=Paper=
{{Paper
|id=Vol-3214/WS5Paper3
|storemode=property
|title=A Practical Experience of AI Solution Used to Improve Varnishing Process Efficiency in Furniture Manufacturing
|pdfUrl=https://ceur-ws.org/Vol-3214/WS5Paper3.pdf
|volume=Vol-3214
|authors=Juan Del Agua,Gabriel Modia
|dblpUrl=https://dblp.org/rec/conf/iesa/AguaM22
}}
==A Practical Experience of AI Solution Used to Improve Varnishing Process Efficiency in Furniture Manufacturing==
https://ceur-ws.org/Vol-3214/WS5Paper3.pdf
A Practical Experience of AI Solution Used to Improve
Varnishing Process Efficiency in Furniture Manufacturing
Juan Del Agua 1 and Gabriel Modia 1
1
AIDIMME Metal-Processing, Furniture, Wood and Packaging Technology Institute, Benjamín Franklin Str,
13, 46980 Paterna (Valencia), Spain
Abstract
This paper shows the results of an R&D project where artificial intelligence techniques were
applied to improve the efficiency of a varnishing process for flat parts in the furniture
manufacturing sector. Specifically, a predictive model of the amount of varnish that the
machine deposits on a piece has been developed. With the use case presented, it is shown
how the datasets have been generated with the data, the type of algorithm training carried out,
and the result of the precision of the different models tested. The model based on random
forest has been the one that has shown the best calculation precision. Finally, the barriers to
the algorithm learning process suffered in the use case have been identified, relating to the
lack of interoperability between the capture systems involved.
Keywords
Artificial intelligence, machine learning, Industry 4.0, furniture manufacturing
1. Introduction
The number of research and developments in the field of Artificial Intelligence (AI) applied to
manufacturing processes has increased in recent years. Predictive maintenance or defect detection
solutions based on the application of AI techniques have been identified. However, there are still
numerous manufacturing processes where there are information gaps that cause low production
efficiency, where AI has great potential to be applied.
The furniture-manufacturing sector traditionally follows a process design known as "batch
manufacturing" where machinery adjustments are required prior to each manufacturing batch. These
adjustments directly affect production efficiency, reducing the available time of the machines and
generating a waste of defective parts processed during the adjustments.
This document shows an application scenario in a manufacturing environment of an AI solution
that allows reducing the configuration time and the waste of defective parts, in the process of
varnishing flat parts in the manufacture of furniture. The AI solution, which allows predicting the
amount of varnish applied to a piece of furniture, has been developed by AIDIMME within an R&D
project at regional level in the Valencian Community.
After the introductory section, the rest of the document is organized as follows. In chapter two, a
brief analysis of the state of the art related to the application of AI in manufacturing processes in
general and in the furniture sector in particular is made. In chapter three the use case is presented,
explaining the current problem of the varnishing process. In chapter four, the developed solution is
detailed as well as its results. Three different models (neural networks, random forest, and linear
regression) were trained. The random forest-based model obtained the best prediction accuracy both
in the training phase and in the test phase. The conclusions are collected in the last chapter of this
work, emphasizing one of the main barriers detected to adopt AI in real industrial environments:
interoperability between different information source provider systems.1
Proceedings of the Workshop of I-ESA’22, March 23–24, 2022, Valencia, Spain
EMAIL: jdelagua@aidimme.es (J. Del Agua); gmodia@aidimme.es (G. Modia)
ORCID: 0000-0003-4324-0533 (J. Del Agua); 0000-0001-6662-0569 (G. Modia)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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2. Related work
Today's manufacturing plants are forced to design production systems which combine high
flexibility (to adapt to increasingly volatile environments) with a high level of efficiency (to maintain
an adjusted cost level). To achieve this type of process where these two traditionally opposed
concepts are integrated into manufacturing systems, industries are changing towards the concept of
intelligent manufacturing. Within the concept of intelligent manufacturing, AI is one of the main
enablers [1].
The ability of AI techniques to provide predictive insights has enabled discerning complex
industrial patterns and offers a pathway for an intelligent decision support system in different
industrial tasks: inspection, predictive maintenance, process optimization, supply chain management,
and task scheduling [2]. In addition to the manufacturing processes where it has already been applied,
AI opens the possibility of designing new processes, models and ways of working that will improve
the efficiency of current systems [4].
After analyzing specific applications of AI, it has been identified that in Supply chain processes,
genetic algorithms and intelligent agents are the most used techniques for supply chain planning
processes, with an approach that incorporates uncertainty in supply [5].
In manufacturing AI methods have begun to find application in manufacturing systems for
automated visual inspections, fault detection, and maintenance [6]. Within AI, Machine Learning
(ML) techniques have been the ones that have had the greatest application so far [7].
Within the maintenance area, as a result of the SIMBA project [8], mathematical models were
obtained to predict when a machine entered abnormal working conditions, applying AI techniques.
In furniture manufacturing sector it has been identified a lack of practical experimentation of AI
methods. With the SAIN4 [9] project, an AI system was developed that made it possible to predict
whether the conditions of a manufacturing process were adequate to manufacture parts within quality
parameters or not.
One of the resulting challenges of the evolution of the intelligent manufacturing concept is the
increased need for interoperability at different levels of the manufacturing ecosystem. Successful
implementation of interoperability in smart manufacturing would, thus, result in effective
communication and error-prone data-exchange between machines, sensors, actuators, users, systems,
and platforms [9]
Regarding the concept of interoperability of systems within the furniture-manufacturing sector,
there is very little practical research. AIDIMME has participated in the EFPF project [13], where a
pilot was developed integrating vision systems, data capture through sensors and human-machine
interface (HMI), demonstrating a positive impact on the efficiency of the manufacturing process.
There are still a large number of tasks where the application of AI techniques can improve the
efficiency of the production process. For this, pilot experiences are necessary to validate both the
information systems and the data they offer, required for the learning processes. These pilot
experiences should also serve to bring out the needs for interoperability between said systems.
3. Use case definition
The use case studied in this paper refers to a project that aims to improve the efficiency of the
varnishing process for flat board pieces. This production process presents big information deficiencies
that have an impact on quality and machine availability. A perfect piece will be one to which an exact
amount of varnish has been applied which optimizes costs, while guaranteeing long-lasting aesthetics
and mechanical properties.
The varnishing operation is carried out in a type of machine where rollers are soaked with varnish,
and they are these that deposit it on the piece of board. In this type of machinery there are a series of
parameters that can be manipulated by the operator: (1) speed of the conveyor belt, (2) speed of the
applicator roller, (3) speed of the dosing roller, as well as (4) distance between rollers.
Another series of parameters are constant for a specific machine: for example the (5) hardness of
the applicator roller. Finally, there are other parameters that do not depend on the machine, but on the
coating varnish: viscosity which depends on the (6) temperature and the type of varnish (7) used. All
these parameters have been identified as influencing the amount of varnish that is finally deposited on
a piece.
Nowadays, a worker spends, in average, 30 minutes with every new order setting up the machine
parameters (1-4) in order to obtain a furniture piece with the right quantity of varnish. The only way
to check the amount of varnish applied is to weigh the piece before and after the varnishing process. It
is a manual and iterative process that is labor intensive and does not guarantee the best possible
combination of machine parameters. In addition, changes in the type of varnish or its temperature
require new modifications of these parameters to be adjusted to the target varnish weight. In addition,
with each new piece format, while these machine adjustments last, a waste of 10-15 varnished pieces
is generated that do not meet the quality standard, until the exact amount of varnish applied to the
pieces is adjusted.
With the present work, Artificial Intelligence (AI) techniques have been used to generate a model
which allows real-time prediction of the amount of varnish applied in the process. This information
saves the operator from having to manually weigh the pieces (before and after varnishing), and he will
be able to adjust the machine parameters more quickly with each new manufacturing order, as well as
detect immediately if there are variations in the target weight to be applied.
4. Implementation scenario
To carry out the learning process by applying AI, it is necessary to collect and integrate data and
information from different sources and systems. On the one hand, sensor data to be installed in the
coating machine: conveyor belt speed (1), applicator roller speed (2), dispenser roller speed (3), and
distance between rollers (4). Also, it is necessary data from a sensor in the coating tank to measure
varnish temperature (6). The parameter of the hardness of the applicator roller (5) is not taken into
account in the experiment, since only one machine is used and, therefore, it is constant. On the other
hand, information must be collected on the value to be predicted (applied varnish weight) and the type
of varnish used (7). To do this, manual information is collected on the weighing of the pieces before
(8) and after (9) being varnished. In Figure 1 green color represents data captured by sensors, red
color data captured by a worker and black color is a constant.
Figure 1: Data involved in the AI predictive model.
4.1. Data capture systems
In order to generate enough data to carry out the learning process through AI, the implementation
of two data capture systems was carried out: an automatic and real-time data capture system with
sensors (data sources 1,2,3,4 and 6) that were installed on the machine and a manual data upload
system for those measurements not able to retrieve by sensors (data sources 7, 8 and 9).
In a roller coating machine located in AIDIMME, inductive sensors were installed to obtain a
measurement of the speed of the conveyor belt, as well as the rotation speed of the dosing and
applicator rollers. A sensor was also installed to measure the distance between the two rollers, and
another sensor to measure the temperature of the varnish (Figure 2). These sensors are connected to a
PLC (Siemens S7 1200) placed near the machine that governs the data capture cycle, and dumps them
into a SQL database hosted on an industrial PC. An application was also developed to record the rest
of data that could not be collected automatically by sensors. The user interface allows the operator to
enter the type of varnish applied, the identification number of the piece being processed and the
weight of the piece before being varnished, as well as the weight of the piece after varnishing.
Figure 2: Sensors installed in the varnishing machine.
4.2. Datasets generation
First, a series of machine configurations were designed to be tested in terms of conveyor belt and
roller speeds (Table 1). For the conveyor belt, the speeds to be tested were: 6, 8, 10 and 12 m/s. The
speed of the applicator roller needs to be in sync with the conveyor belt, and therefore it was also
tested at: 6, 8, 10 and 12 m/s. The dosing roller speeds were: 0 (stopped), 2 and 4 m/s.
The distance between rollers was manually modified each day of experimentation, with all values
being within the 0-0’8 mm range. The temperature of the applied varnish varied in the range 5-45ºC,
cooling and heating previously for the tests carried out.
Table 1
Sensor conditions tested
Conveyor belt Applicator roller Dispenser roller Distance between Varnish
speed (m/s) speed (m/s) speed (m/s) rollers (mm) Temperature (ºC)
6 6 0 0 – 0’8 5 -45
8 8 2
10 10 4
12 12
Two types of varnishes (with different viscosities) were used, manufacturing 60 pieces with each
of them, with different configurations of speeds, distance between rollers, as well as varnish
temperature. The pieces were weighed before and after processing. The data from the sensors was
collected automatically by the PLC, but the data on the weight of the pieces (before and after being
processed), the type of varnish, and the piece identifier were uploaded manually by the user through
the developed interface.
4.3. AI techniques applied
The first step followed was to evaluate the machine learning approach that best fit the project. In
this case, labeled data was available for both the parameter to be predicted and the parameters to be
used in the model, so supervised learning can be applied. Also, since you are trying to predict a
numerical quantity, you should select a regression method. Some of the most common in AI include
linear regression, polynomial regression, neural networks, or support vector machines. In this project,
artificial neural networks, linear regression, and random forest regression were used.
The next step was to study the data available in the two data sets (from sensors as well as from
manual recording). A data cleanup was performed, inspecting the tables for erroneous data, duplicate
data, or outliers that occurred by mistake. Once each data set had been cleaned individually, they had
to be integrated into a single, unified data set. The rows of the two datasets were not connected one to
one, which meant that it was required to find out which rows of one dataset corresponded with those
of the other. For this task, a python script was programmed where the time field was used in both
tables, eliminating the remaining records and being able to generate a single dataset.
Once the data were merged, their normalization was carried out, that is, transforming all the data
into an interval [0,1] following a normal distribution. The data set was then divided into two parts; the
first with 70% of the data (whit 104 records) is called the Training Set, while the second contains the
remaining 30% and is called the Test Set (with 26 records). The algorithms learn from the first part,
but then their performance is checked on both sets. With this technique, you are guaranteed that the
resulting method works well not only with data that you already know, but also with new data.
To validate and compare its performance with the original data, three metrics were used: the mean
absolute error (MAE) measuring the average deviation of the original and predicted data, the root
mean square error (RMSE) measuring the same deviation but focusing on the large deviations, and the
R squared (R2) giving a measure of the similarity of both curves.
4.4. Results
For the training dataset, the predictive model which obtained the best precision indicators was the
random forest (Table 2). Similar results were obtained with a neural network, but the model based on
a linear regression model obtained results with considerably worse precision. For the dataset used as
test, all the accuracy indicators of the three trained models worsen. But the error of the model based
on random forest is still acceptable and consistent.
Table 2
Accuracy indicators of predictive models: Training and test phase
Training / Test Neural Network Random Forest Linear Regression
MAE 3,52 / 5,65 1,84 / 4,9 5,68 / 9,43
RMSE 35,02 / 75,58 10,22 / 54,99 64,4 / 262,88
R2 0,91 / 0,75 0,97 / 0,82 0,84 / 0,13
5. Conclusion
The mathematical models generated based on random forest have a high precision for the
prediction of the amount of varnish deposited on the furniture piece. With this solution, the estimated
amount of time adjusting the machine parameters to apply a specific amount of varnish is reduced
from 30 (without the AI-based solution) to 5 minutes (with AI-based solution), increasing the
availability of the machine for manufacturing activities, and therefore the efficiency of the process.
This time saving has been obtained by measuring the time taken by the user of the machine to adjust
the production to the target weight, before having the solution based on AI and after having it.
Therefore, AI has proven to be valid for its application in complex industrial processes. However,
with the experimentation carried out, a series of barriers have been identified that must be previously
resolved in order to achieve a satisfactory implementation of AI-based solutions in a real industrial
environment:
• To generate a training dataset it is necessary to install external sensors on the machine. For this,
the participation and consensus of production and maintenance workers is required, as well as a
control of the working conditions to guarantee the stability of the data generated.
• Data aggregation must be done manually, which takes extra time. This way of proceeding can
lead to human error, especially on a large dataset where even applying automation scripts could
fail with some outlier examples.
• The retraining of the models, which must be carried out with each new type of varnish or repair
on the machine that involves changes to the rollers or conveyor belt, implies carrying out the
entire process again.
The lack of interoperability between the different systems in charge of collecting the data prevents
the adoption of automated solutions to the barriers detected. With the interoperability of factory
systems, since data would be automatically collected and processed, models could be retrained at pre-
defined time intervals. Since all systems would be accessible, once the model is retrained, the AI
system could communicate with the engineering team and report on the results and performance of
the new model.
Furthermore, once the final predictive model is running, the lack of interoperability with the rest of
the company’s software dampens the positive impact of the AI-based solution. For decision making in
the factory, it is necessary to connect order information, inventory level, quality, maintenance, etc. In
this way, the AI core could be integrated with some other tools that would allow managers to access
all the data in the factory. Systems such as MES (Manufacturing Execution Systems) or Business
Analytics tools such as Power BI [14] or Tableau [15] could be potential candidates to use the result
of the predictive model as input.
6. Acknowledgements
This work was fully supported by the “Instituto Valenciano de Competititividad Empresarial”
(IVACE) through the “Proyectos de I+D en cooperación con empresas” Programme, through
VIRTUALSENSE project with grant agreement IMDEA/2020/22.
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