DEFECTIVE METAL END DETECTION WITH A FUZZY SYSTEM

Perfecto Mari

no Espi

neira, Vicente Pastoriza Santos,

Miguel Santamar

ıa S

anchez, Emilio Mart

ınez Exp

osito

Universidad de Vigo, Departamento de Tecnolog

ıa Electr

onica, E.T.S. Ingenieros Industriales,

Campus Lagoas-Marcosende, 36310 Vigo, ESPA

Keywords:

Image processing, fuzzy control, machine vision system, intelligent fault detection.

Abstract:

The authors have been involved in developing an automated inspection system, based on machine vision, to

improve the repair coating quality control (RCQ control) in can ends of metal containers for ﬁsh food. The

RCQ of each end is assesed estimating its average repair coating quality (ARCQ). In this work we present

a fuzzy model building to make the acceptance/rejection decision for each can end from the information

obtained by the vision system. In addition it is interesting to note that such model could be interpreted and

supplemented by process operators. In order to achieve such aims, we use a fuzzy model due to its ability to

favour the interpretability for many applications. Firstly, the easy open can end manufacturing process, and

the current, conventional method for quality control of easy open can end repair coating, are described. Then,

we show the machine vision system operations. After that, the fuzzy modeling, results obtained and their

discussion are presented. Finally, concluding remarks are stated.

1 INTRODUCTION

In the food canning sector, in the easy open can end

manufacturing process, to guarantee the desired prod-

uct lifespan, a manual, nondestructive testing (NDT)

procedure is carried out. Due to the high process-

ing rate, only an small part of each lot is veriﬁed.

Therefore, it is important to develop an automated in-

spection system to improve the easy open can end re-

pair coating quality control process (all can ends are

checked, and inline). It is for this reason that we had

been involved in the design and implementation of an

inline, automated machine vision system to evaluate

the repair coating of the can ends, that we have named

end repair coating inspection system (ERCIS). In this

work we explore the use of fuzzy models to make the

acceptance/rejection (A/R) decision for each can end.

A Takagi-Sugeno-Kang fuzzy model is developed us-

ing a neuro-fuzzy modeling. The remainder of this

paper is organized as follows: in the next section we

provide an overview of the easy open can end manu-

facturing process and its repair coating quality control

process. Section 3 shows the machine vision system

operations. Then, Section 4 describes the fuzzy mod-

eling. The results obtained and their discussion are

presented in Section 5. Finally, we state concluding

remarks in Section 6.

2 BACKGROUND

A can consists of can body and can end, which are

made from aluminum or steel. Can ends, from hence-

forth ends, are used for all type of cans and can be

standard or easy open. In this paper, we study a spe-

ciﬁc end format named 1/4 Club, with an easy-open

tab in one of its corners.

2.1 Easy open end manufacturing

process

Easy open ends are made from pre-coated metal coils

or sheets. Ends are stamped from coil or sheets in a

press. After stamping, the ends are scored in a pre-

deﬁned geometric shape (scoreline) intended to ease

the end opening. Finally, a tab is attached to form

an easy open end. These steps are performed after

the end piece has been coated and therefore damage

the coating, especially on the scoreline. Repair coat-

ing, which has a ﬂuorescent pigment, is applied after

these steps on the required area to restore the integrity

of the coating.

270

Mariño Espiñeira P., Pastoriza Santos V., Santamaría Sánchez M. and Martínez Expósito E. (2005).

DEFECTIVE METAL END DETECTION WITH A FUZZY SYSTEM.

In Proceedings of the Second Inter national Conference on Informatics in Control, Automation and Robotics, pages 270-274

 SciTePress

2.2 Easy open end repair coating

quality control

Presently a visual inspection of the easy open ends is

carried out (Lin et al., 1998; CFIA, 1998), where the

inspectors assess the repair coating on the scoreline.

This visual inspection is a manual and NDT proce-

dure. To assist in this end inspection the repair coating

has a ﬂuorescent pigment that stands out as a bright

light blue when excited by an ultraviolet black light,

while the background color remains unchanged. This

inspection is based on a statistical sampling. It is be-

cause of this and the high rate of the repair coating

process (100 to 500 ends per minute, depending on

the end format) that only a small part of each lot is

veriﬁed. Therefore, defective can ends can be sent to

the canneries.

3 MACHINE VISION SYSTEM

OPERATIONS

The vision algorithm running on ERCIS take care of

inspects the repair coating quality (RCQ) on each end.

The vision algorithm has two parts: one ofﬂine and

other inline. The ﬂowchart of ERCIS is shown in Fig.

Figure 1: Flowchart of ERCIS

3.1 Ofﬂine

Before the ERCIS begins the continuous or online in-

spection of ends is necessary to conﬁgure or reconﬁg-

ure a series of parameters that will be used later in the

inline processing.

3.1.1 Time delay conﬁguration

An ofﬂine adjustment can be necessary to set the de-

lay, input delay time (IDT), between the sensor at the

ERCIS input detecting the end and the end reaching

the camera, and the delay, output delay time (ODT),

between the ERCIS output sensor detecting the end

and the end arriving next to the rejection system.

3.1.2 Region of interest deﬁnition

The scoreline is enclosed in the region of interest

(ROI). Each quadrant of this ROI, and by simmetry,

the whole ROI is geometrically modeled by the para-

meters b and r (Fig. 2). Besides these parameters it is

necessary to add an e width to the ROI (Fig. 2).

ROI

HOR

= f (b, r, e) (1)

Figure 2: End ROI quadrant geometrical model

Parameters will be adjusted before the line continu-

ous working, and they depend directly on the distance

between and and camera (working distance).

3.1.3 Parameter conﬁguration to decide the end

rejection

In order to take the A/R decision for each end dur-

ing the inline processing is necessary to ofﬂine con-

ﬁgure the Minimum Average Repair Coating Quality

(MARCQ) of the end to not reject it. The object of

this parameter can be seen on Subsection 3.2.2.

3.2 Inline

The end continuous inspection can only start after the

ofﬂine parameter conﬁguration. This inline process

has for each end the following sequence of steps:

3.2.1 Acquisition

This process undertakes the detection of the ends and

acquisition of images of them. It is divided into the

subprocess: end detection before the ERCIS, and end

acquisition.

3.2.2 Evaluation

If the decision queue is not empty then an image has

already been acquired and can be processed. This

process has the following steps:

• End center location and end orientation.

• ROI rectiﬁcation. The ROI is converted into a

straight line strip to ease its analysis. This strip is

a Look-up-Table (LUT) whose size is n × e pixels.

The length n, in which is divided the ROI perime-

ter, depends on the selected resolution. The recti-

ﬁcation method employed selects for each one of

n × e pixels the nearest 4-neighbour (Gonz

alez and

Woods, 2002).

• Repair coating quality: The RCQ is assessed ana-

lyzing one-by-one all the n positions of the LUT

by means of the model obtained in Section 4.

• End A/R decision: The average repair coating qual-

ity (ARCQ) is computed from the RCQ values at

the n positions in which have been divided the ROI.

Then, an end have to be rejected when the condi-

tion ARCQ<MARCQ is given (see the meaning of

MARCQ in Subsection 3.1.3).

• Update the decision queue: The decision queue

must be updated after making the A/R decision of

each end.

3.2.3 Expulsion

This process is subdivided in: end detection after the

ERCIS, and end expulsion.

3.2.4 Stop

If stop signal is activated then the process goes to the

ﬂowchart end with independence of the current state

of the process.

4 MODELING

In order to evaluate the RCQ on each one of n posi-

tions of the LUT, a set of a few attributes that contain

most of the relevant information on each one of the n

positions is studied. The 9 attributes computed from

each e-pixel group of n

LUT position are: Maxi-

mum pixel intensity (Max), Minimum pixel intensity,

Mean pixel intensity is a measure of central tendency

(location), Median pixel intensity is a measure of cen-

tral tendency (location), Pixel intensity standard devi-

ation ( Std) is a measure of dispersion, Pixel intensity

skewness is a measure of the asymmetry, Pixel inten-

sity center of mass (CoM), Pixel intensity moment of

inertia about an axis passing through the CoM, and

Pixel intensity bisector.

A fuzzy inference system (FIS) (Kosko, 1992;

Yager and Zadeh, 1994; Klir and Yuan, 1995), whose

inputs are the selected attributes, will be used to eval-

uate the RCQ on each of the n positions. As an exces-

sive number of inputs prevents the interpretability of

the underlying model and increases the computational

burden, we look for a model with a trade off between

high accuracy and reduced number of inputs. We got

a modeling problem with 9 candidate inputs and we

want to ﬁnd the 3 most inﬂuential inputs as the in-

puts of the model. We so can build 84 fuzzy mod-

els, each one with a different combination of 3 inputs.

The proposed FIS model is a Takagi-Sugeno-Kang

(TSK) inference system (Takagi and Sugeno, 1985;

Sugeno and Kang, 1988). These models are suited for

modeling non-linear systems by interpolating multi-

ple linear models. The TSK model is designed with

zero order (singleton values for each consequent), 3

of 9 attributes as inputs and RCQ as output. The TSK

model is developed using the adaptive neuro-fuzzy in-

ference system (ANFIS) algorithm (Jang, 1993; Jang

and Sun, 1995).

We use a quick and straightforward way of neuro-

fuzzy modeling input selection using ANFIS to im-

prove the interpretability (Jang, 1996). This input

selection method is based on the hypothesis that

the ANFIS model with smallest RMSE (root mean

squared error) after one epoch of training has a greater

potential of achieving a lower RMSE when given

more epochs of training.

Representative input-output data set of the system

should be selected to tune a model. We have only

worked with a speciﬁc end format named 1/4 Club,

with an easy-open tab in one of its corners. We have

selected a collection of 11 ends that agglutinate all

possible end repair coating defects. The obtained

LUT for each end has a length n of 702 positions, with

a width e of 19 pixels. After removing instances with

outlier values, the data set was reduced to 6669 en-

tries. This data set is divided into training and testing

sets of size 3335× 19 and 3334× 19 respectively. The

testing set is used to determine when training should

be terminated to prevent overﬁtting.

It has been selected grid partitioning as the AN-

FIS partition method. The best model after one epoch

of training selects as input attributes the maximum

(Max), the standard deviation (Std), and the center

of mass (CoM). The problem is that this partitioning

leads to a high number of rules, 2

= 8 rules for each

model.

Figure 3: Fuzzy model membership functions

In order to reduce the model complexity we use

subtractive clustering (Chiu, 1994) for the 3 inputs

previously selected. We have selected the model with

range of inﬂuence 0.5 that has 3 rules that gives a

RMSE of 0.3106 for training and 0.3091 for testing.

Table 1: Fuzzy Models Rules

Rules Repair Coating

Quality

If Max is Max1 & Std is Std1 S1[5]

∗

& CoM is CoM2 Defective

If Max is Max2 & Std is Std2 S1[5]

& CoM is CoM3 Defective

If Max is Max3 & Std is Std3 S2[10]

& CoM is CoM1 Acceptable

∗

S[m] = singleton (m=mean)

We can further reﬁne said model performance ap-

plying extended ANFIS training. The ﬁnal model ob-

tained use Max, Std, and CoM as model inputs, RCQ

as output, and 3 rules (Table 1) to deﬁne relationships

among inputs and output. The membership functions

for each input feature are shown in Fig. 3 and single-

ton values for each consequent in Table 1.

This model gives a RMSE of 0.0102 for training

and 0.0101 for testing, more similar values which in-

dicate that there is no overﬁtting. Regarding the inter-

pretability of the model and from its rules, is deduced

that the RCQ at n

LUT position is acceptable if and

only if at said position, see Fig. 3 and Table 1, the

maximum pixel intensity is higher than 150 and the

standard deviation is higher than 35 and the center of

mass is close to 9.5, which is the e-pixel group center.

The interpretation of this is the following:

• The higher the maximum pixel intensity, the higher

the lacquer quantity.

• As a defect region has little or no lacquer and is

more uniformly distributed than an acceptable re-

gion, then the pixel intensity is less scattered (less

Std) in defect regions.

• As the ROI is positioned in the way that the score-

line is at its center zone, and as an acceptable end

has the highest lacquer level at scoreline, then at

each one of n LUT positions the nearer of the e-

pixel group center is the CoM, the better the RCQ.

5 RESULTS

The RCQ of each end is assessed estimating its

ARCQ. This average quality is computed from the

RQ of the n positions in which has been divided the

ROI, and where the RCQ at each n position is ana-

lyzed by means of the FIS obtained.

Figure 4: ARCQ classiﬁcation of the ends

Fig. 4 shows the ARCQ classiﬁcation of the 11

ends previously used to tune the fuzzy model. The

ends were sorted in descending order of ARCQ.

As each end have to be rejected when

ARCQ<MARCQ is given (see the meaning of

MARCQ in Subsection 3.1.3) then what ends are

rejected will depend on the MARCQ value selected.

For example, if MARCQ is 80 then all 11 ends are

rejected. This ﬂexibility to be able to modify the

rejection threshold is an important property of the

ERCIS.

But the most important result is that, with indepen-

dence of the MARCQ selected, the ARCQ classiﬁ-

cation agrees with the one made by an expert human

inspector.

6 CONCLUDING REMARKS

We have been involved in the implementation of a

machine vision system to improve the repair coating

quality control of the easy open can end manufactur-

ing process. The system has the following properties:

• End classiﬁcation in agreement with the one made

by an expert human inspector.

• Flexibility to be able to modify the rejection thresh-

old.

• Interpretability supplied to the operators in order to

ﬁnd out the failure causes and reduce mean time to

repair (MTTR) during failures.

• Total inspection of 100% end production.

In spite of the fact that the end repair coating process

of only one end format (1/4 Club) has been studied, as

this process is common to all formats, it is reasonable

to think that fuzzy models like the found model can

be obtained to make the A/R decision for another end

format.

All this leads to the conclusion that is possible

to design an inline, automated machine vision sys-

tem, which only extracting the ARCQ from each end,

makes a right A/R decision. ANFIS, the neuro-fuzzy

modeling technique used to optimize the fuzzy model,

provided excellent prediction accuracy.

In the future we will study the existence of mod-

els that estimate the easy open can end repair coat-

ing process failure causes. Furthermore, we will re-

search the application of coevolutionary genetic fuzzy

modeling techniques that improve the interpretability

without a signiﬁcant loss of accuracy.

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