Omron AI Controller: Introduction Updated: 10/17/2018 CUSTOMER

Product Specifications New Model NX102 NX1P2 NJ101 NJ301 NJ501 NX701 Instruction execution time LD instruction 3.3ns 2.0ns 1.1ns 0.37ns Arithmetic instruction 70ns 70ns~ 42ns~ 26ns 3.2ns~ Control cycle 1ms~ 2 ms~ 0.5ms~ 0.125ms~ Program capacity 5MB 1.5MB 3MB 20MB 80MB Variable capacity Non-Retained 32MB 2MB 4MB 256MB Retained 32kB 0.5MB Motion control axis 4/6/8/12 4/6/8 0/2 4/8 16/32/64 128/256 Synchronous control axis 0/2/4/8 0/2/4 PTP control axis 4/4/4/4 4/4/4 0/0 Ethernet/IP port 2 ports 1 port 2 port EtherCAT port # of slaves Up to 64 Up to 16 Up to 192 Up to 512 # of NX units Up to 400 Up to 16 Up to 4000 Up to 4000 Built-in I/O n/a 24/40/40 Total Local NX I/O Capacity 32 8 Enterprise Connectivity OPC UA/ SQL SQL OPC UA/SQL

Sysmac IPC – High Reliability & Performance Split-core Windows® & Sysmac Controls Flexibility and expansion of a PC Network port isolation Reboot Windows® from Controls side Full Sysmac controller with EtherCAT Master 7th Generation Intel CPU Higher performance Cache allocation technology Improved security

So what? Detect an issue has happened and do something about it immediately, reducing risk of bad parts or equipment damage Leverage Omron’s proven controls technology and Data Scientist team instead of researching & developing on your own No extra infrastructure/connectivity cost for Fog or Cloud based solutions (typically managed by IT Department)

AI Application Component What is the Omron AI Controller? New solution that is uniquely able to Collect  Analyze  Utilize data on the Edge within a Sysmac controller, for the purpose of extending equipment lifecycle Hardware, Software, and Service (People) Hardware: based on NY5 IPC as well as NX7 Sysmac CPUs Software: two new utilities AI Operator and AI Easy Modeler, as well as Sysmac Library of Function Blocks called “AI Application Components” Service: Omron members deployed for Start-up and Periodic support at a charge NY-Series NX-Series AI Application Component Configure & Startup

✔- ✔+ ✔ What is an “Edge” device? Cloud Fog Edge Within the Machine or Production Line, without any separate Windows® PC or remote server (Cloud) or Internet Fog Computing Collect Data Collection  Processing Analyze Storing  Modeling Utilize Real-time Monitor  Control FB Long-term Monitor  Visualize Cloud ✔- ✔+ Real-time Monitor  Control FB ✔- Long-term Monitor  Visualize ✔+ Fog ✔ Edge Real-time Monitor  Control FB ✔+ Long-term Monitor  Visualize ✔- Edge Omron is focused on bringing value to customers with Edge level solution. Relative advantages and disadvantages are indicated as ✔+, ✔, and ✔- Against Cloud, Edge, and Fog between Edge and Cloud are defined as edge computing. Each of them has advantages and disadvantages for gathering, analyzing, and using data. Edge is better to gather sensor data near the factory than Cloud. On the other hand, Cloud is better to analyze data than Edge because higher computer resources are required. The upper levels are better to monitor massive data with ERP design data and material data for long term. Edge is better to use real time performance for feeding data back to controllers.

What does the Omron AI Controller do? Detecting outliers (failures, defectives) by learning from historical data in the machine 1) Issue definition 2) Cause identification 3) Prepare sensors Human Based on failure impact and feasibility study of AI Controller utilization, define issue Based on Cause and Effect Diagrams, identify cause of machine defect Based on the cause, add necessary sensors to machine Machine defects B occurs Human Method Material Machine Machine Omron AI Controller fully covers the data collection, analysis, and utilization. 　Omron also offers startup support for the entire process. 4) Collect Data collection Pre-processing Causal analysis, modeling 5) Analyze Storage Monitoring Visualization 6) Utilize Control FB All data-based solutions will require 6 steps, some Human, some Technology. First 3 steps are all Human steps, leveraging Subject Matter Expertise and real-world experience Omron AI Controller only solves Steps 4-5-6 with technology Collect the raw data of machine Eliminate noise, create and collect the features of normal/abnormal state Store the characteristics data Create model data after causal analysis Status monitoring and control FB based on model data X1 X2 X3 X4 X5

Learn without being explicitly programmed Machine Learning Anomaly Detection Omron AI Controller addresses narrow scope of AI – only Machine Learning for Anomaly Detection This is still very powerful for manufacturers and machine builders Learn without being explicitly programmed

AI Application Components First Release AI Application Components Pre-made Sysmac Library Function Blocks (FB) for… Mechanism Failure Type Machine event (failure causes) Air Cylinder Operation stop Rubber Gasket broken (Rod/Piston) Over speed Speed Controller broken Over vibration Air Cushion broken Contamination Ball Screw Guide broken Ball Bearings falling out Conveyor Belt or Pulley Vibration and low accuracy Belt loosen Belt broken Pulley broken First release – 3 mechanics, but library will grow with continued research & development Anything outside of these AI Application Components (inside Sysmac Library) would be a custom model, still possible but requires additional clarification and time

Overall Equipment Effectiveness = Availability x Performance Quality Value of Omron AI Controller OEE Overall Equipment Effectiveness Helps improve OEE at the machine level by increasing uptime Uptime Downtime Operating time Stoppage Loss Net operating time Performance Loss Value operating time Defect Loss Loss types Improved by AI Controller 1: Equipment failure 2: Changeover 3: Part replacement 4: Waiting for material 1: ✓ 2: 3: ✓ 4: 5: Minor stoppage/idle 6: Deceleration/slowing 5: ✓ 6: 7: Defect/modification 7: ✓ The AI controller will improve overall equipment effectiveness (OEE) As you know, the key of improving OEE is how to improve availability, performance, and quality

Thank you Expected Americas Launch: Dec 2018