Proceedings of the 2017 IEEE International Conference on Applied System Innovation
IEEE-ICASI 2017 - Meen, Prior & Lam (Eds)
ISBN 978-1-5090-4897-7
1098 -
Enhance the Processing and Management Efficiency of Dental Laboratory by the
strategy of Productivity 4.0
Yu-Jie Cheng1
, Ming-Huang Chen2
, Fu-Chi Cheng3
, Keng-Pei Lin4
, Cheng-Jung Yang5
*
Shu-Zen Junior College of Medicine and Management1
, National Cheng Kung University2
, Fu Chi Dental Laboratory3
, National
Sun Yat-sen University4
, National Pingtung University of Science and Technology5
chelsea800103@gmail.com1
, dearthund1@yahoo.com.tw2
, chengfuchi@gmail.com3
, kplin@mis.nsysu.edu.tw4
,
cjyang@mail.npust.edu.tw5
Abstract
Due to advances in medicine, the societies have become
aging, which leads to the problem of missing teeth. Missing
teeth negatively affects not only external appearance but also
personal dietary habits. Therefore, it has become an important
issue in dentistry to solve the problem of missing teeth. In
Taiwan, dental prostheses for missing teeth are produced by
dental technicians in dental laboratories. The traditional
production of dental prostheses is labor consuming. But the
need for human power in dental laboratories will not be
satisfied in the future due to low human birth rate. Therefore, it
is necessary to decrease the need for human power in this area
by strategies of Productivity 4.0. In this study, we plan to
improve the managing efficiency of traditional dental
prosthesis production in dental laboratories. Firstly the
material of denture and environmental parameters of lab will
be gathered by the framework of Internet of Things (IoT).
Then data mining will be used to improve the efficiency of
inventory management. In this case, the concept of
Productivity 4.0 can be successfully applied to the production
of dental prostheses.
Key words: Internet of Things (IoT), Dental laboratory, Data
Mining, Productivity 4.0
Introduction
Aging is the main cause for tooth defects. According to the
survey by Taiwan Depart of Health, the five year accumulative
tooth loss rate in the population older than 45 years old was
35.9% and in the population older than 65 years old was 47.2%.
It indicates that the tooth loss rate increases rapidly as the age
is older, which is the most significant reason for rapid growth
in the dental market [1]. In 2012, the global value in the dental
device market reached 17.79 billion US Dollars and the mean
year compound growth rate was 6%. The dental devices can be
classified into supplies, usual dental equipment, and dental
treatment equipment. The global value of dental supplies was
around 7.94 billion US Dollars, accounting for 47.5% of
overall dental devices. Moreover, dental bridges, crowns,
artificial dental implants and dental correction were top three
products in dental supplies, accounting for 60 percent of
overall dental devices [2]. In 2012, there were more than 140
dental laboratories manufacturing 86.5 million dentures in
Taiwan. The amounts of dental laboratories and dentures have
increased recently, too. It shows a strong potential in Taiwan
dental device market.
In Taiwan, oral treatment is done by certificated dentists and
dentures are manufactured by dental technicians. In every
dental laboratory, dentists should well cooperate with dental
technicians to manufacture dentures which can perfectly fit
patients [3]. In order to make a perfectly fitted denture, the
design, manufacture, quality control, and staff part should be
all improved. In the design part, the concept of digital dentistry
has been applied. Digital images can be created by X-ray or 3D
oral scanners, and then transformed by specific soft wares. The
denture design and the occlusal simulation can be evaluated
and finally dentures are created by the tooth carving system [4].
As for other parts, the technologies should be upgraded to fit
the goals of processing automation, minimal requirement for
man power, and intelligenization.
Because currently most of the dental laboratories in Taiwan
are small-scaled and traditional, lots of man power is needed.
In the future, the lack of man power will be a big problem.
Therefore, technology improvement as mentioned above is
necessary. “Industry 4.0” proposed in Germany can be a good
example for technology improvement. Simply speaking, the
strategies of “Industry 4.0” emphasize to establish an
intelligence factory with accessibility, resource effectiveness,
and human factor engineering. This factory can use internet
and intelligence integrated sensing technologies to cooperate
with upstream and downstream companies and then provides
sufficient post-sales services. Moreover, big data analysis can
be further integrated to satisfy the needs of clients and to
reduce the waste in cost [5]. This operation concept of the
factory also fits the policies of “Productivity 4.0” proposed by
current Taiwan government [6].
Many research discussed about applying “Productivity 4.0”
or “Industry 4.0” in Taiwan, but most of them only discussed
about potential benefits and impacts over business
management or production lines after assisting companies to
apply this policy [7-10]. Only some studies described the
effects after real execution. Lee [11] has developed both
automatic grinding wheel dressing and intelligent grinding
monitoring system for slanting cylindrical grinders. The
processing parameters and results could be saved as managed
by MySQL and transit to Clouds by PHP webpages. A
corresponding mobile app was also set up. Chen [12] has
designed a HEXA robot which can be controlled by the
man-machine interface. The parameters of this robot can be
sent to Internet by the network communication module. Wang

Proceedings of the 2017 IEEE International Conference on Applied System Innovation
IEEE-ICASI 2017 - Meen, Prior & Lam (Eds)
ISBN 978-1-5090-4897-7 - 1099
[13] has designed an automated production line by integrating
mechanical and electrical equipment with HIWIN industrial
robots, programmable logic controllers, Da modules, stepping
motor conveyor belts, mechanism design, and man-machine
interface. All these interventions fit the concept of “Industry
4.0”. However, the application of “Productivity 4.0” in dental
industry has not yet published. Therefore, how to apply
“Productivity 4.0” in dental laboratories will be evaluated in
this study.
In this study, an advanced inventory management in Fu-Chi
Dental Laboratory will be developed. Initially the amount of
materials and the values from temperature/humidity sensing
modes will be collected by Internet of things (IoT) and
wireless technology. Then RFID technology and the expert
system will be utilized to improve the inventory management
efficiency and to help users to make decisions in material
preparation. In the end, the goal of automation and
intelligenization will be achieved in the dental laboratories.
Methodology
A. The system framework
The whole system framework can be divided into 2 parts: the
framework of material inventory management and the
framework of sensing node. Both 2 parts use Wi-Fi for the
communication network and are flexible to expand extra nodes
in cases if the users want to increase network nodes later on.
There are 3 layers in the framework of material inventory
management is illustrated in Figure 2, including the layer of
database, layer of application and layer of hardware. The layer
of database contains the knowledge base and the database. The
rules of material preparation from the expert knowledge are
saved in the knowledge base. And the database is a kind of
relational database. The data of the current inventory amount,
normal inventory amount, and minimally required inventory
amount of Cocr disks in different sizes as well as the
well-defined objects and the selected values are all saved in the
database. Data saving and modification are done by mapping
the dataset category in the database to the memory block to
reduce the burden of system accessing and to increase the
flexibility of system processing under the offline status of the
database. The layer of application is developed under
Framework 4.5. Initially a reasoning engine block is planned
for accessing knowledge rules, and then new facts are created
by matching facts with the use forward link method. Decisions
will be made after the new facts excite the rules. On the other
hand, the inventory data from the hardware layer are transit via
the Wi-Fi interface, saved in the data buffer, and then uploaded
to the program core every 100ms. The program core then
processes both the inventory data from the hardware layer and
the results of the forward link calculation. Finally the
inventory access mechanism manage with methods and
properties provided by the dataset category, which meets the
properties of flexible expansion and program code reuse. In
the hardware layer, supporting by the DC 12V transformer, the
automated material management is done by combining
Arduino UNO control board and the high frequency RFID.
The data are transit by the Wi-Fi module.
In the framework of sensing node, Arduino UNO control
panel is the core which connects modules of temperature and
humidity. After controller processing, the retrieved data will
be packed by POST method and then transit via Wi-Fi to
ThingSpeak website for saving and presenting the inventory
environmental information.
Fig. 2 the framework of material inventory management
B. The process of expert system
The inference engine is the main unit in the system which
plays as the brain of the virtual agent. On the one hand, after
retrieving the material preparation rules in the knowledge base
and the facts in the database, the normal and minimal inventory
amount will be obtained by sequential fact matching and rule
excitation with the forward reasoning method. On the other
hand, every cycle in the inference process and the new facts
will become the interpretation tool for verifying the inference
results. The reasoning and the decision making of the virtual
agent will be completed and finally the users or experts can
access the material preparation information which assists in
decision making via the user interface. This system uses
CLIPS expert system software to verify all process of rule
excitation and face matching as well as to make sure that the
expert system can be integrated into the material inventory
management system. In the end, the goal of decision support
can be achieved.
C. The hardware development
(1)The hardware framework of material inventory
management
The hardware framework of material inventory management
is illustrated in Figure 3. The Arduino UNO controller panel,
the core of the framework, communicates with the 13.56MHZ
high frequency (HF) RFID reader by the SPI protocol. When
the Cocr discs are transported in or out of the storage room for
CNC manufacturing, the HF tag on each Cocr disc will be
scanned by the HF RFID reader. The HF tag is a passive tag.
When the tag receives the signals from the HF RFID reader,
the ID of the tag will be read by the HF RFID reader and then
transit to the controller panel to be saved. The controller will
then activate the buzzer by transmitting a high electric
potential signal. In addition, the controller will transmit the ID
of the tag which has been scanned to the Wi-Fi module with
RS232 communication agreement every 500ms. The Wi-Fi
module can work under AP or STA setting. When the Wi-Fi

Proceedings of the 2017 IEEE International Conference on Applied System Innovation
IEEE-ICASI 2017 - Meen, Prior & Lam (Eds)
ISBN 978-1-5090-4897-7
1100 -
module receives the data, the data will be packed and further
transmit to the AP mode wireless router by the ceramic antenna
and then saved to the computer database via the TCP-IP
network. In addition, the app of man-machine interface in the
computer is written by Visual C++. The database is developed
by the SQL Server 2014 Express.
Wi-Fi Module
Arduino UNO Controller
HF RFID Reader
Buzzer
ESP8266
802.1 b/g/n
ATMEGA
328P
RS232
Software
Protocal
PCB
Antenna
MFRC522
Chip
(13.56MHz)
User
HF Tag
SPI
Self Excited
Buzzer
RS232
Ceramics
Antenna
Wireless
Router
TCP/IP
Database
User
Cocr
Fig. 3 The hardware framework of material inventory management
(2) The hardware framework of sensing node
The hardware framework of sensing node is shown in Figure
4, which is modified from the hardware framework of material
inventory management by adjusting the input and output unit.
The input unit contains a DHT 22 temperature/humidity sensor,
which can detect the environmental parameters of the storage
room. These parameters will be processed into valuable
information by the Arduino microcontroller and then transmit
via Wi-Fi to the output unit, in which the ThingSpeak Cloud
database is the core. The users can get the curves and the status
of temperature and humidity in the storage room by mobile
devices. The system will also alarm if the environmental
parameters exceed the safety threshold.
Fig. 4 The framework of Sensing node
Verification of the System
A. Practice and testing
The testing has been done in Fu-Ji Dental Laboratory. After
testing, the working sensing distance of the RFID reader was
around 2~3 cm. The scan and record of the inbound and
outbound work could be completed within 2 seconds. In
Figure 5, the computer interface of the material inventory
system was shown. Information about the single
inbound/outbound and the whole Cocr disc inventory,
including sizes, current inventory amount, normal inventory
amount, and minimal (safety) inventory amount, was all
displayed.
Fig. 5 The computer interface of material inventory system
The testing site of temperature and humidity, shown in
Figure 10, was located next to the Cocr disc storage place in
the storage room. The upper limit of temperature was set as 25
℃ and humidity as 65% RH. The system would alarm if the
detected temperature or humidity exceeded the upper limit. In
addition, the values of temperature and humidity displayed on
the ThingSpeak website were shown in Figure 6. In both
temperature and humidity curves, 200 sets of values were
displayed, which were recorded every 20s. The temperature
varied a little due to the human operation inside the room but
still maintained around 24 ℃ . Besides, the humidity
maintained around 40% RH. Since both values were lower
than the upper limit, the value was always 0 (normal status),
not 1 (abnormal status), in both temperature and humidity
alarm curves on the right side.
Fig. 6 The temperature and humidity curves and alarm curves on
ThingSpeak website
B. The experimental results of expert system
After discussion with experts and dental technicians, 26
rules have been set up in the rule base of expert system. The
system then updates the normal inventory amount and
undergoes the warning processes in minimal (safety) inventory
amount by these rules.
In rule 1~6, the minimal (safety) inventory amount is set
according to the presence of orders or not. In rule 7~12, the
warning sign will be sent to users for further material
preparation if the actual inventory amount is smaller than the
minimal (safety) inventory amount. In rule 13~18, a new
normal inventory amount will be calculated by the number of
ordered dentures divided by the base number, rounding it to
the nearest whole number, and finally adding the normal
inventory amount. In rule 19~24, the comparison between the
actual inventory amount and normal inventory amount is done.
If the actual inventory amount is smaller, warning signals will

Proceedings of the 2017 IEEE International Conference on Applied System Innovation
IEEE-ICASI 2017 - Meen, Prior & Lam (Eds)
ISBN 978-1-5090-4897-7 - 1101
be sent to users for further material preparation. In rule 25, the
amount of processed dentures scanned at site B is added to the
historical data in the database and then a new maximal number
of dentures which can be processed in the new single disc will
be calculated. In rule 26, the new maximal number to be
processed is compared with the old one. If these two numbers
are not equal, the new number will replace the old one and the
corresponding base number of discs in different sizes will be
updated.
The experimental results were shown in Figure 9 and the
data were presented in Table 1. In Table 1, the minimal (safety)
inventory amount was the minimally required inventory
amount. In addition, the normal inventory amount was the
number of orders divided by the base number, then rounded to
the nearest whole number, and finally added to the minimal
(safety) inventory amount. The current inventory amounts
shown in Table 1 were sufficient for all denture-processing
orders. As long as the normal inventory amount is maintained,
materials will be prepared in time and the loss due to human
work will be reduced in the dental laboratory.
Conclusion and Future Work
The material inventory management system developed in
this study has the advantages of low cost and expansion
flexibility. By testing results, this system is proved to
effectively reduce the processing time in the dental laboratory.
In addition, the normal and minimal inventory amount can be
real-time calculated by the expert system, which combines the
inbound, outbound, and CNC manufacturing amount and the
rules based on the expert knowledge. Therefore, the manual
work loss due to insufficient material amount or forgetfulness
of material preparation can be effectively reduced, which
achieves the goal of assisting in support and decision making.
Moreover, the changes in the environmental parameters of the
storage room are monitored by the Cloud platform to increase
the stability of inventory materials. A comparison of system
pre-implantation and post-implantation is shown in Table 1.
According to the test results, this system fits the goal of
industrial upgrade from the traditional dental laboratory and
the framework of Industry 4.0.
In the future, this system will be improved further in 2 ways
to increase the system availability. On the one hand, more
different materials, besides the Cocr discs, will be managed in
this system to fit the inventory property of small amount and
variety in the dental laboratory. On the other hand, although
there are preliminary results in predicting inventory amount by
the expert system, the loading time from the rule base increases
significantly when the material inventory amount increases.
Therefore, the system efficiency will be improved by using the
backward link method instead.
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