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International Conference on Changeable, Agile, Reconfigurable and Virtual Production
International Conference on Changeable, Agile, Reconfigurable and Virtual Production
AI Based Injection Molding Process for Consistent Product Quality
AI Based Injection Molding Process for Consistent Product Quality
a
b
Manufacturing Hong
Engineering
Society
International
Conference
2017, Kumar
MESICc*2017, 28-30 June
Seok Park
, Dang
Xuan Phuong
, Saurabh
a
b
2017,
VigoXuan
(Pontevedra),
Hong Seok Park
, Dang
Phuong Spain
, Saurabh Kumarc*
a,c
Department of Mechanical Engineering, University of Ulsan, 93 Daehak-ro, Namgu, Ulsan 680-749, Republic of Korea
of Mechanical
Engineering,
NhaUniversity
Trang University,
Nguyen
Dinh Chieu,
Trang
City, 650000,
Department
of Mechanical
Engineering,
of Ulsan,293
Daehak-ro,
Namgu,Nha
Ulsan
680-749,
RepublicVietnam
of Korea
b
Faculty of Mechanical Engineering, Nha Trang University, 2 Nguyen Dinh Chieu, Nha Trang City, 650000, Vietnam
a,c bFaculty
Costing models for capacity optimization in Industry 4.0: Trade-off
between used capacity and operational efficiency
Abstract
Abstract
a
a,*
b
b
Santana
, P.isAfonso
Wernke
In manufacturing processes,A.
Injection
Molding
widely used, A.
for Zanin
producing, R.
plastic
components with large lot size. So,
continuous
improvements
in product
consistency
is 4800-058
crucial
toGuimarães,
maintaining
a competitive
edge with
in thelarge
injection
molding
In manufacturing
processes,
Injectionquality
isof widely
used for
producing
plastic
components
lot size.
So,
aMolding
University
Minho,
Portugal
b consistency
industry.
Various
optimization
techniques
like
ANN, GA,89809-000
Iterative
method,
andBrazil
simulation
based are
being
usedinjection
for optimization
continuous
improvements
in product
quality
is crucialChapecó,
to maintaining
a competitive
edge
in the
molding
Unochapecó,
SC,
of
Injection
Molding
process and
obtaining
processing
still due based
to variation
during
cycles,
industry.
Various
optimization
techniques
likeoptimal
ANN, GA,
Iterativeconditions.
method, andBut
simulation
are being
used molding
for optimization
quality
failureMolding
occurs. As
manyand
constituents
process,
Material,conditions.
machine together
yields
quality.
This molding
paper is focused
of Injection
process
obtaininglike
optimal
processing
But still
due product
to variation
during
cycles,
on
Realfailure
time AI
basedAscontrol
process parameters
in Material,
injection molding
cycle. Process
and their
quality
occurs.
many of
constituents
like process,
machine together
yields parameters
product quality.
This interrelationship
paper is focused
with
quality
hascontrol
been studied
and parameters
later supposed
to be used
to generate
algorithm
for compensating
deviation of
Abstract
on Real
time failure
AI based
of process
in injection
molding
cycle. Process
parameters
and their the
interrelationship
process
parameters.
temperature
sensor
assisted
used to collect
data in real time
based on
with quality
failure Pressure
has beenand
studied
and later
supposed
to monitoring
be used to system
generateis algorithm
for compensating
theand
deviation
of
its
comparison
withPressure
the
values
interrelationship
is formed
between
parameters
plastic
process
parameters.
and temperature
sensor
assisted
monitoring
is used
to
data
in realmaterial
time
andproperties.
based on
Under
the
concept
of standard
"Industry
4.0",anproduction
processes
willsystem
be pushed
tocollect
be and
increasingly
interconnected,
Algorithm
generates
new
parameter
values
to compensate
deviation
and machine
the
same.
The entire
its comparison
withon
thea process
standard
values
interrelationship
is the
formed
between
parameters
and follows
plastic
material
properties.
information
based
real time
basis an
and,
necessarily,
much
more
efficient.
In thiscontrol
context,
capacity
optimization
process
is supposed
to beprocess
smart
automatic
after
being trained
AI and
machine
learning
using
Algorithm
generates
new
parameter
values
to compensate
thewith
deviation
and
machine
controltechniques.
follows
the Simulation
same.and
Thevalue.
entire
goes
beyond
the traditional
aimand
of capacity
maximization,
contributing
also
for
organization’s
profitability
Moldflow
software
and
real
industry
collected
data
has
been
used
for
understanding
whole
molding
process
establishing
process
is
supposed
to
be
smart
and
automatic
after
being
trained
with
AI
and
machine
learning
techniques.
Simulation
Indeed, lean management and continuous improvement approaches suggest capacity optimization insteadusing
of
relationship
betweenand
failure
parameters.
product
in real industry
is chosen
data establishing
Moldflow software
real industry
collected An
dataautomotive
has been used
for understanding
whole
moldingfor
process
maximization.
The study
of and
capacity
optimization
and costing
models
is an important
research
topic
thatacquisition,
deserves
implementation
and validation
entire
AI based system.
relationship between
failure of
and
parameters.
An automotive product in real industry is chosen for data acquisition,
contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical
implementation and validation of entire AI based system.
model
based on
different costing models (ABC and TDABC). A generic model has been
© 2019for
Thecapacity
Authors. management
Published by Elsevier
B.V.
©
2019
The
Authors.
Published
by
Elsevier
B.V.
developed
and
it
was
used
to
analyze
idle
capacity
and
to design
strategies towards the maximization of organization’s
This
is an
open
accessPublished
article under
the CC BY-NC-ND
license
(https://creativecommons.org/licenses/by-nc-nd/4.0/)
© 2019
The
Authors.
by Elsevier
B.V.
This is an
open
access article
under
the CC BY-NC-ND
license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
value.
The
trade-off
capacity
maximization
vs
operational
efficiency
is highlighted
and it isAgile,
shown
that capacity
Peer-review
under
responsibility
of the
committee
of the(https://creativecommons.org/licenses/by-nc-nd/4.0/)
International Conference
on Changeable,
Reconfigurable
This
is an open
access
article under
thescientific
CC BY-NC-ND
license
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable
optimization
might
hide
operational
inefficiency.
and
Virtual
Production.
Peer-review
under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable
and Virtual Production.
©
2017
TheProduction.
Authors. Published by Elsevier B.V.
and
Virtual
Peer-review
under
responsibility
of the scientific
of the Manufacturing
Engineering Society International Conference
Keywords: Data
Driven;
Smart manufacturing;
Machinecommittee
learning; Diagnosis;
Injection Molding
2017.
Keywords: Data Driven; Smart manufacturing; Machine learning; Diagnosis; Injection Molding
Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency
Introduction
*1.Corresponding
author. Tel.: +82-010-3073-7035; fax: +82-(0)52-259-1680.
address:author.
saurabh2313@gmail.com
* E-mail
Corresponding
Tel.: +82-010-3073-7035; fax: +82-(0)52-259-1680.
E-mail
address:
saurabh2313@gmail.com
The
cost
of idle
capacity is a fundamental information for companies and their management of extreme importance
2351-9789
2019 The Authors.
Published
by Elsevier
in
modern©production
systems.
In general,
it isB.V.
defined as unused capacity or production potential and can be measured
This
is an open
access
article
under
the CC BY-NC-ND
license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
2351-9789
©
2019
The
Authors.
Published
by
Elsevier
B.V.
in
several
ways:
tons
of
production,
available
hours
of manufacturing, etc. The management of the idle capacity
Peer-review
under
responsibility
of the scientific
committee
of the
International Conference on Changeable, Agile, Reconfigurable and Virtual
This is an open
access
article under
CC BY-NC-ND
license
(https://creativecommons.org/licenses/by-nc-nd/4.0/)
*
Paulo
Afonso.
Tel.:
+351
253
510
761;
fax:
+351
253
604
741
Production.
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual
E-mail address: psafonso@dps.uminho.pt
Production.
2351-9789
Published
by Elsevier
B.V. B.V.
2351-9789 ©©2017
2019The
TheAuthors.
Authors.
Published
by Elsevier
Peer-review
underaccess
responsibility
of the scientific
committee oflicense
the Manufacturing
Engineering Society International Conference 2017.
This is an open
article under
the CC BY-NC-ND
(https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable
and Virtual Production.
10.1016/j.promfg.2018.12.017
2
Hong Seok Park et al. / Procedia Manufacturing 28 (2019) 102–106
Hong Seok Park/ Procedia Manufacturing 00 (2019) 000–000
103
Introduction
In manufacturing processes, injection molding has been a premiere and popular technology in the production of
plastic components. With the evolution of hi-tech modern molding machines and simulation technologies, faults are
becoming increasingly infrequent [1]. Increased demand for quality products, puts an extra pressure on
manufacturers in becoming top producers of plastic components. The quality of molded products are the result of
multiple machine, material and process parameters [2]. Improper settings of process variables will produce various
defects in the final product [3,4]. As need for control of the injection molding process is high, the first step in this
case is to precisely design, measure and monitor the process to make the key process variables observable and
controllable [5]. The process of injection molding includes four main stages: plasticization, injection, cooling and
ejection. Among these four, the cooling stage takes from 50% to 80% of the cycle time [6]. Many optimization
techniques and simulation-based studies are being in use for obtaining optimal process parameters for conducting
injection molding process and obtaining product with highest quality. But still there is a question mark on their
practical utility as due to variation during molding cycles, quality failures occur. Process monitoring and control, as
well as use of variotherm technology or conformal cooling/warming channels would benefit from application of
artificial intelligence methods to function in the most optimal way [7].
The goal of this paper is to present a knowledge-based study and implementation of autonomous quality control
system for consistent quality products with injection molding. We are in the age of revolutionized information and
technology, where machines are able to generate the data recorded during production cycle and with Big data
management, we have now the capability to process the data and obtain requisite trouble shooting methods. But still
in the field of injection molding, quality and its failure troubleshooting are dependent on human interference. Real
time process parameter optimization and control without any human interference is still a distant approach due to
complexity of injection molding process and related input and output parameters. The contributions of this paper are
system model for AI based quality control, powered by Big data and its proposed implementation in real industrial
environment using real data of injection molding process for an automotive product. The paper includes state of the
art in this field, followed by research methodology, description, discussion and conclusion.
1. State of the Art
In the recent years, several approaches have been used to monitor real time process parameter values and utilize it
for developing efficient quality control system and reduce some prominent quality defects like warpage, shrinkage
and flashes. These approaches include the Model based classical control, Taguchi techniques, Artificial neural
networks (ANN), Fuzzy logic, Genetic Algorithms, Support Vector Machines, Case Based Reasoning are being in
use for process parameters optimization [8]. Adaptive process control has also been utilized for self-learning control
which utilizes data acquisition, data mining and knowledge-building models [9]. Process monitoring for injection
molding using nozzle-based pressure and temperature sensors has also been explored [10]. Apart from that many
pressure and temperature sensor assisted monitoring system are used by manufacturers to get insight of the molding
process in real time. Still there is lot of scope for the development in AI based control system as with the use of
machine learning and big data, injection molding machines can learn the operations by themselves.
2. Research Methodology
This paper describes about real time monitoring, diagnosis and autonomous quality control system. The
process flow starts with data generation from millions of injection molding cycles with the use of cavity pressure
and temperature sensors as shown in figure 1. Data generated is recorded with their identical cycle id, corresponding
process parameters, real sensor signals and corresponding product quality types as shown in the table 1. Data
collected of millions of cycles is stored in a database for further analysis and reference model generation purpose.
Collected data is used to study the interrelationship between machine input process parameters, real sensor readings
and product quality type. Based on this study a compensatory algorithm is proposed to variate the input parameters
of the machine to get desirable conditions inside the cavity, depicted by sensor readings.
Hong Seok Park et al. / Procedia Manufacturing 28 (2019) 102–106
Hong Seok Park/ Procedia Manufacturing 00 (2019) 000–000
104
Table 1: Cycle number and corresponding values
3
Fig. 1. Sensor assisted data acquisition system
After the analysis of real time sensor readings and comparing it with the standard values, system itself generates
new machine input process parameters accordingly. A conceptual flow diagram for the realization of control system
is shown in the figure 2.
Fig. 2. Flow diagram for control system
3. Description and Discussions
In real time monitoring and control system, 2 temperature and 2 pressure sensors are installed inside the cavity of
car door module. Sensors provide real time inputs about cavity temperature and pressure. Product quality depends
upon various process parameters like injection pressure, hold pressure, mold temperature, injection speed etc.
Among these parameters some are controllable directly through the injection molding machine controller and some
of them are dependent on machine input parameters. In order to have change in dependent parameters an
interrelationship is formed between plastic materials pvt behavior and machine variables. Experienced engineers of
injection molding machine can also provide the corresponding machine variable values for an uncontrollable process
parameter. In supervised learning phase of the AI based modelling these cavity temperature and pressure values are
identified based on the corresponding final product quality that what values of cavity temperature and pressure gives
product of bad quality. After analyzing it with comparison with reference, the system calculates the difference and
based on its learning algorithms it generates corresponding input machine parameters to provide required increase or
decrease in it. New process parameter implementation is followed by the machine controller. All the programming
for data acquisition, analysis, comparison and new process parameter estimation is done with the use of python. An
interface has to be generated to integrate developed control system in to the actual injection molding machine
control system. Collected data from millions of cycles is utilized to train this AI based model for its automatic
functioning and improved accuracy. The proposed quality control system if implemented successfully in an injection
molding machine will be able to provide immediate control of machine control values to compensate the fluctuation
in the operating conditions of an ongoing molding process. System architecture for implementation and validation
of this system is shown in the fig 3.
4
Hong Seok Park et al. / Procedia Manufacturing 28 (2019) 102–106
Hong Seok Park/ Procedia Manufacturing 00 (2019) 000–000
105
Fig. 3. Smart Control System
4. Conclusion
Quality consistency and stable processing conditions are a common problem for injection molding process due to
non-uniform variations in the molding machine after several cycles. The quality of final product depends on all the
parameters involved in molding process. Process monitoring can be done with the assistance of sensors and other
methods. Then relation between parameters that can and cannot be measured can be described through application
of modelling and formulas. With use of 2 temperature sensors and pressure sensors data for millions of cycles is
recorded and stored in a database to generate reference model and control algorithms. After building standard value
and establishing interrelationship between sensor signals, and machine input parameters, study is focused on real
time monitoring and control. For that purpose, a learning algorithm is setup with the mindset of generating machine
input parameters for corresponding compensation in sensor readings and hence the product quality. Despite several
existing methods of quality control, use of AI will be more beneficial than the conventional ones. Future works with
this study is to generate learning algorithms and implementation of whole AI based control system for the
manufacturing of car door with the use of use of injection moiling, and validation of whole system with real
experiments.
Acknowledgements
This work is supported by the Ministry of Trade, Industry & Energy (MOITE, Korea) under Industrial
Technology Innovation Program No. 10062677. “For plastic injection molds with 50% improved cooling efficiency,
development of 3D printing equipment & mold steel powder manufacturing technology having 30 µm grade in
diameter”.
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