1
00:00:04,940 --> 00:00:07,050
At the end of this topic,

2
00:00:07,050 --> 00:00:09,525
you will be able to:
Explain the digital twin,

3
00:00:09,525 --> 00:00:11,415
benefits of a digital twin,

4
00:00:11,415 --> 00:00:13,600
list the categories
of the digital twin,

5
00:00:13,600 --> 00:00:15,180
explain the advantages of having

6
00:00:15,180 --> 00:00:16,890
a digital twin in manufacturing,

7
00:00:16,890 --> 00:00:19,365
describe the digital twin
in smart manufacturing,

8
00:00:19,365 --> 00:00:21,020
list the procedure
for implementing

9
00:00:21,020 --> 00:00:23,000
a digital twin in any industry.

10
00:00:23,000 --> 00:00:25,440
First, let's discuss
the digital twin.

11
00:00:25,440 --> 00:00:28,460
In 2002, Michael Grieves
came up with the term

12
00:00:28,460 --> 00:00:30,100
digital twin to describe

13
00:00:30,100 --> 00:00:32,460
a new way to manage the
life cycle of a product.

14
00:00:32,460 --> 00:00:34,820
Because engineering,
manufacturing, and

15
00:00:34,820 --> 00:00:38,140
quality teams couldn't easily
share processes and data,

16
00:00:38,140 --> 00:00:39,600
the idea stumbled along for

17
00:00:39,600 --> 00:00:41,990
many years before Industry 4.0.

18
00:00:41,990 --> 00:00:45,020
Next, let us discuss the
digital twin approach.

19
00:00:45,020 --> 00:00:47,160
A digital twin is a virtual copy

20
00:00:47,160 --> 00:00:48,860
of an object or a system that

21
00:00:48,860 --> 00:00:51,020
stays up to date
with real-time data

22
00:00:51,020 --> 00:00:53,280
and help make decisions
through simulation,

23
00:00:53,280 --> 00:00:55,120
machine learning, and reasoning.

24
00:00:55,120 --> 00:00:56,720
Now we will be learning

25
00:00:56,720 --> 00:00:58,820
about the benefits
of a digital twin.

26
00:00:58,820 --> 00:01:01,800
Digital twins link
production and performance,

27
00:01:01,800 --> 00:01:05,320
providing valuable insights
into manufacturing processes.

28
00:01:05,320 --> 00:01:07,820
Digital twins help
manufacturers to

29
00:01:07,820 --> 00:01:09,440
accelerate digital
transformation

30
00:01:09,440 --> 00:01:10,840
and smart manufacturing.

31
00:01:10,840 --> 00:01:13,400
Digital transformation
efforts are becoming

32
00:01:13,400 --> 00:01:16,040
more and more apparent
as data integration,

33
00:01:16,040 --> 00:01:17,340
artificial intelligence,

34
00:01:17,340 --> 00:01:19,590
and the Internet of Things
continue to improve.

35
00:01:19,590 --> 00:01:23,260
Next, let us take a look at
categories of digital twins.

36
00:01:23,260 --> 00:01:25,300
Digital twins can
be divided into

37
00:01:25,300 --> 00:01:27,305
three categories: Product,

38
00:01:27,305 --> 00:01:29,340
the creation of
digital duplicates

39
00:01:29,340 --> 00:01:31,300
for use in developing
new products.

40
00:01:31,300 --> 00:01:33,180
Production, manufacturing,

41
00:01:33,180 --> 00:01:34,360
and production processes are

42
00:01:34,360 --> 00:01:36,250
validated by using
digital twins.

43
00:01:36,250 --> 00:01:39,080
Performance, data
collection analysis,

44
00:01:39,080 --> 00:01:41,315
and decision-making
using digital twins.

45
00:01:41,315 --> 00:01:42,820
When all three types of

46
00:01:42,820 --> 00:01:44,980
digital twins are
combined and integrated,

47
00:01:44,980 --> 00:01:47,140
it's called a digital thread.

48
00:01:47,140 --> 00:01:50,180
Information gathering during
the entire development and

49
00:01:50,180 --> 00:01:52,180
manufacturing
processes allows this

50
00:01:52,180 --> 00:01:54,605
to be incorporated into
other goods as well.

51
00:01:54,605 --> 00:01:56,440
Now, let us take a look at

52
00:01:56,440 --> 00:01:58,680
the digital twin in
Smart manufacturing.

53
00:01:58,680 --> 00:02:01,040
This figure here shows
the details about

54
00:02:01,040 --> 00:02:04,060
digital twin system of
machine shop floors.

55
00:02:04,060 --> 00:02:06,680
Here all process
equipment workers

56
00:02:06,680 --> 00:02:09,015
are all connected with
main focused point,

57
00:02:09,015 --> 00:02:12,220
that is, in time machine
of cyber twin systems.

58
00:02:12,220 --> 00:02:14,840
Next, let's discuss
how manufacturers

59
00:02:14,840 --> 00:02:17,835
use information or product
data for digital twins.

60
00:02:17,835 --> 00:02:19,960
Data or information obtained

61
00:02:19,960 --> 00:02:21,780
from product performance
distribution

62
00:02:21,780 --> 00:02:23,500
and end-user experiences can

63
00:02:23,500 --> 00:02:25,645
provide insights
for digital twins.

64
00:02:25,645 --> 00:02:27,500
So this leads to engineers and

65
00:02:27,500 --> 00:02:29,535
designers to utilize
this information,

66
00:02:29,535 --> 00:02:32,920
to enhance product performance,
process performance,

67
00:02:32,920 --> 00:02:34,820
and quality, particularly in

68
00:02:34,820 --> 00:02:37,695
terms of customization
and user-friendliness.

69
00:02:37,695 --> 00:02:39,700
Next, let's now learn about

70
00:02:39,700 --> 00:02:41,700
digital twin in the
manufacturing industry.

71
00:02:41,700 --> 00:02:44,280
It examines the design
implementation and

72
00:02:44,280 --> 00:02:47,080
initial outcomes of an IoT
platform embedded with

73
00:02:47,080 --> 00:02:48,500
a digital twin framework for

74
00:02:48,500 --> 00:02:50,600
the best manufacturing
process control and

75
00:02:50,600 --> 00:02:53,560
quality management based
on a real-world use case.

76
00:02:53,560 --> 00:02:56,040
Lean management and
Six Sigma concepts are

77
00:02:56,040 --> 00:02:58,620
based practices for
developing a digital twin.

78
00:02:58,620 --> 00:03:01,180
Manufacturing will
adopt digital twins to

79
00:03:01,180 --> 00:03:04,320
boost efficiency and lower
maintenance expenses.

80
00:03:04,320 --> 00:03:06,600
Integrating and
analyzing data for

81
00:03:06,600 --> 00:03:09,760
equipment and other
production IT systems offers

82
00:03:09,760 --> 00:03:11,900
real-time correlation
and monitoring of

83
00:03:11,900 --> 00:03:14,000
process design
specifications and

84
00:03:14,000 --> 00:03:16,530
actual process
with quality data.

85
00:03:16,530 --> 00:03:18,100
Now, we will discuss

86
00:03:18,100 --> 00:03:20,980
the advantages of having a
digital twin in manufacturing.

87
00:03:20,980 --> 00:03:23,430
Digital twins also
improve engineering,

88
00:03:23,430 --> 00:03:25,110
product design efficiency,

89
00:03:25,110 --> 00:03:28,620
strategy, and execution can
produce remarkable results,

90
00:03:28,620 --> 00:03:30,520
aviation is getting digital.

91
00:03:30,520 --> 00:03:32,580
Digital twins help US Air Force

92
00:03:32,580 --> 00:03:35,440
create a new aircraft
prototype in one year.

93
00:03:35,440 --> 00:03:37,380
Manufacturing digital twins

94
00:03:37,380 --> 00:03:39,120
have saved millions elsewhere.

95
00:03:39,120 --> 00:03:41,220
Digital twins reduce
defective items

96
00:03:41,220 --> 00:03:43,260
by analyzing various
data sources,

97
00:03:43,260 --> 00:03:45,940
minimizing delays, and
enhancing production.

98
00:03:45,940 --> 00:03:49,120
This concept goes beyond
maintenance forecasting alone.

99
00:03:49,120 --> 00:03:50,660
Next, let us discuss

100
00:03:50,660 --> 00:03:53,415
the applications of digital
twin for manufacturing.

101
00:03:53,415 --> 00:03:55,460
First, product design.

102
00:03:55,460 --> 00:03:57,620
During the design phase,
digital twins can be

103
00:03:57,620 --> 00:03:59,940
used as virtual prototypes
that can change to

104
00:03:59,940 --> 00:04:01,920
test different simulations or

105
00:04:01,920 --> 00:04:04,940
designs before even spending
money on a real prototype.

106
00:04:04,940 --> 00:04:06,875
This saves time, money,

107
00:04:06,875 --> 00:04:09,040
and because it can reduce
on the number of times

108
00:04:09,040 --> 00:04:10,420
the product needs to be changed

109
00:04:10,420 --> 00:04:12,415
before it can be put
into production.

110
00:04:12,415 --> 00:04:15,685
Next, let's discuss production
process optimization.

111
00:04:15,685 --> 00:04:18,850
A digital twin of the production
line can be constructed

112
00:04:18,850 --> 00:04:22,110
using sensors to monitor and
improve production targets.

113
00:04:22,110 --> 00:04:24,390
By making adjustments
to the digital twin,

114
00:04:24,390 --> 00:04:27,330
one can better understand
how to maximize output,

115
00:04:27,330 --> 00:04:30,770
reduce variation, and get
to the bottom of a problem.

116
00:04:30,770 --> 00:04:32,910
Moving on to the
quality management.

117
00:04:32,910 --> 00:04:34,410
Keeping quality high and

118
00:04:34,410 --> 00:04:36,710
reducing or eliminating
rework requires

119
00:04:36,710 --> 00:04:39,430
constant monitoring and
acting on data from

120
00:04:39,430 --> 00:04:41,270
sensors connected
to the Internet

121
00:04:41,270 --> 00:04:43,130
of Things throughout
manufacturing.

122
00:04:43,130 --> 00:04:44,850
The digital twin can simulate

123
00:04:44,850 --> 00:04:46,950
the entire manufacturing
procedure to

124
00:04:46,950 --> 00:04:49,550
pinpoint problem areas
and such as upgrades,

125
00:04:49,550 --> 00:04:52,425
such as upgrading machinery
or improving methods.

126
00:04:52,425 --> 00:04:55,010
Next, let's discuss the
supply chain management.

127
00:04:55,010 --> 00:04:56,550
Companies in the logistics and

128
00:04:56,550 --> 00:04:57,950
distribution sectors rely on

129
00:04:57,950 --> 00:04:59,210
digital twins to monitor

130
00:04:59,210 --> 00:05:00,450
performance measures like

131
00:05:00,450 --> 00:05:02,110
the effectiveness
of their packaging,

132
00:05:02,110 --> 00:05:03,810
the effectiveness
of their fleet,

133
00:05:03,810 --> 00:05:06,250
and the efficiency of
their delivery routes.

134
00:05:06,250 --> 00:05:07,930
They shine when applied to

135
00:05:07,930 --> 00:05:09,960
the problems of just-in-time and

136
00:05:09,960 --> 00:05:11,750
just-in-sequence
manufacturing and

137
00:05:11,750 --> 00:05:13,810
the evaluation of
distribution networks.

138
00:05:13,810 --> 00:05:15,710
Moving on to the
predictive maintenance.

139
00:05:15,710 --> 00:05:16,850
Using a digital twin,

140
00:05:16,850 --> 00:05:18,930
manufacturers can
monitor the health of

141
00:05:18,930 --> 00:05:21,710
particular pieces of
machinery or processes.

142
00:05:21,710 --> 00:05:24,610
They can see any discrepancies
that can suggest a need

143
00:05:24,610 --> 00:05:26,310
for maintenance or repair

144
00:05:26,310 --> 00:05:28,165
before a major breakdown occurs.

145
00:05:28,165 --> 00:05:30,650
They are used for adjusting
load distributions,

146
00:05:30,650 --> 00:05:33,135
tool calibrations,
and cycle times.

147
00:05:33,135 --> 00:05:36,030
Cross-discipline
collaboration comes next.

148
00:05:36,030 --> 00:05:38,150
With digital twins, operational

149
00:05:38,150 --> 00:05:39,910
data is readily available,

150
00:05:39,910 --> 00:05:42,630
facilitating cross-disciplinary
cooperation,

151
00:05:42,630 --> 00:05:45,955
enhanced communication, and
quicker decision-making.

152
00:05:45,955 --> 00:05:48,890
Data from engineering,
manufacturing, sales,

153
00:05:48,890 --> 00:05:50,290
and marketing may be shared,

154
00:05:50,290 --> 00:05:52,530
allowing better
collaboration and results.

155
00:05:52,530 --> 00:05:55,750
Then, examine the
customer service process.

156
00:05:55,750 --> 00:05:57,810
A digital twin can
provide data on

157
00:05:57,810 --> 00:05:59,710
the performance distribution and

158
00:05:59,710 --> 00:06:01,690
the end-user experience
of a product.

159
00:06:01,690 --> 00:06:03,770
Engineers and
designers can utilize

160
00:06:03,770 --> 00:06:06,010
this information to boost
product satisfaction

161
00:06:06,010 --> 00:06:07,710
through changes of features

162
00:06:07,710 --> 00:06:10,640
like personalization
and user-friendliness.

163
00:06:10,640 --> 00:06:12,690
Moving on to the
general procedure for

164
00:06:12,690 --> 00:06:14,870
implementing digital
twins in industries.

165
00:06:14,870 --> 00:06:17,250
Integrating process
design, quality,

166
00:06:17,250 --> 00:06:20,430
specification equipments,
real-time operational specs,

167
00:06:20,430 --> 00:06:22,030
and quality measurement into

168
00:06:22,030 --> 00:06:24,210
end-to-end closed-loop
systems enables

169
00:06:24,210 --> 00:06:26,030
online monitoring
and analytics of

170
00:06:26,030 --> 00:06:29,375
the production process and
product quality traceability.

171
00:06:29,375 --> 00:06:31,710
For integrating the
above-said details

172
00:06:31,710 --> 00:06:33,030
from various departments,

173
00:06:33,030 --> 00:06:35,150
digital twins should be aligned

174
00:06:35,150 --> 00:06:38,395
properly with industrial
internet technology.

175
00:06:38,395 --> 00:06:41,810
Online tracking of production
processes and quality for

176
00:06:41,810 --> 00:06:43,730
production equipment
and products

177
00:06:43,730 --> 00:06:46,580
is aligned with
digital twins as well.

178
00:06:46,580 --> 00:06:48,110
Create a digital lean capability

179
00:06:48,110 --> 00:06:49,610
by fusing lean management and

180
00:06:49,610 --> 00:06:51,850
Six Sigma principles
and practices

181
00:06:51,850 --> 00:06:54,320
with online data, and
analytical tools.

182
00:06:54,320 --> 00:06:56,460
During implementation
of the digital twin

183
00:06:56,460 --> 00:06:57,600
in the industry,

184
00:06:57,600 --> 00:07:00,240
for optimizing the production
process and quality,

185
00:07:00,240 --> 00:07:01,740
the following challenges are

186
00:07:01,740 --> 00:07:04,045
faced by machinists
in various areas.

187
00:07:04,045 --> 00:07:06,750
To collect the information
from the below segments

188
00:07:06,750 --> 00:07:09,340
in boosting the utilization
of overall equipment,

189
00:07:09,340 --> 00:07:11,040
in reducing scrap,

190
00:07:11,040 --> 00:07:13,550
in collecting and processing
the production data,

191
00:07:13,550 --> 00:07:15,980
and utilizing
data-driven decisions

192
00:07:15,980 --> 00:07:17,480
to improve the production.

193
00:07:17,480 --> 00:07:19,820
Moving on to how
machinists approach

194
00:07:19,820 --> 00:07:23,280
the challenges while implementing
a digital twin concept.

195
00:07:23,280 --> 00:07:25,400
Bring together and standardize

196
00:07:25,400 --> 00:07:27,760
all sensor data and
setpoint values,

197
00:07:27,760 --> 00:07:30,485
calculating scrap
and waste costs,

198
00:07:30,485 --> 00:07:34,465
set up a digital copy of
production space in an industry.

199
00:07:34,465 --> 00:07:36,410
Let's look at the
output observed by

200
00:07:36,410 --> 00:07:38,350
the machinist in an
industry to optimize

201
00:07:38,350 --> 00:07:40,690
the production process
and quality of a product

202
00:07:40,690 --> 00:07:43,835
after implementation using
a digital twin concept.

203
00:07:43,835 --> 00:07:46,255
Output, reduced waste in scrap.

204
00:07:46,255 --> 00:07:48,510
Waste is a major
expense for industries

205
00:07:48,510 --> 00:07:51,190
and even a small amount of
waste can be very significant.

206
00:07:51,190 --> 00:07:52,750
The digital twin concept helps

207
00:07:52,750 --> 00:07:54,610
eliminate experimental
waste and allows

208
00:07:54,610 --> 00:07:56,345
for better resource utilization

209
00:07:56,345 --> 00:07:58,170
ultimately reducing waste.

210
00:07:58,170 --> 00:08:00,010
Quality control in real time.

211
00:08:00,010 --> 00:08:01,470
Modifying a parameter in

212
00:08:01,470 --> 00:08:02,890
real-world
manufacturing can have

213
00:08:02,890 --> 00:08:04,690
a significant impact on quality.

214
00:08:04,690 --> 00:08:07,790
But manually setting up a
quality control can take hours.

215
00:08:07,790 --> 00:08:10,870
Digital twins provide
real-time outcome modeling,

216
00:08:10,870 --> 00:08:14,290
allowing for faster and more
effective quality control.

217
00:08:14,290 --> 00:08:16,785
Automatic settings
for quality control.

218
00:08:16,785 --> 00:08:19,070
Existing sensors and
setpoint readings

219
00:08:19,070 --> 00:08:21,750
can help the digital twin
automate quality control.

220
00:08:21,750 --> 00:08:24,130
Engineers can spend
less time monitoring

221
00:08:24,130 --> 00:08:25,630
production lines and more time

222
00:08:25,630 --> 00:08:27,390
on high-value activities by

223
00:08:27,390 --> 00:08:30,470
utilizing the proper technology
to automatically alter

224
00:08:30,470 --> 00:08:32,110
set points based on simulated

225
00:08:32,110 --> 00:08:34,230
quality outcomes and
real observations.

226
00:08:34,230 --> 00:08:36,330
Total efficiency of
the equipment or

227
00:08:36,330 --> 00:08:39,320
overall equipment
effectiveness, OEE.

228
00:08:39,320 --> 00:08:42,090
Real-time and automated
quality control provides

229
00:08:42,090 --> 00:08:44,830
production equipment
efficiency statistics and

230
00:08:44,830 --> 00:08:48,150
relieves line employees and
engineers of tedious jobs.

231
00:08:48,150 --> 00:08:49,690
A newer, more reliable

232
00:08:49,690 --> 00:08:52,030
machine can enhance
production as well.

233
00:08:52,030 --> 00:08:54,225
Complexity in production.

234
00:08:54,225 --> 00:08:56,900
Manufacturing involves
many complex variables.

235
00:08:56,900 --> 00:08:59,410
Humans can't quickly
process industrial data.

236
00:08:59,410 --> 00:09:01,670
This data demands
data-driven methods.

237
00:09:01,670 --> 00:09:03,670
Production data
improves production.

238
00:09:03,670 --> 00:09:06,610
Now let's summarize what
we have learned so far.

239
00:09:06,610 --> 00:09:09,850
A digital twin is a virtual
replica of an object or

240
00:09:09,850 --> 00:09:13,210
a system that is constantly
updated with real-time data.

241
00:09:13,210 --> 00:09:15,490
Digital twins aid
in the acceleration

242
00:09:15,490 --> 00:09:18,170
of digital transformation
and smart manufacturing.

243
00:09:18,170 --> 00:09:19,890
The various applications of

244
00:09:19,890 --> 00:09:21,630
digital twins in manufacturing,

245
00:09:21,630 --> 00:09:24,770
such as product design,
production process optimization,

246
00:09:24,770 --> 00:09:27,230
quality management,
supply chain management,

247
00:09:27,230 --> 00:09:30,630
a predictive maintenance,
cross-discipline collaboration,

248
00:09:30,630 --> 00:09:33,170
and examination of customer
service processes.

249
00:09:33,170 --> 00:09:35,450
By this, we have come to
the end of this topic.

250
00:09:35,450 --> 00:09:38,770
Let us meet in another
interesting session. Thank you.