Visual Analysis of Deep Learning Methods for Industrial Vacuum

Metalized Film Product

Thiago Moura da Rocha Bastos

1 a

, Luiz Stragevitch

2 b

and Cleber Zanchettin

1 c

Center of Informatics, Universidade Federal de Pernambuco,

Av. Prof. Moraes Rego, Cidade Universit

aria, Recife - PE, Brazil

Department of Chemical Engineering, Universidade Federal de Pernambuco,

Av. Prof. Moraes Rego, Cidade Universit

aria, Recife - PE, Brazil

Keywords:

Visualization, Clustering Analysis, Feature Extraction, Quality Analysis.

Abstract:

Extract information to support decisions in a complex environment as the industrial is not an easy task. In-

formation technologies and cyber-physical systems have provided technical possibilities to extract, store, and

process many data. In parallel, the recent advances in artiﬁcial intelligence permit the prediction and evalu-

ation of features and information. Industry 4.0 can beneﬁt from these approaches, allowing the visualization

of process, feature prediction, and model interpretation. We evaluate the use of Machine Learning (ML) to

support monitoring and quality prediction of an industrial vacuum metalization process. Therefore, we pro-

posed a semantic segmentation approach to fault identiﬁcation using images composed of optical density (OD)

values from the vacuum metalized ﬁlm process. Besides that, a deep neural network model is applied to prod-

uct classiﬁcation using the segmented OD proﬁle. The semantic segmentation allowed ﬁlm regions analysis

and coating quality associations through their class and format. The proposed classiﬁer presented 86.67% of

accuracy. The use of visualization and ML approaches permits systematical real-time process monitoring that

reduces time and material waste. Consequently, it is a promising approach for Industry 4.0 on monitoring and

maintenance support.

1 INTRODUCTION

The consistent growth of available data on manu-

facturing industries and the necessity for production

and monitoring improvements promoted a fast de-

velopment of complex systems and sensor technolo-

gies, as data-driven fault detection, diagnosis, and

soft-sensors (Fan and Wang, 2014). These tools al-

low to obtain data information from production lines,

environmental variables, machine parameters (Wuest

et al., 2016), and possibilities the use of Machine

Learning (ML) algorithms to extract knowledge and

make predictions (Fan and Wang, 2014). Further-

more, the applications of ML algorithms can support

surpass current difﬁculties on vacuum metalization

process as elevated lead-time for quality analysis and

limited inspection of the manufactured products.

Otherwise, large amounts of information in ML

models make process comprehension an arduous task.

https://orcid.org/0000-0001-8360-9396

https://orcid.org/0000-0002-8803-5106

https://orcid.org/0000-0001-6421-9747

Recent visual applications have tried to solve this is-

sue by combining information technology and human

intelligence to obtain insights from the data and sup-

port decisions under critical scenarios. Some exam-

ples for production planning (Wu et al., 2018), simu-

lation (Dutta et al., 2018), production monitoring (Xu

et al., 2017) and testing (Pajer et al., 2017) used the

association between ML and visualization tools to im-

prove industrial activities. So, we explored visualiza-

tion techniques for algorithm and product variables

interpretation to facilitate the use and comprehension

of analysts and process engineers.

Attempting to explore ML potential and construct

understandable models for a vacuum metalization

process, this work proposes applying ML visual tools

for interpretability and product classiﬁcation through

physical-properties images, reducing laboratory in-

spection and promoting production-time analysis.

The framework initiates with ﬁlm density col-

lected by 26 sensors. These values are processed and

further targeted with quality classiﬁcation from labo-

ratory analysis. Then, we used the density ﬁlm pro-

380

Bastos, T., Stragevitch, L. and Zanchettin, C.

Visual Analysis of Deep Learning Methods for Industrial Vacuum Metalized Film Product.

DOI: 10.5220/0010815400003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages

380-386

ISBN: 978-989-758-555-5; ISSN: 2184-4321

ﬁle for semantic segmentation, and after, both objects

segmented and not segmented are used to train and

validate a deep learning classiﬁer. Besides the accu-

racy metric, we evaluate the output layers to observe

sparsity and information taken from the ML model

(Fig. 1). Consequently, this work presents the follow-

ing contributions:

• Use systematic visual information through ML al-

gorithms instead of small ﬁlm samples of products

information from laboratory analysis.

• Predict product classiﬁcation, reducing the

standby time that the physical tests require.

• Indicate inferior quality products for ﬁlm inspec-

tion in production time.

• Visualize coating quality in production time, sup-

porting process monitoring and maintenance for

the industrial vacuum metallization process.

Figure 1: Illustration of the computational pipeline for in-

dustrial vacuum metallized ﬁlm, the equipment light sen-

sors measure the optical density (OD) values to represent

features. a) We applied a semantic segmentation model to

the OD object proﬁle. b) Proﬁle regions are associated by

color to observe patterns and possible faults. c) We used a

deep Neural Network (NN) in both proﬁles, the segmented

and the not segmented one, to predict the ﬁlm classiﬁcation

(as approved or disapproved). d) We evaluate the NN out-

put layers to interpret the model prediction and information

sparsity.

2 RELATED WORK

2.1 Physical Information for Industrial

Process Monitoring

This work uses industrial machine data of a vacuum

metallization process. In this process, a ﬁlm substrate,

or any oriented polymer as polypropylene (PP) and

polyethylene terephthalate (PET), is loaded in the ma-

chine in the form of a roll. It unwinds and is coated

by the aluminum evaporated on the substrate surface.

This coating process occurs through a transversely

disposed system composed of a ceramic boat heated

by resistances and an aluminum wire fed with a wire

conductor. During the coating process, the product

optical density - OD is measured for automated con-

trol actions. An in-depth description of the process

can be viewed in (Bishop, 2007; Perry and Lentz,

2009) and a process illustration in Fig. 2.

Figure 2: Vacuum Metallization process illustration. Where

the polymer ﬁlm is fed and metallized through vacuum

metal deposition.

The coating quality can appear as a higher OD at

a constant deposition rate, in the same way, that an

improved surface provides a more continuous coating

at a lower coating density. The OD of the coating

usually represents the opacity of a thin metal coating

expressed as a logarithm to base ten (1) (Bishop and

Mount, 2016).

Opacity =

Incident light

Transmitted light

Transmittance (T ) =

Transmitted light

Incident light

Opacity =

OpticalDensity = log

Opacity = log

(1)

The coating opacity expresses the light, water va-

por, and gas barrier effects obtained by metal coat-

ing. For example, a package of potato chips with an

opaque thin metal coating with OD of 1.7 achieve 49

days of shelf life, instead of the three days necessary

to turn the chips rancid by light (I.F., 1993). Thus, it

demonstrates the utility of the coating and its moni-

toring by ﬁlm OD.

2.1.1 Visual Information for Fault Identiﬁcation

The coating process by vacuum metallization occurs

through boats heated by resistance, separated (0.1

m), and disposed transversely to the web orientation.

Then, the wire metal is fed on resistance-heated boats

by conductors and evaporated to be deposited on the

web (Bishop, 2011). Following the metal deposition,

we obtained OD values in a similar position and ori-

entation, which propitiates a positional and individual

evaluation of each evaporation local system rate, and

the coating quality describes OD values or fault iden-

tiﬁcation, as pinholes, debris, and scratches (Bishop,

Visual Analysis of Deep Learning Methods for Industrial Vacuum Metalized Film Product

381

Figure 3: Deep Neural Network used to ﬁlm class prediction obtained through sequential models from Keras API. We can ob-

serve two convolutional and max-pooling layers in the model that extract information, followed by the dropout regularization

to avoid over-ﬁtting and, at last, a softmax activation function for ﬁnal classiﬁcation.

2011). So, it is intuitive to explore the potential ap-

plication of visual tools based on ML algorithms and

OD ﬁlm proﬁles to locate and identify these defects

in the web proﬁle.

2.1.2 Semantic Segmentation

Researches have shown a considerable potential of se-

mantic segmentation applications on detecting and lo-

calizing objects in images. Borovec et al. (Borovec

et al., 2017) proposed semantic segmentation to detect

and localize Drosophila egg chambers in microscopy

images. Lei et al. (Lei et al., 2020) applied deep learn-

ing and image processing to detect multiple objects on

images. Recent applications of deep Learning models

showed the capacity to detect and count plants and

extract patterns and compression of images (Akbari

et al., 2020). Thus, these applications demonstrate

the potential employment of semantic segmentation

algorithms for fault identiﬁcation on ﬁlm proﬁle im-

ages and his description using the similar local and

global context of the OD values.

2.2 Product Quality Prediction and

Interpretation

The use of a data-driven system with statistical pro-

cess monitoring (SPM) and ML models has been

ﬁgured out as a signiﬁcant research area over the

last two decades (Qin, 2012). The data-driven qual-

ity improvement provided by ML algorithms can

handle qualitative and quantitative variables in the

same framework and model batch processes and op-

timize operation proﬁles through multivariate analy-

sis (Kano and Nakagawa, 2008). ML algorithms’ ad-

vantages have pushed researchers on industrial qual-

ity monitoring processes, mainly using deep Learning

and ensembles algorithms.

2.2.1 Deep Neural Networks

Qin et al. point out as factual, kernel, or kernel-

ization methods to explore nonlinear latent relations,

as through neural networks (NN), besides, to retain

linear computational cost, turning these models also

been intensely studied for process monitoring (Qin

et al., 2020; Alcala and Qin, 2010).

Also, existing deep learning platforms provide

frameworks to construct distributed and parallel com-

puting on the graphing processing unit. This strategy

solves data struggling as speed restriction, typically

observed in modeling large-scale massive datasets as

industrial monitoring and quality prediction. For this

work, we used a convolutional neural network model

provided by Keras (Chollet et al., 2015) a Tensorﬂow

high-level application programming interface (Wong-

suphasawat et al., 2018).

We could determine the NN structure through ex-

perimentation with different multi-layer perceptrons

conﬁgurations, varying layers, neurons, and adjust-

ing dropout regularization. The Fig.3 shows the se-

quential NN model used to predict each ﬁlm product’s

correspondent class analyzed using the Keras API’s

with inputs of 65x22 images of OD values, where

the rows represent length positions and the columns

represent the OD sensors. To extract image informa-

tion and make the ﬁnal product classiﬁcation, we use

two convolutional, max-pooling 2x2, and ReLU acti-

vation function layers with 3 and 8 neurons, respec-

tively. The sequence uses a dropout of 0.5 to reduce

the overﬁtting and a ﬂattening with a dense layer of 1

neuron with a softmax activation function for the ﬁnal

ﬁlm classiﬁcation.

2.2.2 Model Interpretation

Even though most image analysis methods need to

transform multivariate processes in raw forms that

cause performance and spatial information loss, im-

age data collection and processing advances have pro-

moted miscellaneous industrial process applications

research. Recent applications have demonstrated the

capacity to solve multilayered problems propitiating

a computational cost reduction and simpliﬁcation on

features description and distribution (Liu et al., 2017).

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

382

Once semantic segmentation provides regions

contour and meaning, and the ﬁlm quality depends

directly on the uniformity and the presence of dep-

recated areas, we can analyze if the use of OD pro-

ﬁle combined with the clustering mask and regions

contour can improve the classiﬁcation analysis. Aca-

demic studies had explored pixel values as a resource

of ﬁtness, sensitivity, and information (Bach et al.,

2015; Shrikumar et al., 2017). So this work also pro-

poses comparing NN layers outputs information when

using different training images, the original OD ﬁlm

proﬁle and the OD ﬁlm proﬁle with cluster mapping

and contour made by the semantic segmentation.

3 METHOD DESCRIPTION

In this work, we evaluate the vacuum metalization

process (Materials, 2016) of an international ﬁlm

manufacturing from the TOPMET 2450 machine, us-

ing data from 26 light detectors (B1 - B26) for opti-

cal density (OD) measures, equally disposed on trans-

verse orientation to the web, more details in (Perry

and Lentz, 2009). It was collected OD values with

an interval of 2 min between each OD capture. Due

to the size product variability, the width varies be-

tween 2,5 - 4,5 m and length up to 87 km, it was used

padding to short objects up to 45 km and a crop for

over this length. The width used was from B3 to B24

sensors positions due to the proportion of objects with

valid OD values upper than 75%, as shown in the Fig-

ure 4.

Figure 4: Optical density data set description for local coat-

ing systems with his values description according to the

position of the sensors. The optical density distribution

demonstrate that some positions presents a lack of valid val-

ues that should not be considered.

Over the 32.240 rows of data, we extracted 142

objects with the respective classiﬁcation as approved

or not. For clustering, we used all of the 142 objects.

Otherwise, for class prediction, in the NN training,

we used an oversampling on the disapproved class,

turning 21 to 77 objects because of the class imbal-

ance. The lack of objects for this project was the dif-

ﬁculty of obtaining objects target class through infor-

mation crossing using experimental results obtained

in the laboratory and registered on the enterprise sys-

tem.

Initially, we process the data using TensorFlow

and an image segmentation algorithm to observe OD

distribution and area aggregation proﬁle. After that,

we applied a deep NN to classify the ﬁlm as approved

or disapproved. The code is available on GitHub

4 EXPERIMENTAL STUDY

4.1 Class Prediction and Interpretation

The performance analysis provides the best sequential

model presenting 86.67% of accuracy on the test and

an average loss of 0.46 with an inference time of 4 ms,

demonstrating, initially, the NN models’ applicability

for multilayered problems. Furthermore, the reduced

number of objects compared with the features used to

train and test the algorithm can impact the model per-

formance, needing more tests and evaluation to vali-

date the results.

4.2 Film Region Semantic Segmentation

for Cluster Analysis

The clustering analysis used image processing

through spatial regularization on super-pixels to make

segmented regions more compact. The pipeline used

comprises (i) computation of super-pixels; (ii) ex-

traction of descriptors; (iii) soft classiﬁcation, using

the Gaussian Mixture Model for unsupervised learn-

ing; (iv) ﬁnal segmentation using Graph Cut (Borovec

et al., 2017).

This work experiment used a super-pixel size of 1

and regularization of 0.1, with the number of classes

equal to four. These parameters were obtained by ex-

perimentation, observing the best OD proﬁle division

and comprehension. Fig. 6 shows approved and dis-

approved examples, where the yellow and red regions

are interpreted as deprecated areas.

Fig. 6a is an approved object and presents a sig-

niﬁcant region of class 1 (blue light), differently from

Fig. 6b, which shows a substantial part of class 2

(yellow) with class 3 (red) areas distributed inside this

class. The class 3 region format of points and edges in

different vertical positions indicates possible fault re-

gions associated with pinholes and local coating sys-

tem imbalance.

https://github.com/tmrb/Master-project-codes

Visual Analysis of Deep Learning Methods for Industrial Vacuum Metalized Film Product

383

Figure 5: After addressing a class for each OD value of a product, we sum the number of OD classes by-product, representing

area count. Dividing the objects according to his ﬁnal classiﬁcation, we could compare the area count occurrence in the

objects approved and disapproved to observe possible inﬂuences in the product’s ﬁnal quality. The blue line represents the

area counting for approved objects and the orange line for disapproved objects.

This visualization provides insightful observa-

tions. For the disapproved ﬁlm, the local coating

system on 2, 11, 13, 16, 22 positions present red as

the signiﬁcant class indicating potential local system

Figure 6: Film proﬁle semantic segmentation for fault iden-

tiﬁcation. Each region color represents a class obtained by

clustering. The marine blue is class 0 and represents the

padding area, the blue light class 1, yellow class 2, and red

as class 3. a) It is an approved ﬁlm object. b) A disapproved

ﬁlm object.

problems or imbalances in one of the process vari-

ables: wire feed, wire aligning, or boat temperature.

For the approved ﬁlm, the predominant blue re-

gion indicates a good coating and uniformity. On the

other hand, the spaced red points showed small areas

with a soft covering associated with pinholes region,

which can be caused mainly by smalls dirt or debris

in the ﬁlm proﬁle that could be avoided by vacuum

and venting variables adjustments.

Looking for objects analysis through class appear-

ance, Fig. 5 count the class pixels area for each ob-

ject. Observing that, class 3 demonstrated many high

area counts for major disapproved objects, reinforcing

the clustering analysis and the association of class 3

as undesired OD regions that contribute to object dis-

approval. For the further classes, no substantial class

counting differences were obtained between approved

and disapproved products.

4.3 Class Prediction with Semantic

Segmented Images

Another interesting evaluation is in the layer’s out-

puts, where we can observe the layer weights map-

ping and infer if the semantic segmentation could im-

prove the classiﬁcation process. Fig. 7 demonstrate

the three ﬁrst layers outputs, ﬁrst, using the original

OD values proﬁle as the object input (Fig. 7a), and

second using the images obtained through the seman-

tic segmentation application (Fig. 7b). The three ini-

tial layers correspond to the convolutional layers with

three neurons, max-pooling (2x2), and another con-

volutional with eight neurons.

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

384

Figure 7: The three initial layers output analysis of using

different types of images as input to train/test algorithm. a)

Optical Density (OD) proﬁle values of metalized ﬁlm used

as objects and the respective initial layers outputs. b) OD

proﬁle ﬁlms after semantic segmentation with four OD re-

gions classes representation used as input to train/test algo-

rithm and his respective initial layers outputs.

It was possible to observe an improvement in

layers output distribution attributed to semantic seg-

mented images as the inputs. Fig. 7a demonstrates

three empty outputs for the 5th, 7th, and 8th neuron,

while Fig. 7b present only the 8th output empty of

information. Besides that, the object contour and re-

gions mapped by the layer’s output values are more

continuous and distinguishable for inputs obtained by

the semantic segmentation, demonstrating improve-

ments in the edges capture.

In terms of performance as accuracy and loss, we

did not observe relevant differences. These metrics

presented the same median values of 86.67% for ac-

curacy and 0.46 for loss, owing to the lack of objects

used to train/test/validate the algorithms. However,

the output distribution reveals essential contributions

of the previous image segmentation, with improved

pixel-wise analysis and model sensitivity. In addition,

the pixel importance was better propagated through

the layers and represented in the ﬁnal layers,

demonstrating sensitivity enhancement to the model.

As the original OD proﬁle does not present any

difference in pixel relevance, a more distributed layer

weights proﬁle is expected, as closer to Fig. 7b.

Moreover, the weights highlighted over the algo-

rithms’ layers trained with segmented inputs demon-

strate a better sparsity of information. The reﬁning of

the importance of the features mapping reveals spe-

ciﬁc details in the shadow layers. Also, sparsity is

a crucial feature and represents efﬁciency improve-

ments by reducing resource use, like storage, com-

munication, and computation requirements.

5 CONCLUSIONS

This work demonstrated that deep learning methods,

like neural networks and semantic segmentation, can

be applied to fault identiﬁcation, not only for images

but also for physical measures spatially distributed,

propitiating semantic analysis, quality monitoring,

and better quality prediction of the product. The use

of semantic segmentation associated with image con-

struction through physical measures represents a nov-

elty for industrial applications and academic explo-

ration, contributing to many other applications be-

sides the Vacuum metalization process. The use of

more objects and tests can propitiate further applica-

tions of the deep learning model applied, using as in-

dicators for feature control, fault detection, and qual-

ity monitoring. Moreover, these algorithms can im-

prove coating in real-time, demonstrating deprecate

region identiﬁcation, promoting feature manipulation,

and guiding operator analysis.

ACKNOWLEDGEMENTS

The authors would like to thanks the Conselho Na-

cional de Desenvolvimento Cient

ıﬁco e Tecnol

ogico

- CNPq and Laborat

orio de Combust

ıveis - LAC/

UFPE for the industrial partnership incentive and sup-

port.

REFERENCES

Akbari, M., Liang, J., Han, J., and Tu, C. (2020).

Learned multi-resolution variable-rate image com-

pression with octave-based residual blocks. arXiv

preprint arXiv:2012.15463.

Alcala, C. F. and Qin, S. J. (2010). Reconstruction-based

contribution for process monitoring with kernel prin-

Visual Analysis of Deep Learning Methods for Industrial Vacuum Metalized Film Product

385

cipal component analysis. Industrial & Engineering

Chemistry Research, 49(17):7849–7857.

Bach, S., Binder, A., Montavon, G., Klauschen, F., M

uller,

K.-R., and Samek, W. (2015). On pixel-wise explana-

tions for non-linear classiﬁer decisions by layer-wise

relevance propagation. PLOS ONE, 10(7):1–46.

Bishop, C. (2011). Vacuum Deposition onto Webs, Films

and Foils. Elsevier Science.

Bishop, C. A. (2007). Vacuum deposition onto webs, ﬁlms,

and foils. Second Edition, 91(2).

Bishop, C. A. and Mount, E. M. (2016). 15 - vacuum metal-

lizing for ﬂexible packaging. In Wagner, J. R., editor,

Multilayer Flexible Packaging (Second Edition), Plas-

tics Design Library, pages 235 – 255. William Andrew

Publishing, 2nd ed. edition.

Borovec, J., Kybic, J., and Nava, R. (2017). Detection

and localization of drosophila egg chambers in mi-

croscopy images. In Wang, Q., Shi, Y., Suk, H.-I.,

and Suzuki, K., editors, Machine Learning in Medical

Imaging, pages 19–26, Cham. Springer International

Publishing.

Chollet, F. et al. (2015). Keras.

Dutta, S., Shen, H., and Chen, J. (2018). In situ predic-

tion driven feature analysis in jet engine simulations.

In 2018 IEEE Paciﬁc Visualization Symposium (Paci-

ﬁcVis), pages 66–75.

Fan, J. and Wang, Y. (2014). Fault detection and diagnosis

of non-linear non-gaussian dynamic processes using

kernel dynamic independent component analysis. In-

formation Sciences, 259:369–379.

I.F., G. (1993). Vacuum coating applications for snack food

packaging. 36th Ann. Tech. Conf. Proc. Society of Vac-

uum Coaters, pages 254—-258.

Kano, M. and Nakagawa, Y. (2008). Data-based pro-

cess monitoring, process control, and quality im-

provement: Recent developments and applications in

steel industry. Computers & Chemical Engineering,

32(1):12 – 24. Process Systems Engineering: Contri-

butions on the State-of-the-Art.

Lei, Y., Yao, X., Chen, W., Zhang, J., Mehnen, J., and Yang,

E. (2020). Multiple object detection of workpieces

based on fusion of deep learning and image process-

ing*. In 2020 International Joint Conference on Neu-

ral Networks (IJCNN), pages 1–7.

Liu, Y., Fan, Y., and Chen, J. (2017). Flame images for

oxygen content prediction of combustion systems us-

ing dbn. Energy & Fuels, 31(8):8776–8783.

Materials, A. (2016). Roll-to-roll web coating technology.

WEB Coating Products, 10.

Pajer, S., Streit, M., Torsney-Weir, T., Spechtenhauser, F.,

oller, T., and Piringer, H. (2017). Weightlifter: Vi-

sual weight space exploration for multi-criteria deci-

sion making. IEEE Transactions on Visualization and

Computer Graphics, 23(1):611–620.

Perry, M. and Lentz, R. (2009). Susceptors in microwave

packaging. Development of Packaging and Products

for Use in Microwave Ovens, pages 207–236.

Qin, S. J. (2012). Survey on data-driven industrial process

monitoring and diagnosis. Annual Reviews in Control,

36(2):220 – 234.

Qin, S. J., Dong, Y., Zhu, Q., Wang, J., and Liu, Q. (2020).

Bridging systems theory and data science: A unifying

review of dynamic latent variable analytics and pro-

cess monitoring. Annual Reviews in Control, 50:29 –

48.

Shrikumar, A., Greenside, P., and Kundaje, A. (2017).

Learning important features through propagating ac-

tivation differences. In Precup, D. and Teh, Y. W.,

editors, Proceedings of the 34th International Con-

ference on Machine Learning, volume 70 of Pro-

ceedings of Machine Learning Research, pages 3145–

3153, International Convention Centre, Sydney, Aus-

tralia. PMLR.

Wongsuphasawat, K., Smilkov, D., Wexler, J., Wilson, J.,

Man

e, D., Fritz, D., Krishnan, D., Vi

egas, F. B.,

and Wattenberg, M. (2018). Visualizing dataﬂow

graphs of deep learning models in tensorﬂow. IEEE

Transactions on Visualization and Computer Graph-

ics, 24(1):1–12.

Wu, W., Zheng, Y., Chen, K., Wang, X., and Cao, N.

(2018). A visual analytics approach for equipment

condition monitoring in smart factories of process in-

dustry. In 2018 IEEE Paciﬁc Visualization Symposium

(PaciﬁcVis), pages 140–149.

Wuest, T., Weimer, D., Irgens, C., and Thoben, K.-D.

(2016). Machine learning in manufacturing: advan-

tages, challenges, and applications. Production and

Manufacturing Research: An Open Access Journal,

4(1):3.

Xu, P., Mei, H., Ren, L., and Chen, W. (2017). Vidx: Visual

diagnostics of assembly line performance in smart fac-

tories. IEEE Transactions on Visualization and Com-

puter Graphics, 23(1):291–300.

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

386