Glue Level Estimation through Automatic Visual Inspection in PCB

Manufacturing

Bruno P. Iglesias

, Mario Otani

and Felipe G. Oliveira

Institute of Exact Sciences and Technology (ICET), Federal University of Amazonas (UFAM),

Itacoatiara, Amazonas, Brazil

Cal-Comp, Institute of Research and Technological Innovation (ICCT), Manaus, Amazonas, Brazil

Keywords:

Glue Level Control, Automatic Visual Inspection, Machine Learning.

Abstract:

Nowadays, the increasing use of automatic visual inspection approaches in the manufacturing process is re-

markable. The automation of production lines implies proﬁtability and product quality. Moreover, optimized

human resources result in process optimization and production increase. This work addresses the problem of

optimizing the glue tube replacement in Printed Circuit Boards (PCB) manufacturing, warning a human oper-

ator only just in time to replace the glue tube. We propose an approach to estimate the glue level, in the glue

injection process, during PCB manufacturing. The proposed methodology is composed of three main steps: i)

Pre-Processing; ii) Feature extraction; and iii) Glue level estimation through machine learning. The proposed

predictive model learns the relation between visual features and the glue level in the tube. Real and simulated

experiments were carried out to validate the proposed approach. Results show the obtained Root Mean Square

Error (RMSE) measure of 0.88, using Random Forest regression model. Furthermore, the proposed approach

presents high accuracy even regarding noisy images, resulting in RMSE measures of 3.64 and 4.15 for a Salt

and Pepper and Gaussian noise, respectively. Experiments demonstrate reliability and robustness, optimizing

the manufacturing.

1 INTRODUCTION

In the current economy, manufacturing companies

must be competitive. The mentioned competitiveness

depends on increasing production, providing quality

to the manufacturing processes. In this context, the in-

spection process plays a fundamental role in the qual-

ity of industrial tasks (Rahman et al., 2019).

The automation of visual inspection approaches

has grown consistently in recent decades, with partic-

ularly relevant challenges being posed in different in-

dustrial scenarios and manufacturing processes (Ab-

dul Rahman et al., 2018).

In some well-stated production lines, it is not al-

ways possible to include internal sensors in enclosed

devices, like injection glue tubes. Thereby, automatic

visual inspection approaches are efﬁcient and reason-

able to address the refered problems. Automatic vi-

sual inspection can be applied to: i) waste reduction;

ii) quality of ﬁnal products improvement; or iii) pro-

cess optimization (Thielen et al., 2020).

In this paper, we present an approach to optimize

the glue tube replacement, during the glue injection

process, in manufacturing of Printed Circuit Board

(PCB). The glue level is estimated from visual fea-

tures, warning the human operator within the appro-

priate time to replace empty glue tubes. We also intro-

duce a liquid level estimation strategy based on Ran-

dom Forest (RF), to obtain a more efﬁcient estimation

of glue levels. Experiments in real-world scenarios

and simulations show that the obtained results are ac-

curate and applicable in industrial scenarios.

Our main contribution is to provide an approach

to estimate the glue level in an autonomous glue in-

jection process. Furthermore, the proposed predictive

model learns the relation between visual features and

the semantic level. Thereby, the human operator is al-

located to glue tube replacement only at the appropri-

ate time, optimizing the human resource during man-

ufacturing of PCB. Figure 1 presents a 3D model of

an experimental setup used for automatic visual in-

spection, regarding a glue injection process in PCB

production line.

The remainder of this paper is structured as fol-

lows. In Section II we present a brief discussion on

related works regarding automatic visual inspection.

Iglesias, B., Otani, M. and Oliveira, F.

Glue Level Estimation through Automatic Visual Inspection in PCB Manufacturing.

DOI: 10.5220/0010540807310738

In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2021), pages 731-738

ISBN: 978-989-758-522-7

731

Figure 1: Three-dimensional model of an experimental

setup used for automatic visual inspection for glue level

control in PCB manufacturing.

The proposed methodology is presented in Section III

and validated by real and simulated experiments dis-

cussed in Section IV. Finally, in Section V we draw

the conclusions and discuss paths for future investi-

gation.

2 RELATED WORKS

Problems related to automatic visual inspection are

of signiﬁcant importance and have been the subject

of intensive investigation (Zhang et al., 2018) (Rah-

man et al., 2019). For fault detection, the majority of

works perform classiﬁcation of presence or absence

of faults, in manufacturing processes (Abdul Rahman

et al., 2018) (Vafeiadis et al., 2018).

Many industrial applications are addressed as a

fault detection approach. In (Rocha et al., 2016), a

visual inspection approach is presented to detect ab-

sence/presence of surface mount components (SMC)

on printed circuit boards (PCB). The authors propose

a methodology based on the combination of Machine

Vision and Machine Learning (using Support Vector

Machine (SVM)) to detect component absence, with

more quality and precision, using noisy digital images

acquired directly from PCB industrial production line.

The obtained results demonstrated the robustness of

the methodology, obtaining 97.25% of accuracy.

(Zhou et al., 2017) has proposed a detection ap-

proach for oil-air and oil-water interfaces, from im-

ages of transparent tubes containing water and oil. A

statistical based approach to detect the mentioned in-

terfaces is used. Through real experiments, results

show that the multi-interface detection method has

high precision and reaches the requirements of indus-

trial applications.

In some industrial applications, liquid control is a

paramount task. (Zhang et al., 2018) presents a ma-

chine vision approach for liquid particle inspection in

pharmaceutical injection. The authors proposed an

approach based on particles segmentation, tracking

and classiﬁcation, to reduce potential defects in injec-

tion process. From the experiments, the proposed in-

spection approach demonstrates effectiveness in real

application, achieving accuracy above 97%.

The authors of (Abdul Rahman et al., 2018) pro-

posed a strategy to classify bottles regarding shape

and level. In shape analysis the bottles are classiﬁed

in ”good” or ”defect” classes. Meanwhile, in level

analysis the liquid is classiﬁed in ”overﬁlled”, ”under-

ﬁlled” and ”good” classes. For this, a local standard

deviation and Hough transform technique are used,

using a decision tree as a learning model. The experi-

mental results obtained from the developed technique

show an accuracy of 97% for shape and 93% for level

analysis.

In (Saad et al., 2017) is presented a real-time in-

spection approach for beverages product. The pro-

posed system regards color concentration and bottle’s

water level. Quadratic distance technique is applied

for color concentration analysis based on a combina-

tion of Red, Green and Blue (RGB) histogram. The

vertical and horizontal coordinates are used to inspect

three conditions of the level, which are ”overﬁll”,

”underﬁll” and ”passed”. The proposed system has

achieved 100% accuracy using 246 samples.

In mounting processes, glue injection and level

control are important steps for efﬁcient manufactur-

ing. In (Vafeiadis et al., 2018) the authors proposed

the inspection of dies attachment on PCB, using ma-

chine learning techniques. For this, an analysis is per-

formed regarding the excess or insufﬁcient glue injec-

tion. For the inspection of PCB, a pixel-based vector

of the regions of interest is used, altogether with a

SVM classiﬁer. The experiments achieved an accu-

racy of 81.39%.

The existing solutions for liquid level control are

based on the classiﬁcation of the liquid level into

”overﬁll”, ”underﬁll” and ”good” classes (Saad et al.,

2017) (Abdul Rahman et al., 2018). The closest ap-

proach, regarding glue control, only inspect the ex-

cess or insufﬁcient level of injected glue on PCB

(Vafeiadis et al., 2018). Thereby, although previous

works on automatic liquid level control approaches

exist, to the best of our knowledge, there is no prior

work tackling speciﬁcally the automatic glue level

control in PCB manufacturing problem.

The presented approach is particularly interesting

because it proposes an automatic glue level control

for PCB production. Additionally, it predicts the con-

tinuous level representation during the glue injection

process. In this sense, it is possible to verify the

ICINCO 2021 - 18th International Conference on Informatics in Control, Automation and Robotics

732

Color space

changing

Binarization

Morphological

operation

Pre-Processing

Feature extraction

Contour

extraction

Glue area

estimation

Glue level

prediction

Machine Learning

Image

Figure 2: Overview of the proposed approach for automatic visual inspection for glue level control, through the stages: i)

Pre-Processing; ii) Feature extraction; and iii) Glue level prediction through machine learning.

percentage of glue into the tube during the injection

process, warning the human operator, to replace the

empty tube, only in proper time. Moreover, it pro-

vides important information for manufacturing man-

agement.

3 METHODOLOGY

In this paper, we propose an automatic visual in-

spection for glue level control process, based on vi-

sual features and machine learning combining. An

overview of the proposed methodology is shown in

Figure 2, whose details will be presented in the next

subsections.

In Figure 2 we present an overview of the

proposed approach, highlighting the main steps to

achieve the glue level estimation, indicating the cor-

rect time to replace the empty glue tube. To reach

this goal, images are acquired continuously and a pre-

processing stage is performed. Shape based features

are extracted to quantify the glue level into the tube.

Finally, a machine learning technique is applied to

predict the glue level.

Our problem can be summarized as follows:

Problem 1 (automatic visual inspection). Let

{

,...,i

}

be a series of glue tube images provided

by a camera. Also let

{

,...,l

}

be a series

of previously known reference glue levels. Our main

goal is to correctly associate an unknown image (

)

to the correspondent glue level (

), representing the

glue level in the tube.

3.1 Pre-processing

3.1.1 Color Space Changing

In applications involving structured environment and

predeﬁned objects and lighting conditions, color in-

formation is used as a feature to represent patterns.

In multi-band images, each layer represents a spec-

trum, corresponding to a complementary information

to multi-band image combining. In this sense, indi-

vidual layers are frequently used to ﬁlter patterns in

learning processes.

In this work the images (

I ) are initially acquired

in Red, Green and Blue (RGB) color space and after

the images are converted to L*U*V* color space. In

L*U*V* color space images, the colors are based on

Tristimulus value (L) and Chromaticity (U and V) co-

ordinates, where L component has the range [0,100],

U component has the range [-134,220], and V compo-

nent has the range [-140,122] (Remmach et al., 2020).

For glue level estimation, the V component is used, as

monochromatic image (

), due to sensitivity to pat-

terns in glue tube images. In Figure 3 are presented

examples of raw RGB and V component images.

(a) (b)

Figure 3: Color space changing process. Figures 3a and 3b

correspond to RGB and V component images, respectively.

Glue Level Estimation through Automatic Visual Inspection in PCB Manufacturing

733

3.1.2 Binarization

Binarization is a technique used to transform a gray

level image (

) into a binary image (

). Binariza-

tion consists of using a threshold value to separate

pixels into two classes, the background (black pixels)

and foreground (white pixels). The quality of a bina-

rization method relies on ﬁnding an efﬁcient threshold

value.

Otsu binarization function is a global method

used to ﬁnd a threshold value based on maximizing

the interclass variance of background and foreground

(Otsu, 1979) (Gonzalez and Woods, 2017). The Otsu

threshold value (T) is computed as folows:

M × N

. (1)

M × N

. (2)

+ N

= M × N. (3)

+ ω

= 1. (4)

µ = µ

× ω

+ µ

× ω

. (5)

Where, M and N are the amount of image rows

and columns, respectively. N

and N

are the amount

of pixels that are greater than and less than the thresh-

old (T ), respectively. ω

and ω

are the proportion of

pixels. µ

and µ

are the average gray values of pix-

els in background and foreground. µ is the average

gray value of all pixels. A simpliﬁed equation for the

interclass variance (g) is given by:

g = ω

× ω

× (µ

− µ

)

. (6)

Thereby, to ﬁnd the threshold value which maxi-

mizes the interclass variance between background and

foreground, all gray values must be evaluated. The in-

terclass variance assessment is performed as follows:

max

= arg(max(g(T ))). (7)

Otsu binarization method is used in this work be-

cause it can efﬁciently separate background and fore-

ground in structured environments with predeﬁned

objects. Moreover, Otsu method can reduce noise

very well.

3.1.3 Morphological Operation

Morphological operations are nonlinear functions that

process images based on shape and are especially

suited to the processing of binary images. Morpho-

logical image processing removes imperfections tak-

ing into account the form and structure of the image.

Morphological operations probe an image with a

matrix called structuring element. For this, the struc-

turing element is positioned in all locations in the

image, being compared with the current position al-

together within its neighborhood. Many image pro-

cessing functions are based on the mentioned strategy,

such as: i) erosion; ii) dilation; iii) opening; and iv)

closing.

Erosion () shrinks an image by stripping away a

layer of pixels from both the inner and outer bound-

aries of regions. Dilation (⊕) adds a layer of pixels to

both the inner and outer boundaries of regions. Open-

ing (#) opens up a gap between objects connected

by a thin bridge of pixels. Moreover, any regions that

have survived the erosion are restored to their original

size by the dilation. Closing ( ) ﬁlls holes in the re-

gions while keeping the initial region sizes (Gonzalez

and Woods, 2017).

In this work, we use the opening morphological

operation due to removing small regions and small

connections, as can be observed in Figure 4. Further-

more, the glue tube size is preserved during image

processing. Additionally, a rectangular structuring el-

ement is used, with a size of 5 ×5. The opening func-

tion is represented as follows:

A # B = (A  B) ⊕ B. (8)

Figure 4: Glue tube image after Otsu binarization and open-

ing morphological operation stages.

3.2 Feature Extraction

3.2.1 Contour Extraction

Contour corresponds to a line connecting all the

points along a boundary, representing an object’s

shape. The technique used in this work to extract

the contour from binary images performs the border

following for topological analysis. The referred tech-

nique is used due to its efﬁciency in shape representa-

tion.

ICINCO 2021 - 18th International Conference on Informatics in Control, Automation and Robotics

734

In the mentioned technique, ﬁrst, a pixel that satis-

ﬁes the border conditions is deﬁned as a starting point.

After, into an auxiliary structure, i) a uniquely iden-

tiﬁable number is assigned to the starting point, ii) a

sequential number of the border is also assigned to

the newly found border, and iii) the parent border is

assigned to the newly found border. A new pixel is as-

sessed, and if the border conditions are satisﬁed, the

new pixel is added to the auxiliary structure as a new

border component. In the end, only the binary image

contour remains in the output image (

C ), represent-

ing the shape of objects (Suzuki and be, 1985). Figure

5 presents the glue contour, extracted from raw glue

tube image, representing a shape feature.

Figure 5: Glue contour extracted from glue tube image.

3.2.2 Glue Area Estimation

After the image pre-processing stage, ﬁltering the

most relevant features and reducing noise, and after

contour extraction stage, ﬁltering only the remaining

glue within the tube, in the current stage the glue area

on image is quantiﬁed.

Regarding the rectangular glue shape, veriﬁed in

frontal image of the glue tube, we compute the rectan-

gle which completely covers the glue with minimum

area, representing the glue area on image. In this way,

it is possible to compute the rectangle area (

G ), from

obtained width and height, as follows:

G = width × height. (9)

Thereby, from an image acquired from a glue tube

in injection process it is possible to estimate the visual

glue area to predict the glue level in unknown scenar-

ios. The estimated glue area corresponds to the shape

feature representation used in learning process.

3.3 Glue Level Prediction

The glue level prediction in this work is performed us-

ing the Random Forest supervised learning algorithm.

Random forest is a bagging technique, where the trees

in random forest are run in parallel. It operates by

constructing a set of decision trees at training time

and outputting the class that is the mode of the classes

(classiﬁcation) or mean prediction (regression) of the

individual trees (Breiman, 2001).

In the Random Forest algorithm, features are ran-

domly selected to build several uncorrelated decision

trees. The referred randomness implies in data rep-

resentation diversity and overﬁtting reduction, in the

training process. Additionally, the Random Forest

technique selects the features that contribute the most

to the learning process and discards those that do not.

Thus, it is possible to create more discriminative mod-

els for the regression process (Forsyth, 2019).

The prediction of random forest regression is an

average of the predictions produced by the trees in the

forest. Each tree prediction corresponds to a weighted

average of the response values (Y

,...,Y

), observed

in the original training data. The ﬁnal random forest

prediction at some observed predictor vector (X

) is a

combination of the training response values given by:

Y (X

) =

∑

i=1

, (10)

where w

),...,w

) are the non-negative

weights regarding the constraint

∑

i=1

) = 1, as

follows:

min

1≤i≤n

≤

Y (X

) ≤ max

1≤i≤n

. (11)

Random Forest regression was used in this work

due to robust and efﬁcient results in automatic visual

inspection (Thielen et al., 2020)(Dong et al., 2020).

Additionally, the predictions given by random forests

regression are always within the range of response

values in the training dataset.

4 EXPERIMENTS

In this section we present experimental results

and compare the performance against existing ap-

proaches.

4.1 Experimental Setup

The glue tube is mounted on an Epson SCARA G3-

351S robot, in a PCB production line. Images from

the glue tube were collected using a Basler AcA5472-

17uc camera, mounted with a Lens TS1614 F1.4

f16mm. A Dell laptop with an Intel



Core

T M

i7-

8550U CPU and 16 GiB DDR3-2133 main memory

is used to execute the proposed approach (Figure 6).

Glue Level Estimation through Automatic Visual Inspection in PCB Manufacturing

735

Figure 6: Experimental setup used for automatic visual in-

spection for glue level control in PCB manufacturing.

4.2 Glue Level Prediction Assessment

This experiment evaluates the accuracy of the pro-

posed approach for glue level control. Glue tubes

similar to industrial glue tubes were used in these

experiments. Additionally, three different regression

models are evaluated: i) Random Forest (proposed);

ii) Polynomial; and iii) Neural Network. The com-

parison regression models were used due to good

results obtained in automatic liquid analysis (Saad

et al., 2017)(Abdul Rahman et al., 2018) and overall

automatic visual inspection context (Rahman et al.,

2019)(Thielen et al., 2020).

The regression model training is performed from

a set of input images and the testing stage regards an-

other set of input images, different from training im-

age dataset. For the training process, are used 205

images, meanwhile for the testing process, are used

3320 images.

The proposed Random Forest regression model

uses 100 trees in the forest and the minimum number

of samples required to split an internal node equals

2. The used Polynomial regression model is based on

3rd degree polynomial, represented through a cubic

spline. Meanwhile, the Multilayer Perceptron (MLP)

Neural Network regression model uses a hidden layer

of size 15, Adam optimizer and Relu activation func-

tion, for 500 epochs.

To evaluate the prediction’s quality, the RMSE

method is used to quantify how similar the known

glue levels (groundtruth) and the predicted glue lev-

els are. For RMSE analysis, the closer to zero the

result, the more accurate the predictions are. Table 1,

shows the glue level control results. Results show that

the proposed Random Forest regression model out-

performs the other regression models.

Table 1: Results for glue level prediction. This experiment

presents the RMSE for Random Forest (proposed), Polyno-

mial, and Neural Network regression models.

Random

Forest

Polynomial

Neural

Network

RMSE 0.88 0.95 1.83

(a)

(b)

(c)

Figure 7: Comparison between groundtruth and estimated

glue levels. Figure 7a represents the groundtruth and esti-

mated glue level through Random Forest regression. Fig-

ure 7b represents the groundtruth and estimated glue level

through Polynomial regression. Figure 7c represents the

groundtruth and estimated glue level through Neural Net-

work regression.

Additionally, as complementary analysis, Figure 7

ICINCO 2021 - 18th International Conference on Informatics in Control, Automation and Robotics

736

(a) (b) (c)

Figure 8: Glue tube images in injection process. Figure 8a corresponds to raw glue tube image. Figures 8b and 8c correspond

to raw image with Salt and Pepper noise and Gaussian noise, respectively.

represents the comparison between groundtruth glue

levels and the estimated glue levels, regarding the

used regression models. From Figure 7 it is possi-

ble to verify the proximity between estimated and true

glue levels. In Figure 7a we can observe the most

accurate predictions, using the proposed regression

model.

4.3 Robustness Evaluation of Glue

Level Prediction in Presence of

Noise

This experiment evaluates the robustness of the pro-

posed approach for glue level control in presence

of noise. Two different types of noise are added

in glue tube images, Salt and Pepper and Gaussian.

The three different regression models, Random For-

est (proposed), Polynomial and Neural Network, are

evaluated. In this experiment the added noise simu-

lates the image acquisition process regarding the pres-

ence of noise.

For this assessment, the regression model train-

ing is performed from a set of images without noise

and the testing stage regards another set of images

with added noise. Figure 8 represents glue tube im-

age examples. Figure 8a represents a glue tube with-

out noise. Figure 8b represents a glue tube with Salt

and Pepper noise, with 0.01 noise density. Figure 8c

represents a glue tube with Gaussian noise, with 0.01

noise density.

To evaluate the prediction’s quality, the RMSE

method is used to quantify how similar the known

glue levels and the predicted glue levels are. For

RMSE analysis, the closer to zero the result, the more

accurate the predictions are. Table 2, shows the glue

level control results for different types of noise and

different regression models. Results show that the

proposed Random Forest regression model outper-

forms the other regression models even in presence of

noise, demonstrating the robustness of the proposed

approach.

Table 2: Results for robustness evaluation of glue level pre-

diction in presence of noise. In this experiment are pre-

sented the RMSE for Salt and Pepper and Gaussian noise.

RMSE

Noise Salt and Pepper Gaussian

Noise density 0.01 0.01

Random Forest 3.64 4.15

Polynomial 3.95 4.52

Neural Network 4.03 4.44

(a)

(b)

Figure 9: RMSE behavior in noise density variation. Fig-

ure 9a corresponds to RMSE behavior in Salt and Pepper

noise variation. Figure 9b corresponds to RMSE behavior

in Gaussian noise variation.

Additionally, the proposed glue level estimation is as-

Glue Level Estimation through Automatic Visual Inspection in PCB Manufacturing

737

sessed through the noise density variation analysis.

Thereby, the glue level estimation accuracy is eval-

uated regarding different noise densities. In Figure

9 the regression models are applied in two different

noises, Salt and Pepper and Gaussian noises, regard-

ing three noise densities, 0.001, 0.005 and 0.01. In

Figures 9a and 9b the RMSE behaviors are presented

regarding the Salt and Pepper and Gaussian noises,

respectively. From Figures 9a and 9b it is possible

to verify the proposed approach effectiveness, even in

different noise densities.

5 CONCLUSION

In this paper, we addressed the problem of automatic

visual inspection for glue level control. Unlike other

state-of-the-art approaches, our method continuously

monitors the glue level during the glue injection pro-

cess in PCB manufacturing, aggregating more infor-

mation to production process.

Real-world and simulated experiments involving

different regression models and simulated noise types

have shown that the obtained glue level predictions

are reliable and accurate considering the obtained re-

sults. Additionally, the proposed approach demon-

strates robustness, even in presence of noise during

image acquisition, and feasibility to real time indus-

trial application, once the experiments were carried

out in real time scenario.

As future work, we intend to combine different

predictive methods to improve the glue level estima-

tion accuracy. We also intend to concentrate efforts

to extend the automatic visual inspection approach to

tackle other types of problems related to PCB manu-

facturing. The volume and position control of injected

glue is also a relevant problem we intend to investi-

gate and incorporate in production lines.

ACKNOWLEDGMENT

This work was developed with support from Cal-

Comp Eletronic through R&D project in Institute of

Exact Sciences and Technology of Federal University

of Amazonas, Itacoatiara, Amazonas.

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