The document discusses the evolution and significance of data in smart manufacturing, highlighting the role of IoT, AI, and cloud computing in enhancing manufacturing processes. It outlines the data lifecycle in manufacturing, applications of data-driven strategies, and a case study on silicon wafer production, emphasizing real-time monitoring and quality control. Additionally, it addresses security challenges and global technical standards necessary for secure smart manufacturing systems.

1. From Journal of Manufacturing Systems
Fei Tao, Qinglin Qi, Ang Liu, Andrew Kusiak (2018)
報告人：陳佑昇
2021/07/16

2. JCR
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1
For Journal of Manufacturing Systems
2020
JIF=8.633

3. Smart
Manufacturing
English Chinese
notion of a convergence 收斂的概念
hinge solely on 完全取決於
the rest of 其餘的部分
put forward 提出
apprenticeships 學徒制
Generally speaking 一般來說
the Bessemer process 貝賽麥轉爐煉鋼法
benchmarking 基準評價(測試)
scrap 廢棄
exponentially 以指數方式
Information silos 資訊煙囪(孤島)
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2
Vocabularies 1/4

4. Smart
Manufacturing
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3
Vocabularies 2/4
English Chinese
velocity 速率
veracity 真實
ambiguities 模稜兩可
latency 潛因
demographics 人口特徵
SEMs
(Small and Medium Enterprises)
中小型企業
regulators 監管機構
pass through 經歷
vibration 震動
Last but not least 最後,但同樣重要的一點
redundant 多餘的
abreast 並列

5. Smart
Manufacturing
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4
Vocabularies 3/4
English Chinese
ETL
(Extract-Transform-Load)
抽取、轉置、載入
MRO
(Maintenance,Repair,Operation )
維護、維修、運作
connotations 內涵
idling 閒置
trajectory 軌線
taking into account 考慮到
criteria norms 標準規範
AGV(Automatic Guided Vehicles) 自動導引車
shop floor scheduling 工廠作業排程
anomalous 不恰當
torque 力矩

6. Smart
Manufacturing
English Chinese
synthetizing 合成
thickness 厚度
Bayesian inference 貝氏推論
illuminated 啟發
eliminated 排除
tendency 傾向
Silicon wafer 晶圓(半導體)
crystalline 透明
photovoltaic 光電的
chamfering 刨邊
polishing 拋光
viscos 黏膠
degumming 脫膠
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Vocabularies 4/4

7. Smart
Manufacturing
Content
01
• Introduction
02
• Historical perspectives on manufacturing
data
03
• Lifecycle of manufacturing data
04
• Data-driven smart manufacturing
05
• Case study : Silicon wafer production line
06
• Conclusion and future work
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8. 1. Introduction

9. Introduction
IoT solutions
deployment of sensors in
manufacturing to collect real-
time manufacturing data
AI solutions
enable “smart” factories to
make timely decisions with
minimal human involvement
Cloud computing
enable networked data storage,
management, and off-site analysis
New IT in Smart manufacturing
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10. • New-IT in manufacturing, which
drives the development of
smart manufacturing
• Manufacturers are beginning to
recognize the strategic
importance of data
• Big data empowers companies to
adopt data-driven strategies to
become more competitive
Data lifecycle
Big data
Manufacturing data
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Smart manufacturing

11. 2. Historical perspectives on
manufacturing data

12. Historical perspectives on manufacturing data
- history of IT/MT evolution
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13. Historical perspectives on manufacturing data
Documents
DB & Information
system
Cloud &
Internet
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14. 3. Lifecycle of manufacturing data
data collection, transmission, storage,
processing, visualization, and application

15. Lifecycle of manufacturing data- Part1
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Data
transmission

16. Lifecycle of manufacturing data- Part2
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Data
storage

17. Data Source
Data
Collection
Data
Storage
Data
Analysis
Date Transfer
Data
Management
Handicraft
Age
Human experience
Manual
collection
Human
memory
Arbitrary
Verbal
communication
N/A
Machine
Age
Human and
machines
Manual
collection
Written
documents
Systematic
Written
documents
Human
operators
Information
Age
Human, machines,
information and
computer systems
Semi-
automated
collection
Databases
Conventional
algorithms
Digital files
Information
systems
Big Data
Age
Machines, product,
user, information
systems, public
data
Automated
collection
Cloud
services
Big data
algorithms
Digital files Cloud and AI
Lifecycle of manufacturing data
- V.S. in different manufacturing ages
Table 1. Comparison of manufacturing data in different manufacturing ages
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18. 4. Data-driven smart manufacturing

19. Smart
manufacturing
play a role in monitoring the
manufacturing process in real time in
order to ensure product quality
Real-time monitoring module
identify and predict emerging problems
/ enhance smooth functioning of
manufacturing processes
Problem processing module
provide the driving force for smart
manufacturing throughout the
different stages of the manufacturing
data lifecycle
Data driver module
accommodates different kinds
of manufacturing activities /
various data is collected
Manufacturing module
The connotations of data-driven smart manufacturing
- Data-driven smart manufacturing framework
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20. The connotations of data-driven smart manufacturing
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• The structured process of data
collection, integration, storage, analysis,
visualization and application is generally
applicable for a variety of different
industries
• Help decision makers understand
changes in the shortest possible time,
make accurate judgments regarding
them, and develop rapid response
measures to troubleshoot issues

21. /33
20
The connotations of data-driven smart manufacturing
- Characteristics of data-driven smart manufacturing

22. /33
21
Data-driven-smart manufacturing application
- Smart design
• Product design begins by
researching and understanding
customer demands, behaviors, and
preferences
• User-related big data improves the
capacity for manufacturers to
translate customer voices into
product features and quality
requirements
• Enables users to not only accelerate
computationally expensive tasks,
but also reduce costs

23. /33
22
Data-driven-smart manufacturing application
- Smart planning and process optimization
• Production planning is necessary to
determine the production
capacity of a manufacturing
facility
• Using intelligent optimization
algorithms to optimize
manufacturing resources and the
execution procedures for the task
• Can result in improved
productivity and product quality,
as well as reduced costs

24. /33
23
Data-driven-smart manufacturing application
- Material distribution and tracking
• Ideal scenario: the right material
should be delivered to right
equipment at the right time, so
that it can be processed through
the right operations
• Traceability of materials is
necessary to ensure that certain
types of materials
• Conducive to product quality
control and product defect
traceability

25. /33
24
Data-driven-smart manufacturing application
- Manufacturing process monitoring
• There are many factors can affect
the manufacturing process and
influence changes in product
quality
• Before the occurrence of
production abnormities, the
anomalous events often reveal
certain patterns that can be
captured by a variety of data
• Predict when production
abnormities will occur so can be
dynamically adjust production

26. /33
25
Data-driven-smart manufacturing application
- Product quality control
• Big data analytics can serve the all-
around quality monitoring, early
warning of quality defects, and
rapid diagnosis of root causes
• Factors that result in quality defects
can also be eliminated or
controlled
• Quality management can be
embedded into every step of the
manufacturing process

27. /33
26
Data-driven-smart manufacturing application
- Smart equipment maintenance
• Maintenance Data analytics can
predict the tendency for
equipment capacity to
deteriorate, the lifespan of
components, and the cause and
extent of certain faults
• Related to energy consumption
can help to uncover energy
fluctuations and abnormalities or
peaks in real time

28. 5. Case study:
Silicon wafer production line

29. Case study
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• This case describes a silicon wafer
production line. Silicon wafers are
important components of crystalline
silicon photovoltaic cells
• Through analysis of the material data,
the operator can monitor in real
time where and how the material is
being processed (RFID, Sensors)
• The vibration data can be leveraged
to characterize the operational
patterns of the multi-wire slicing
machine

30. Case study
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• There are smart meters installed in each piece of production equipment
• Manufacturers can clearly see the trends and characteristics of energy consumption in
both short term and long term, and hence make energy plans accordingly

31. 6. Conclusion and future work

32. Conclusion
Envisioning the
future of data in
manufacturing
perspective
the role of data analytics
in manufacturing was
discussed
Development perspective
the lifecycle of big manufacturing data was illustrated as a series of phases
Historical perspective
the evolution of
manufacturing data was
reflected in accordance with
four manufacturing eras
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33. Study limitations
The current data collection
technologies are not fully ready for
smart data perception
The vast majority of previous
researches mainly focused on data
collected from the physical world
instead of data from virtual models
Network unavailability, overfull bandwidth,
and unacceptable latency time, etc. that
limit its applicability for the low-
latency and real-time applications
Smart
Manufacturing
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34. Future work
Smart
Manufacturing
more conducive to data
collection and data transmission
Compatible with the
heterogeneous interfaces and
communication protocols
reduce bandwidth requirement,
latency time, and service
downtime
Using new technologies
for data storage and
processing
made more responsive,
adaptable, and predictive
Using digital twin
technologies
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35. Thank You

36. Smart
Manufacturing
Sources
1) Data-driven smart manufacturing/ Journal of Manufacturing
Systems 48 (2018) 157–169/Fei Taoa, Qinglin Qia, Ang Liub,
Andrew/ Download form Data-driven smart manufacturing –
ScienceDirect
2) PPT template, all images and graphics (shapes) in this
template are produced by allppt.com. Redistribution of the
template or the extraction graphics is completely prohibited.
3) P1. Journal Citation Reports from
https://jcr.clarivate.com/jcr/home
4) P9. P21-26. Microsoft Stock images (royalty-free images)

37. Smart manufacturing security challenges

38. • Trust –
Edge nodes that offer services to IoT
devices should be able to validate
• Shared Technology/Virtualization –
Insecure hypervisor can lead to a
single point failure and privilege
escalation attacks
• Provisioning –
Secure automatic configuration of
access credentials, node
management agents, and analytics
software
/5
1
Security challenges
Industrial Edge Computing represents an
attractive target for cyber-criminals

39. Global technical standards
/5
2
 IEC(International Electrotechnical Commission) prepares and publishes international
standards for all electrical, electronic and related technologies
• Published IEC 62443 on Industrial Network and System Security
 ETSI (European Telecommunications Standards Institute) supports the development and
testing of global technical standards for ICT-enabled systems, applications and services.
• Published ETSI TS 103 645 on Cyber security for consumer IoT
 ISO/IEC 27001 is an international standard on how to manage information security

40. Global technical standards- IEC 62443
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• General – Describes the problems and threats
of the automated industrial control system and
puts forward the Reference Model
• Policies& Procedures – How to ensure the
safety of production operations through
management Policies and process planning
• System – How to ensure that the integrated
automation solutions are protected and discusses
how to perform security assessment and other
methods
• Component – How to ensure the safety of
imported automated equipment and tools

41. 1. No universal default passwords
2. Implement a means to manage reports of vulnerabilities
3. Keep software updated
4. Securely store sensitive security parameters
5. Communicate securely
6. Minimize exposed attack surfaces
7. Ensure software integrity
Global technical standards- ETSI TS 103 645
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42. 8. Ensure that personal data is secure
9. Make systems resilient to outages
10. Examine system telemetry data
11. Make it easy for users to delete user data
12. Make installation and maintenance of devices easy
13. Validate input data
Global technical standards- ETSI TS 103 645
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5

43. Smart
Manufacturing
Sources
1) 智慧製造的資訊安全 from https://www.rockwellautomation.com/zh-
tw/company/news/magazines/security-for-smart-manufacturing.html
2) Intel IIoT Security-The Industrial Internet of Things (IIoT) from
https://www.intel.sg/content/www/xa/en/internet-of-things/industrial-
iot/security/overview.html
3) BSI Group物聯網：正視網路安全(上篇) from
https://www.bsigroup.com/localfiles/zh-tw/e-news/no196/the-internet-of-things-
get-serious-about-security-part1.pdf
4) BSI Group物聯網：正視網路安全(下篇) from
https://www.bsigroup.com/localfiles/zh-tw/e-news/no197/the-internet-of-things-
get-serious-about-security-part2.pdf
5) 歐洲電信發展協會 ETSI TS 103 645 V2.1.2 (2020-06) from TS 103 645 - V2.1.2 - CYBER;
Cyber Security for Consumer Internet of Things: Baseline Requirements (etsi.org)
6) 工控遵循IEC 62443-2-4 因應資通安全法規範 from
https://www.netadmin.com.tw/netadmin/zh-
tw/viewpoint/DB1C1183C13C4ADC8EEDF61E2DBD22B9
7) P1. Image from https://cspingenieros.com/seguridad-informatica-y-ciberseguridad/
8) P3. 工控資安標準 IEC 62443 from 工控資安標準 IEC 62443 (ntu.edu.tw)