AR IMPACT IN INDUSTRY - DEPLOYING AND SCALING AUGMENTED REALITY - ALBERTO TORASSO, DAQRI
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AR IMPACT IN INDUSTRY DEPLOYING AND SCALING AUGMENTED REALITY IN SIEMENS ALBERTO TORASSO, DAQRI SENIOR ACCOUNT MANAGER
AGENDA
• Introduction to DAQRI
• 3 Case Studies With Siemens
- Gas Turbine Servicing
- Wind Turbine Servicing
- Wind Turbine Inspection
• Best Practices For Creating
Real AR Solutions
DAQRI WORLDWIDE
DUBLIN MILTON KEYNES
• Headquartered in Los BATTLE
VIENNA
DETROIT
Angeles, CA
• Founded in 2013
LOS ANGELES • 300 employees across
7 offices in 4 countries
DAQRI PRODUCTS
DAQRI SMART HELMET®
DAQRI SMART HUD™
DAQRI SMART GLASSES™
DAQRI SOFTWARE PLATFORM
Analytics API Services Analytics CORE Apps Camera Remote Expert
4D Studio
Custom
Applications
Enable app Remote Expert API Remote Expert Desktop 4D Viewer Thermal
development at
scale by providing Enable easy and/or
a common set of WYSWIG Editor automated content
tools and services creation at scale
across hardware
platforms Unity Extension PUBLIC API
Work Package
Eclipse Plug-in C / C++ Creation Engine
Form Factor
Extensions
Customer Portal Operating System UI FW CV RGB Thermal
Security Auto HUD
Connectivity Multimedia Display Sensors Depth System Services
Device
Management
Enable commercial
deployments at
scale Hardware
User Management
SMART GLASSES HOLOGRAPHIC
SMART HELMET
HUD
Support and
Resources
5
UNITY EXTENSION
DAQRI SDK that allows developers to
create and deploy apps to
SMART DSH DE via Unity
DEVICES
A man ag ed solu tion f or th e en terprise.
C++ API
Public APIs that provides
access to DAQRI Hardware
and services, enabling native
development and integration
with existing applications
CASE STUDY 1 GAS TURBINE SERVICING DAQRI and Siemens created an application to improve the key servicing task of burner assembly.
GAS TURBINE SERVICING CASE
STUDY
NORMAL TECHNICIAN
TRAINING PROCESS
• 6 weeks in the classroom
• 7 weeks of hands on
training
• On the job training with
someone more experienced
• After 3 years, certified as
turbine fitterCAN AR MAKE IT BETTER?
GAS TURBINE SERVICING CASE
STUDY
BURNERS
CONNECTS TO
GENERATORGAS TURBINE SERVICING CASE STUDY
GAS TURBINE SERVICING CASE
STUDY TIME TO FIRST
BURNER ASSEMBLY
WITH DAQRI SMART HELMET
60
52
45 48
45
TIME IN MINUTES
40
30
15
0
Novice 1 Novice 2 Refresher ExpertGAS TURBINE SERVICING CASE
STUDY
COMPARE TIME TO FIRST ASSEMBLY
FOR NOVICE WORKERS
NORMAL WITH DAQRI SMART HELMET
600
500
480
400
TIME IN MINUTES
300
200
100
45 52
0GAS TURBINE SERVICING CASE
STUDY
PREDICTING ADOPTION
SYSTEM USABILITY SCALE
MANAGEMENT END USER END USER
DRIVEN ADOPTION SUPPORTED DRIVEN
ADOPTION ADOPTION
EXPERT 73
73
USER GROUPS
REFRESHER 85 85
85
NOVICE 85
0 10 20 30 40 50 60 70 80 90 100 110
SYSTEM USABILITY SCORE
• Brooke, J. (1986/1996). SUS - A quick and dirty usability scale. Usability Evaluation in Industry, 189(194), 4-7.
• Sauro, J. (2011). A practical guide to the System Usability Scale: Background, Benchmarks & Best Practices. Denver, CO: Measuring Usability LLC.GAS TURBINE SERVICING CASE
STUDY
KEY TAKEAWAYS
ERROR
SPEED EFFICIENCY CONFIDENCE
REDUCTION
• Typically takes a day to • No instructor required • Novices felt empowered • No users made any
complete first assembly and “safe“ when using errors in assembly using
vs. 45 min with AR • More intuitive learning the App to complete the the app
• Access to reference assembly
• Learned knowledge • Refresher noted being
immediately applied information more quickly reminded of things he
forgot
“Without the helmet, “Humans are very visual, If “With support like this, I “It helped me see some
learning this assembly I can interact with the would be able to do any things I didn’t remember
would take me a day” drawings in 3D like this it assembly” from when I originally
makes understanding wrote the assembly
novice participant #2 them a lot easier, novice participant #1 procedures”
especially if I have to deal
with new components that refresher participant #1
I have never built before”
expert participant #1CASE STUDY 2 WIND TURBINE SERVICING DAQRI and Siemens Wind Power (now Siemens Gamesa) created an application to improve servicing of critical yaw control motors using AR.
WIND TURBINE SERVICING CASE STUDY
YAW MOTORSWIND TURBINE SERVICING CASE STUDY
WIND TURBINE SERVICING CASE STUDY
TIME TO COMPLETE
FIRST SERVICING
WITH DAQRI SMART HELMET
100
80
77
TIME IN MINUTES
60
40
40
30
20
0
Novice Refresher ExpertWIND TURBINE SERVICING CASE STUDY
PREDICTING ADOPTION
SYSTEM USABILITY SCALE
MANAGEMENT END USER END USER
DRIVEN ADOPTION SUPPORTED DRIVEN
ADOPTION ADOPTION
EXPERT 73
73
USER GROUPS
REFRESHER 48 85
NOVICE 71
0 10 20 30 40 50 60 70 80 90 100 110
SYSTEM USABILITY SCORE
• Brooke, J. (1986/1996). SUS - A quick and dirty usability scale. Usability Evaluation in Industry, 189(194), 4-7.
• Sauro, J. (2011). A practical guide to the System Usability Scale: Background, Benchmarks & Best Practices. Denver, CO: Measuring Usability LLC.WIND TURBINE SERVICING CASE STUDY
KEY TAKEAWAYS
ERROR
SPEED EFFICIENCY CONFIDENCE
REDUCTION
• Learned knowledge is • Add order to a • All participants felt safe • No users made any
immediately applied traditionally non-ordered while wearing the helmet errors in servicing the
without need for process allowing for to complete their tasks yaw motor
classroom instruction measurement and
improvement • Most felt equally as • Refresher noted that the
• The refresher noted he aware of their linearity added by the
he felt he was notably • Novices are largely able surroundings while using app helped him ensure
faster using the app than to learn the process on AR save 1 participant he was completing the
re-learning from their own right steps
reference materials
Refresher participant #1 “It won’t take a person “The technology supports
noted that he felt faster long to learn how to use it” a field technician to do the
completing tasks with AR right thing on the
and could see himself expert participant #1 equipment”
using it on a daily basis in
the future. novice participant #2CASE STUDY 3 WIND TURBINE INSPECTION DAQRI and Siemens Wind Power (now Siemens Gamesa) created an application to support inspection of wind turbine hubs before shipment to assembly site.
WIND TURBINE INSPECTION CASE STUDY
HUBApp to their job/hub inspection, if there was anything that the App could better address, WIND TURBINE INSPECTION CASE STUDY and any additional comments. FIGURE 4. At left: Inspector completing the 9 inspection points using current methods. At right: Inspector completing the 9 inspection points using the DAQRI AR App, wearing the DAQRI Smart Helmet DE.
WIND TURBINE S
I NESRPVEI C ITNI O
G NC C
AASE
S ES T
SUTU
DDYYDAQRI 11
SIEMENS-GAMESA
WIND TURBIN CASE
E I NSTUDY
SPECTION CASE STUDY
DAQRI
Result s SIEMENS-GAMESA CASE STUDY
Time Savings Accuracy and Quality
Preparation 3 Current Method
Current Method
Walking from office DAQRI AR Condition
N U M B E R O F TA R G E T S N AG S
Inspection
2
CONDITION
Walking back to office
DAQRI AR Condition
Post-processing
1
0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00
0
TIME (MINUTES) INSPECTOR 1 INSPECTOR 2
Figure 5. Mean time to complete each component of the hub inspection, using DAQRI AR technology Figure 6. There were three target snags placed inside the hub. The inspectors missed one of three
versus current methods. Error bars show standard deviation (SD). snags using current methods, but identified and documented all three snags using the DAQRI AR App,
run on the DAQRI Smart Helmet DE.
Current condition (min) DAQRI AR Condition (min) In the current method (i.e., non-AR) condition, both Inspector 1 and 2 missed the “missing
eye bolt” snag. Furthermore, in this condition, Inspector 2 placed two fluid-spill-related
Mean SD Mean SD
snags (i.e., not target snags), for total of four snags.WIND TURBINE INSPECTION CASE STUDY
KEY TAKEAWAYS
ERROR
SPEED EFFICIENCY CONFIDENCE
REDUCTION
• the two inspectors were • Add order to a • All participants felt safe • Both inspectors missed
43.85% faster using the traditionally non-ordered while wearing the helmet one of the three target
DAQRI AR App, as process allowing for to complete their tasks snags using current
compared to current measurement and methods. They found all
methods improvement • Novice were able to go three in the AR condition.
trough an inspection
• The DAQRI AR App • The DAQRI AR App process that before • Linear flow guided
condition was 725.15% preparation time was would have taken long to inspectors in the
faster than current shorter than current learn inspection, which was
methods, post-inspection, methods because all more accurate and of
thanks to automatic inspection points were higher quality, as
generation of a report already in the App, compared to current
methods
“Less documentation “Don’t forget an inspection “Guidance, quality, “Same inspection Every
afterwards ” point, easy data collecting, reassurance” time”
easy handover to a “Better quality, more
novice colleague” novice consistent inspections”
manaager inspectors #1 and #2M A D E W I T H D A Q R I V OS™ S D K
AND OVER 200+ MORE COMPANIESAR BEST PRACTICES
EXPAND!
MEASURE
RESULTS
THE RIGHT UX
USER CENTERED
DESIGN
GO TO REAL
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