, C. Wang, Neural networks based adaptive control and

optimization in milling process, International Journal of

Advanced Manufacturing Technology 15 (1999) 791–795.

[8] H. Schulz, Hochgeschwindigkeitsbearbeitung Carl Hansen

Verlag Munchen Wien, Germany, 1999.

[9] Y. Altintas, Prediction of cutting forces and tool breakage in

milling from feed drive current measurements, ASME Journal

of Engineering of Industry 11 (1992) 386–392.

[10] T. Kim, P. Kim, Adaptive cutting force control for a

machining centre by using indirect cutting force measure-

ments, International Journal of Machine Tool Manufacturing

36 (8) (1996) 925–937.

[11] R. Yager, D. Filev, Essentials of Fuzzy Modeling and Control,

Wiley, New York, 1994, pp. 313–354.

[12] H. Ying, Analytical structure of a typical fuzzy controller

employing trapezoidal input fuzzy sets and nonlinear control

rules, Information Science 116 (1999) 177–203.

[13] Fagor Automation, Needs of new numerical controls,

Producción Mecánica 3 (1999) 74–83 (in Spanish).

[14] Siemens AG, Sinumerik 840D/FM-Nc., 1996, pp. 36–40.

[15] A. Nosrat, Adaptive controls lead to peak ef ficiency,

American Machinist, April (1997) 78–80.[16] Siemens AG, Sinumerik 840D, OEM-package NCK, Soft-

ware release 4, User’s Manual, Siemens AG, 1999.

[17] M. Kaever, S. Platen, M. Weck, Control integrated process

monitoring for rough milling operations—innovative logging

module solution, in: Proceedings of the 2nd International

Workshop on Intelligent Manufacturing Systems, Leuven,

Belgium, pp. 727–736.

[18] R.E. Haber, R.H. Haber, A. Alique, S. Ros, Application of

knowledge-based systems for supervision and control of

machining processes, in: S.K. Chang (Ed.), Handbook of

Software Engineering and Knowledge Engineering, vol. 2,

World Scientific, Singapore, pp. 673–710.

1 More details about costs and implementation cannot be

provided because they may include confidential data not releasable

under Mondragón Corporación Cooperativa (MCC) policy.



14/14

[19] G. Schmitt-Braess, R.E. Haber, R.H. Haber, A. Alique,

Multivariable circle criterion: stable fuzzy control of a

milling process, in: Proceedings of the IEEE International

Conference on Control Applications, Glasgow, UK, 2002,

pp. 385–390.[20] SORALUCE S.C.L., Design and implementation of FLC to

regulate the cutting force and its application to machine tool

(CONBOMAQ), Internal Report, Spanish National Plan for

R&D (PROFIT), FIT-020200-2001-49, 2001.

R.E. Haber was born in Santiago the

Cuba, Cuba in 1969. He received the BE

degree with first class honors in auto-

matic control engineering from the Uni-

versidad de Oriente (UO), in 1992. In

1995, he was the recipient of PhD grant

supported by the Spanish Office for

Scientific Cooperation (AECI) in Spain.He received an excellent cum laude PhD

in industrial engineering from Technical

University of Madrid (UPM) in 1999.

He joined with the Spanish Council for Scientific Research

(CSIC) in 1999 working in several research and development

projects. In 1999, he also joined as Assistant Professor with the

Computer Engineering Department at the High Technical School

in the Universidad Autonoma de Madrid (UAM) teaching in

several graduates courses. He has published several technical

papers in specialized journals and chapters of books. His research

interests include control theory and applications, hardware–

software solutions, soft-computing techniques, classical and

adaptive control, supervisory control, complex electromechanical

processes.

J.R. Alique received the BSc degree in

physics from the University Complu-

tense of Madrid (UCM), Spain in 1969.

He received the MSc and PhD degrees in

physics sciences from International In-

stitute of Philips, North Holland in 1971,

and University Complutense in 1973.

From 1969 to 1970, he was Assistant

Professor of physics and automation at

UCM. He has been working in automa-

tion since 1972 in the Spanish Council

for Scientific Research (CSIC). From 1991 to 1996, he was theDirector of the Technology Transfer Office in the CSIC. From 1996

to 2001, he was the vice director of R&D projects in the Ministry of

Science and Technology. He is currently researcher at Instituto de

Automatica Industrial (CSIC) and Head of the Computer Science

Department. His research interests include automation of machining

processes, numerical controls, robotics systems and intelligent

modeling and control.

A. Alique received the BSc and PhD

degrees in physics from University

Complutense of Madrid in 1972 and

1979, respectively. He is currently re-

searcher at Instituto de Automatica

Industrial (CSIC). He has published

several technical papers on supervisory

fuzzy control and modeling of machin-

ing processes. He is currently the main

researcher of two projects funded by

CICYT. His research interests include

fuzzy modeling and control, robotics systems and intelligent

control.

J. Hernández was born in 1969. He

received the degree of physics from the

Universidad de Valladolid in 1992. He

joined the Research Centre IKERLAN in

1993 being the recipient of PhD grant.

He received the PhD degree in physics in

1997. In 1998 he joined IDEKO Tech-

nology Centre. As a researcher in the

Control Engineering Department, he has

taken part in several European and

international projects (SIMON, COM-

PRO, IDMAP, IMS-SIMON) in the field of machining process

monitoring and control. His main fields of interest include:

monitoring and control of machining and forming processes,monitoring of machine components, and development of control

strategies for improving machine-tool performance.

R. Uribe-Etxeberria was born in 1967.

In 1990 he got the degree of Industrial

Technical Engineering in the field of

electronics. In 1991 he received the MSc

degree in control engineering and infor-

mation technology at UMIST (University

of Manchester Institute of Science and

Technology). He joined IDEKO in 1991

as a research engineer working in the

fields of control engineering, sensoring

and signal processing and their applica-

tions in the machine-tool world. Since 1996, he is the head of the

Control Engineering Department at IDEKO. His fields of interest are:

machine automation, monitoring and control of machining processes

and the application of information technology to machine tools.


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