fundamentals of an industry 4.0 solution

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Fundamentals of an Industry 4.0 SolutionAndy Simoneau

Advanced Manufacturing and Industry 4.0AMI4

Industry 4.0 and Cyber Physical Systems


My Industry 4.0 Story – Day 1

One day a Tier 1 supplier had a QA problem so they called the biggest cloud computing company to solve it.

Production (OT)• Inspection for part defect• False rejects and missed defects• Defect – microcracking and pitting• Inspection technique – image

analysis and ultrasonic testing• Cycle time 6s - reduce to 2s

Compute side (IT)• Collect images• Store data• Use machine learning• Automate part accept/reject

3HOURS


My Industry 4.0 Story – Day 2

Production (OT)• Differentiate images between

microcracks, fingerprints, shadows, grease, etc.

• We need 20 photos of all surfaces stitched together.

• Analyse for defects – Pass/FAIL

Compute side (IT)• We can store the data – no problem• Can’t handle the Volume, Velocity, Variety• We can’t make decisions on the part PASS/FAIL in

time

QA Inspectors (talked to them after the meeting)• No faith in the system• Manipulating parts before and after inspection – “they knew a defect when they saw one”• They were correcting the machines, pre-cleaning parts• Carrying out full inspection of failed and passed components

Outcome:Project postponed till we get more information and can identify the appropriate solution


My Industry 4.0 Story – What went wrong?

3 MAJOR MISTAKES

#1. Lack of understanding of how the other side functions, and a language barrier between OT and IT, they were identifying similar goals – but they could not see it because they did not understand the other sides language.

#2. A honed focus on the solution – not the problem.

#3. No understanding of how the people doing the job interact with processes and technology. Assumptions are made about the people and processes that we are developing IoT solutions for. Missed people – missed verticals.

History


History

1780 - Mechanisation

1870 - Electrification

1970 - Automation

Present - Digitisation

Future - Personalisation

Industrial production based on machines powered by water and steam

Mass production initiated, assembly lines are trending

Automation using electronics and computers

Introduction of connected devices, data analytics and AI to automate processes further

The 5th industrial revolution will focus on person-machine collaboration, humans working with cognitive computing. People will be added back into the equation, being upskilled to perform value-added tasks leading to mass customisation.

The Pillars of Industry 4.0


9 Pillars of Industry 4.0

Advanced Manufacturing Solutions

Additive Manufacturing

Augmented Reality – AR/VR

Simulation

Cloud Storage

Cyber-Security

Big Data and Analytics

Industrial Internet

Horizontal/Vertical Integration

Autonomous systems, robots, numerous integrated systems, sensors and standard interfaces

3D printing for spare parts, rapid prototyping, complex geometries

Cross company data integration based on data transfer standards. Preconditioned for fully automated value chain (supplier to customer, management to the shop floor

Augmented reality for heads up display, SOP’s, safety, maintenance and training.

Full evaluation of available data (e.g. ERP, MES, CRM, machine data). Realtime decision making support and optimization

Simulation of value networks – process, operations, systems, etc.

Management of huge data volumes in open systems and real-time communication of production systemsOperation in networks and open systems to protect data, and machines. High level networking between intelligent machines, products and systems)

Network of machines and products


Building Blocks and Fundamental Pieces

Artificial Intelligence (AI)

Digital Twin

Edge Analytics

Additive – 3D printing

Sensors

Robotics

AR, VR, and MR

Platforms & Connectivity

Blockchain

Cybersecurity


The Initial Driver of Industry 4.0

Sensors

Digital Twin

Edge Analytics

Additive – 3D printing

Platforms & Connectivity

IND4 + CPS


Platforms and Connectivity

Start-Ups and SME’s are critical part of success

Start-ups and SME’s have expertise in niche technologies such as machine learning, predictive analytics, cyber-security.

Independent software vendors (ISV’s) build the underlying platform with the capabilities to host IoT applications and leverage applications using cloud technology

Automation vendors are in the OT domain, expertise in installation and integration -they remain key to data collection and meeting the base requirements of IoT.

AutomationVendors

ISV’s

Start-ups & SME’s


Digital Twinning

Converting a physical machine into data that can be interpreted by people.

Can be done for a single process, operation, machine, production line, . . .

➢ Allows for analytical decision making

➢ Allows for process and product visualization

➢ Improved system and process control

➢ Commissioning machines from remote centres

➢ Can be scaledAllows us to accurately predict the current and future states of physical assets by analysing their digital counterparts – READING/ANALYSING SENSOR DATA.


Edge Analytics

Decentralized at-source computing is expected to see a significant uptake across all industries.

Popular use cases:➢ Quality assurance➢ Process performance➢ Realtime monitoring of

worksite safety➢ Package tracking and

logistics➢ Traffic monitoring➢ Condition based

maintenance➢ Agri-health monitoring


Additive Manufacturing + Hybrid Engineering

Microwave guide array – 316 Stainless steel

Cons:➢ Expensive➢ Low volume➢ Time consuming➢ Material issues➢ Still requires processing – hybrid

machining➢ Removal of design constraints

poses major challenges for manufacturing

Pros:➢ Material

options/combinations are unique

➢ No design constraints➢ Complex, novel structures➢ High degree of

customization➢ Near net shape

Hybrid Systems

Hybrid manufacturing machines combine one of the 7 types of Additive Manufacturing (AM) technologies and CNC milling capabilities into one single machine bed.

Allows both technologies to work on the part one after another or alternatingly to create geometry and desired features.

For example, AM technology with its freedom creates a complex shape while the CNC milling head carries out the precision machining.


Hybrid Systems



Sensors

Sensors are a key growth opportunity and primary facilitator for adoption of Industry 4.0 across industry.

Requirements of Industry 4.0:• Connected multi-interaction between

machines, and human operators through a network

• Digital data – analytics, virtual models, simulation

• Physical and Virtual Technical Assistance – minimizing machine down time, improving decision making through a comprehensive visualization of the process

• Autonomous decision making – complex decision making allowing for mass production and mass customization.


Sensors (cont’d)

1. Accelerometer2. Gyroscope3. Magnetometer4. GPS5. Proximity Sensor6. Ambient Light7. Microphone8. Touchscreen9. Fingerprint10. Pedometer11. Barometer12. Bar Code/QR Scanner13. Heartrate Sensor14. Thermometer15. Humidity/Moisture


Sensors (cont’d)

Thank you

Advanced Manufacturing and Industry 4.0AMI4


fundamentals of an industry 4.0 solution

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