Internet of Manufacturing Things: Lessons from Precision Manufacturers

This talk will discuss the state-of-the art in the Internet of Manufacturing Things (IoMT) and its potential in analyzing and improving shop floor productivity. The talk will characterize the challenges of bringing IoMT to the shop floor and will introduce key enabling technology, including: high-speed data collection from heterogeneous sources; integration across software and hardware platforms; and tools for decision-making across spatial and temporal scales. This talk will present lessons learned in leveraging IoMT to improve production efficiency across high-precision manufacturers.

Transcript

1. The Internet of Manufacturing Things: Lessons from Precision Manufacturers Athulan Vijayaraghavan CTO and Co-Founder, System Insights Berkeley, CA | Chennai, India MC2 Conference, Chicago IL April 28, 2015
2. Background • Co-Founder and CTO of System Insights • Working with MTConnect from inception • System Insights: We develop VIMANA, IoMT platform for predictive analytics • We work with: 2
3. The Internet of Things 3 “interconnection of sensors, devices, and other things”
4. The Hype Cycle 4
5. Why Manufacturing? • Manufacturing is Big: $2 Trillion sector • Discrete Manufacturing: Products for consumers and the supply chain • High potential for productivity improvement • Manufacturing generates a very large amount of data – most of it falls on the floor 5
6. Manufacturing Today • Global • Fragmented • Heterogeneous 6
7. Improving the Transformation • Focus: – Productivity? Profitability? – Part quality? Employee safety? – Sustainability? Energy usage? • How about a more holistic view? 7 transformation raw material stuff
8. Track flow of resources and intelligence across manufacturing process Process Improvement Design Integration Usage Analytics Grand Challenge: Process Traceability 8 tim e Foundry Forging Roughing Finishing Sub-Assembly Warehouse Final Assembly Shipping unique part Design Impacts Usage Impacts Manufacturing
9. Solving the Grand Challenge 9 Standards Sensors + Data Software Reduce data complexity Improve interoperability Across all hierarchies of production Keeps Growing Data Management Decision Making
10. Standards: Why we Are Here • Manufacturing data highly complex • High barriers to entry • Specialized technical knowledge deterrent to innovation 10
11. Standards: IoMT Requirements • Security – MTConnect is read-only and inherently secure • Reliability – MTConnect provides a reliable messaging platform • Performance – MTConnect can run on embedded platforms – One agent can support multiple devices • Sensor data – The most complete standardized sensor vocabulary and extensible for new sensors – Support for realtime time-series data 11
12. Standards: For IoT • MQTT Protocol – Lightweight IP-based M2M communication protocol – Pub/sub model – highly scalable – Large community, supported by Eclipse • MQTT + MTConnect – Take domain model of MTConnect and apply in MQTT – MTConnect can sit below/above MQTT layer based on the application 12
13. Sensors + Data: Volumes Small Shop: 2~10 TB/year Medium Shop: 5 ~ 25 TB/year Large Shop: 16 ~ 80 TB/year Enterprise: 80 ~ 5000 TB/year US Machining Sector: 200 PB ~ 1XB/year 13
14. Data: Types Structured Unstructured Tribal Knowledge Sensor Machine Telemetrics Alarms, Faults Quality Control Performance + Test Annotations Over-rides Interruptions 14 So what do we do with all of this data?
15. Software: Event Reasoning Event Processing fusion filter aggregate relationships The Manufacturing Event Cloud spindle speed position alarms notification static data feedrate overrides Event: Something that happened at a point in time tribal knowledge 15 Complex Event Processing Temporal Overlap Contains Before/After Spatial Clustering Trending Shapes
16. Software: Temporal Decision Scales Temporal scales can vary from µ- seconds to days anytime: process management neartime: process improvement realtime: process control m-Seconds Seconds Hours Days Process Interface Sub- Components Manufacturing Equipment Manufacturing Supply Chain Manufacturing Enterprise Temporal Decision Scale Manufacturing Analysis Scale 16
17. Software: Multidimensional Reasoning :01 :02 :03 :04 :05 :06 V Mill 1 Lathe 1 Robot CMM Cell Day Line Part 1 Part 2 Part 6 ... ... Batch Power: 3562 Watts Load[X]: 120% Alarm: ACTIVE Time Equipment Product :01 :02 :03 :04 :05 :06 V Mill 1 Lathe 1 Robot CMM Cell Day Line Part 1 Part 2 Part 6 ... ... Batch Time Equipment Product Report: Total Energy Usage Period: Daily Device: V-Mill 1 :01 :02 :03 :04 :05 :06 V Mill 1 Lathe 1 Robot CMM Cell Day Line Part 1 Part 2 Part 6 ... ... Batch Time Equipment Product Analysis: Power Consumption Device: Cell 1 Time: Now Multi-dimensional reasoning allows us to slice data across any plane, including: time, machine organization, parts 17
18. LESSONS 18
19. The Internet Can Be Big • Machine tools, part handling, robotics • Data everywhere • Visibility everywhere 19
20. Consistency Consistency Consistency • Utilization • Parts • Device Performance 20
21. Human in the Loop • Critical source of know-how • Invite feedback and involvement • Transparency and knowledge-sharing 21
22. IoMT: Enabling Technology 22 High speed data from heterogeneous sources Integration across software and hardware platforms Decision-making across spatial and temporal resolutions
23. IoMT vs. IoT • Enterprise focused vs. Consumer Focused • Islands of Excellence vs. New Ecosystems • Mature markets vs. New Markets • “the internet of what?” vs. “take my money!” 23
24. Closing Thoughts • Terrific potential • Being domain specific helps – a lot • Don’t reinvent the wheel • Don’t forget the human in the loop 24
25. Questions? 25 We  are  at  the  show   #  307