Design and Implementation of Painting and Replacement Automation

Robot System in Spindle Line for Smart Manufacturing

OUNGSUB KIM1,2, YOHAN HAN1,2, JONGPIL JEONG1

1Department of Smart Factory Convergence, Sungkyunkwan University,

2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419,

REPUBLIC OF KOREA

2AI Machine Vision Smart Factory Lab, Dev,

296, Sandan-ro, Danwon-gu, Ansan-si, Gyeonggi-do, 15433,

REPUBLIC OF KOREA

Abstract: In the process of painting the surface of injection products, the spray trajectory and speed are very

important and have a very important effect on productivity and quality. However, since it is very difficult to

standardize and quantify each product, it was difficult to apply automation to the painting process, so there were

many cases where people directly operated it. Recently, with the development of robot technology, many

manufacturing processes performed by humans are being replaced. To automate painting in the spindle line of

the painting process, we design Painting and Replacement Automation Robot System in Spindle Line. By

implementing it in an actual production line, we have greatly improved productivity and reduced paint

replacement time.

Key-Words: Smart Factory, Injection Molding, Painting, Articulated Robot, Spindle Line, Automation, Smart

Manufacturing.

Received: October 24, 2021. Revised: October 26, 2022. Accepted: December 14, 2022. Published: January 24, 2023.

1 Introduction

A smart factory is an intelligent factory that applies

ICT technology to all production processes, from

product planning to sales, to maximize operational

efficiencies, such as productivity improvement,

quality improvement, and cost reduction, and to

produce customized products. Quality is a very

important factor in the manufacturing industry and

has a significant impact on the growth of companies

and securing market competitiveness, [1]. In the

manufacturing industry, the painting process is a

very important part of the manufacturing process,

and especially in the plastic injection industry, the

quality of the product is very closely related to the

spraying technology in the painting process. The

painting process is a typical 3D environment, and

since most of the paints used in the process are toxic

products, it is fatal to the human body, and the

spraying and processing of paints depend on the

skilled technicians of technicians, so it is

impossible to replace human resources and highly

dependent on technology. To solve such a problem, a

painting system using a robot is being actively

researched. However, in the case of products of

complex shape or small size, it is difficult to operate

using a painting robot, and the composition of the

robot environment according to product changes

such as product color, spraying speed, and

trajectory is inefficient in the painting process using

robot automation, resulting in time and inefficiency.

It causes many constraints.

The structure of the paper is as follows. Section

2 discusses related research, the theoretical

background before introducing the system proposed

in this paper. Section 3 introduces the design of

Painting and Replacement Automation Robot

System in Spindle Line proposed in this paper.

Section 4, Experiments and Evaluation, explains the

experimental environment, experimental results,

and performance evaluation of the proposed system.

Finally, in the conclusion of Section 5, the results of

the study and future research are described. In this

paper, it is possible to implement an efficient

production environment by deriving the spraying

trajectory, controlling the amount of paint, and

applying Auto Color Change to realize optimal

painting quality using a robot. Through the proposed

system, production speed and painting automation

at the level required in actual manufacturing sites

are possible, and the research results will be used as

important data to improve process efficiency and

product quality.

2 Related Work

In this chapter, before introducing the proposed

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2023.14.1

Oungsub Kim, Yohan Han, Jongpil Jeong

E-ISSN: 2415-1513

Volume 14, 2023

system, the theoretical background and related

research are explained, and optimization and

automation for system design are described. Paint

spray trajectory modeling is an important field that

is attracting attention in the industry, and various

models are being developed and used. Since the

1990s, the process of trajectory planning, [2] and

optimization problems have been studied for

uniform paint spraying based on CAD models [3].

The method of deriving the trajectory of a product

through an intersecting plane on the product surface

is described in [4]. In the method proposed in [5], a

method of generating a spray trajectory on a 2D

surface by calculating a curve with minimized

curvature was studied. A programming-based

approach is described in [6], and [7] conducted a

study to maintain an even surface while minimizing

the waste of paint.

2.1 Smart Manufacturing

Smart manufacturing refers to digitalization,

standardization, and integration for an efficient

manufacturing environment by combining various

technologies such as the Internet of Things (IoT),

robot, automation, big data, cloud, and edge

computing, [8]. The concept of smart manufacturing

has been defined by several organizations in the US,

such as the Department of Energy (DoE) and the

National Institute of Standards and Technology

(NIST), [9], [10], [11]. In general, Intelligent

Manufacturing is sometimes used without being

distinguished from Smart Manufacturing. The

technology and essential elements of smart

manufacturing were studied to explain the

difference between smart manufacturing and

intelligent manufacturing, [9], [12], [13].

Intelligent Manufacturing refers to a

manufacturing environment that is more closely

related to technologies such as Artificial Intelligent

and Machine Learning and enables active operation.

On the other hand, there is a difference between

Smart Manufacturing and Intelligent Manufacturing

in that Smart Manufacturing focuses on data-based

operational efficiency, [14], [15].

We tried to implement Smart Manufacturing by

computerizing and standardizing the data that exists

as know-how in the manufacturing domain and then

constructing a system that controls using robots.

2.2 Optimization

Deriving the optimal spray trajectory in the painting

process is very important. Because the coating

thickness of a product has a very important effect on

quality, traditional manual methods had to rely on

the know-how of technicians. In this chapter, we

derive the optimal spraying trajectory for the

realization of the painting robot system using robots

to produce high-quality products. To derive the

spray trajectory, various variables such as the

position, direction, andspeed of the spray gun must

be considered. To optimize the spray gun according

to the product, the product is 3D scanned to derive x

and y values. The production speed and distance

between products are derived to optimize the

painting process. The algorithm for deriving the

spray trajectory is described below, [16].

Given a paint gun profile, the paint thickness on

a fiat patch is related to the paint gun velocity and

the overlap distance.

Moreover, the paint thickness is inversely

proportional to the paint gun velocity. This means:

where

q¯(x, d, v) is the paint thickness on a plane; x the

distance to the gun center; d the overlap distance; v

the

gun velocity: ρ is a function of x and d To find an

optimal velocity v and overlap distance d the mean

square error of the thickness pd deviation from the

required thickness must be minimized, i.e.,

The maximum paint thickness and minimal paint

thickness have to be optimized too because they

will determine the paint thickness deviation from

the average paint thickness.

From equations (2) and (3), we have:

The minimization of E(d,v) is only related to the

overlap distance d.

2.3 Automation

Auto spray painting can atomize paint by using

compressed air as an energy source, and it is

possible to paint by adjusting the viscosity of paint in

various patterns and a wide range.

When using an auto spray gun, the coating

should be done considering the number of overlaps

according to the shape of the pattern. In general,

WSEAS TRANSACTIONS on ELECTRONICS

DOI: 10.37394/232017.2023.14.1

Oungsub Kim, Yohan Han, Jongpil Jeong

E-ISSN: 2415-1513

Volume 14, 2023

circular patterns should be painted with 1/2 overlap,

oval patterns with 1/3 overlap, and rectangular

patterns with 1/4 overlap. In addition, the viscosity of

the paint generally uses 13 ∼ 16 sec in Primer Coat,

20 ∼ 30 sec in Top Coat. Since the viscosity of the

paint is very sensitive to temperature/humidity, the

viscosity of the paint must be kept constant through

constant temperature and humidity.

Paint is filled in the gun body, and the filled

paint is operated by a trigger signal from the robot to

make a pattern of a certain size and color the

surface to be painted.

When the air pushing the needle is discharged

through the cylinder by the trigger signal, the needle

and saddle are brought into close contact with each

other by the pressure at the rear end of the needle,

and the spray is sprayed.

The 3-Way color change is a device for

controlling the flow of paint in two directions (Inlet,

Outlet, Return). It is operated by a sol valve and

classified as N.C/N.O type. If the paint is stagnant

for a long time, mottling occurs due to hardening

and pigment precipitation. In order to prevent

hardening and mottling due to pigment

precipitation, It is necessary to maintain the fluidity

of the paint through continuous circulation or

discharge, [17].

3 Painting and Replacement

Automation Robot System in Spindle

Line

In the manufacturing industry, the painting process

is a very important part of the manufacturing

process, and especially in the plastic injection

industry, the quality of the product is very closely

related to the spraying technology in the painting

process.

A general cosmetic case painting process

consists of 12 steps, and the current status of the

painting process is as follows. Fig 1 is an

introduction to the painting process.

Fig. 1: Painting Process for Cosmetic Case

Recently, a painting system using a robot is

being actively researched for the automation of

sophisticated spraying technology. However, in the

case of products of complex shapes or small sizes, it

is difficult to optimize color, spraying speed, and

trajectory with each product change.

In this paper, we propose a painting process

automation system using a robot that derives the

spraying trajectory, controls the amount of paint,

appliesauto spray and auto color change, and meets

the production speed required by the manufacturing

site to achieve optimal painting quality.

Table 1 describes the specifications of the robot

and the main parts used in the system proposed in

this paper.

Table 1. Specifications of Main Parts

Category

Manufacturing

Company

Spec.

Robot

HYUNDAI

Degree of Freedom: 6

Auto Spary

Gun

DURR

Ecogun AS AUOT Mini

Color Change

Cartridge

DURR

10port

Gear Pump

System

DURR

3cc/1r

Electric Sol

Valve

SMC

Fig. 2 shows the painting process automation

robot concept applied to the system proposed in this

paper. For the coating of fast-moving products on

the Spindle Line, 8 spray guns and color change

cartridges capable of driving each x and y-axis can

realize optimal product coating. It moves quickly

from the top to the top to realize vertical painting,

derives the optimal spraying trajectory based on 3D,

and implements an automation process that produces

quality products.

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DOI: 10.37394/232017.2023.14.1

Oungsub Kim, Yohan Han, Jongpil Jeong

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Fig. 2: Design Concept of Painting Process

Automation Robot

Auto spray gun and 3-Way color change move

up and down using minimum paint through spray

trajectories optimized for each product, apply the

product to the surface, and apply overlapping paint

to maintain a constant coating thickness of the

product, constant temperature, and humidity It

realizes painting automation by maintaining the

best paint condition in an enclosed space. When

changing the product, the machine replaces the

color change and cartridge cleaning, which were

manually performed by people, and the replacement

and cleaning work that previously took 2 hours can

be reduced to less than 15 minutes. Fig. 3 is an

image showing the Auto spray gun and 3-Way color

change cartridge.

Fig. 3: Auto Spray Gun & 3-Way Color Change

Cartridge

4 Experiment & Evaluation

To verify and apply the system effect proposed in

this paper, an experiment was conducted with

domestic manufacturing companies. The company

produces cosmetic cases. Because the cosmetic

cases have complex shape and small size, it has been

painted only by hand. We implemented and tested the

painting process automation system using robots by

constructing robots in the cosmetic case painting

line.

4.1 Experience Environment

This company has a Spindle Line(86m) for painting

the surface of cosmetic cases. Fig. 4 shows examples

of items actually produced by this company.

Fig. 4: Cosmetic Case Image before/after Painting

Also, we had to consider the production conditions.

The production conditions are as follows.

•

Existing spraying speed: 3m/min

•

Product thickness: 75mm

•

Production: 159.6ea/min

•

Existing color replacement time: 20min

The experiment contrasted and compared with

the status of the painting process described above,

along with the daily production volume and process

defect rate of products recorded through daily

reports before the construction of the painting robot,

and Fig. 5 shows the picture after the construction

of the painting robot.

Fig. 5: Painting Process after Constructing the

Robot System

4.2 Experiment Result

To verify and apply the system effect proposed

in this paper, an experiment was conducted

with domestic manufacturing companies. Before

building the painting robot, the daily production

of products was 58,000 pieces/day, and the

process defect rate was 3.2%. After the painting

robot was built, the daily production number of

products was 67,200 pieces/day, and the process

defect rate was 2.0%, respectively 15.9%, and

37.5% respectively. showed effect. Table 2

shows the performance index before and after the

painting robot.

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Oungsub Kim, Yohan Han, Jongpil Jeong

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Table 2. KPI before/after Constructing Robot System

Key

Performance

Indicator

Before

After

Improvement rate

(%)

Production (ea/day)

58,000

67,200

15.9

Defective rate (%)

3.2

2.0

37.5

5 Conclusion

In this paper, we proposed Painting and

Replacement Automation Robot System in Spindle

Line using an articulated robot. For the experiment,

we successfully implemented a painting robot

system in the cosmetic case painting process. In

order to realize the optimal painting quality using

robots, the spraying trajectory was optimized, the

amount of paint was controlled and Auto Color

Change was applied to realize an efficient

production environment.

Through the proposed system, production speed

and painting automation at the level required in

actual manufacturing sites are possible, and the

research results will be used as important data to

improve process efficiency and product quality.

Robots that perform human-body driving functions

are expected to be needed in various industrial fields

in the future. We plan to research applications that

can disseminate and expand the technology in

various areas such as smart factories, smart cities,

and smart farms that require the role of robots.

Acknowledgment:

This work was supported by the Technology

Development Program (Project Number:

1425163235, S3261275) funded by Ministry of

SMEs and Startups(MSS, Korea)

Corresponding author: Professor Jongpil Jeong

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The authors equally contributed in the present

research, at all stages from the formulation of the

problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

Conflict of Interest

The authors have no conflicts of interest to declare

that are relevant to the content of this article.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

_US

This work was supported by the Technology

Development Program (Project Number:

1425163235, S3261275) funded by Ministry of

SMEs and Startups(MSS, Korea)

Corresponding author: Professor Jongpil Jeong