Machine Learning for Auto Optimization

Transcription

1 Machine Learning for Auto Optimization

2 What is Machine Learning? Definition: Machine learning refers to any system where the performance of a machine in performing a task improves by gaining more experience in performing that task. Experience refers to the data that we fed in to the algorithm and improvements refers to it output which is considered as an action. ML is intelligence acquired by a machine, which is similar to human natural intelligence. ML use existing data to forecast future behaviors, outcomes, and trends. ML involves using statistical / mathematical techniques.

3 Examples of Machine Learning A computer program is said to learn from experience E with respect to task T and performance gauge P. ML Algorithm Historic Experiences Traffic pattern(e) Performance Future Traffic measuring(p) pattern(p) Traffic Task pattern(t) Optical Character Recognition: categorize images of hand written characters by the letters represented. Face detection: Find faces in Image. Spam Filtering: identify messages as spam or non spam.

4 Applying Machine Learning to CNC Machines Performance Improvement using Machine Learning: Thermal Displacement Compensation Automatic Servo Tuning Adaptive Control for optimizing cycle time. Learning control for achieving high performance machining. Inertia Estimation, for higher acceleration to reduce cycle time. Smart Program Analysis Acc/dec decided dynamically Preventive Maintenance using Machine Learning: Prediction of Failures Data Analysis using AI - Pattern Analysis/ Waveform Analysis Minimizing Downtime using AI

5 Thermal Displacement Compensation Conventional Method :Temperature sensor :Displacement sensor Data Collection Temp. Disp. Analysis, Formulation Heat transfer analysis Thermal fluid analysis. etc. Software development It is not easy to derive the relationship between temperature and displacement necessary for thermal displacement compensation

6 Thermal Displacement Compensation Using machine learning :Temperature sensor :Displacement sensor Model development tool Thermal Displacement Compensation option Temp. Disp. Data Collection Software Learning Data Model Development Software Machine Learning TDC Model Thermal Displacement Compensation Temp. Comp. Machine learning can derive the relations from the data of temperature and displacement and can create thermal displacement model.

7 Automatic Servo Tuning Auto-tuning of servo gain and Acc/Dec time constant according to target work piece Useful for machining optimization Ethernet Manage Workpiece 1 SERVO Tuning Data 1 Workpiece 2 SERVO Tuning Data 1 : SERVO Tuning Data 2 Collect Collect Restore SERVO Tuning Data 2 SERVO Tuning Data 1 Workpiece1 Workpiece2 Workpiece1 Machine tools

8 Inertia Estimation, For higher acceleration to Reduce Cycle Time Can automatically estimates the inertia when Job changes. Can achieve optimum positioning time.

9 Adaptive Control for Optimizing Cycle Time Automatic Feed rate control according to spindle load and temperature. Controlling feed rate according to spindle load strikes a good balance between shorted cycle time and longer life time of cutting tools.

10 Adaptive Control for Optimizing Cycle Time

11 Servo learning Control Learning Control for Achieving high performance machining Suppress periodic machining disturbance.

12 Servo learning Oscillation Learning Control for Achieving high performance machining Avoid chip Entanglement by oscillation cutting for chip shredding using servo learning. Contribution to productivity improvement by continuous operation. Reduction of production costs by elimination of chip removal system.

13 Smart Program Analysis-Acc/Dec decided dynamically Artificial Intelligence Contour Control Function for reading small segments of program in advance and will create smooth profile.

14 Prediction of Failures- AI Spindle Monitor Anomaly monitoring of spindle by machine learning. Can predict the spindle failure in advance. Calculation of Anomaly score Model creation at normal state Acquisition of servo data

15 Data Analysis Using AI- Pattern Analysis/Waveform Analysis Collection of various sensors data and servo data Collect data from various sensors (temperature, shock etc.) via CNC by using i/o units. Motor speed Machine Acc. Collect servo data with high speed sampling (1ms) and to store with file format Displays collected data for analysis. Operation Management software Sensor data... Servo data Database Servo data VIEWER software Analog interface module External sensor Applications MULTI SENSOR I/O UNIT Shock sensor Temperature sensor Monitor the servo and spindle loads and establish pattern(signature) for the component.

16 Minimizing Down time using AI Manages diagnosis information of Trouble Diagnosis and Machine Alarm Diagnosis with final solutions when alarm occurs. When newly alarm occurs, indicate solution from similarly diagnosis information Normal Trouble Diagnosis AI Trouble Diagnosis Collect Add Actually Measures/ Treatments Indicate Alarm! Operator implement diagnosis according to CNC guidance/manual. Operator needs to diagnose when multiple estimation causes finally to be left AI indicate higher probability solution from past history data. Automatic judgment from countermeasure / treatment information in case of multiple estimation causes remained. Rapidly restoration at trouble

17 Applying Machine Learning for Robotic Automation. Faster Bin Picking application: Robots automatically learns the picking sequence of work piece. Drastically reduces the time for manual setting and tuning. AI Bin picking Application

18 Applying Machine Learning for Robotic Automation. Learning Vibration Control: Learning robot realizes high speed smooth motion with suppression of vibration by LVC (Learning Vibration Control). W/O LVC W/ LVC Learning Control + Sensor Technology Accelerometer Vibration Suppressed!! Learning robot merit This function has overcome vibration issues of high speed motion, which has not be used before. Cycle time can be reduced by high speed motion. (i.e. realization of higher performance for each )

19 Prediction of Failures- Mechanical Failures To eliminate unplanned downtime. Process Health Mechanical Health proceed production proceed production proceed production Reducer to be exchanged next weekend. proceed production Vision detection result Welding current monitor Servo gun status monitor Operational status Increasing vibration of J2! System Health Memory usage Alarm information Maintenance health Grease replacement Battery replacement Greasing to the balancer bush Replace grease! Alarms

20 Machine Learning on Standalone Vs network of Systems Stand alone Machine with networking Server with ML Learning with experience is confined to one machine. Learning will be vast since all machines will be sharing there data and solution can be immediately found.

21 Machine learning With IOT IoT- Connects Things Internet of Things IOT provides a platform on which number of devices are connected and pushing down data in a centralized system. IoT devices follow these five basic steps: measuring, sending, storing, analyzing, acting. The collected datasets are fed into Machine learning algorithms to take active decisions.

22 Cloud Computing In IOT System, to save huge amount of data, known as Big Data, stack of storage devices are required. IOT data will be increasing exponentially & hence will require frequent hardware up gradation. To Run Machine learning/ai algorithms, high computation power processors are required and single processor is not sufficient. ON Premises ON CLOUD

23 Advantages of Cloud Computing Flexibility If your needs increase it s easy to scale up your cloud capacity, drawing on the service s remote servers. Disaster recovery Businesses of all sizes should be investing in robust disaster recovery,. Automatic software updates Suppliers take care of servers for you and roll out regular software updates. Capital-expenditure Free Cloud computing cuts out the high cost of hardware. Work from anywhere With cloud computing, if you ve got an internet connection you can be at work.

24 T2 Sec Non-Critical data sent directly After processing data is saved in cloud FOG Computing Data Segment Cloud Critical Data Non- Critical Data FOG Computing is an intermediate layer between device and Cloud. FOG (T3 Time for processing) T4 Sec On- Premises IOT Devices (T1 time for data generation)

25 IOT/Cloud computing with ML The only way to analyze the data generated by the IoT is with machine learning/ai.

26 FANUC Solutions for Machine learning & IOT. AI and Cloud computing FANUC Intelligent Edge Link & Drive system Data collection and Monitoring (IOT) Connecting MT-LINK i Visualization Communicating Host system software Diagnosis Notification Data collection Communication interface Collecting FANUC MT-LINK i Smart/AI Features AI Thermal displacement compensation AI Servo Tuning. AI spindle monitoring. AI contour control(aicc). AI Bin Picking Smart Adaptive Control Smart Feed axis Acc/Dec Servo learning Control. Zero Down Time(ZDT) Learning Vibration control

27 Thank You for your Kind attention