Towards Smart Machine Tools A NIST Perspective of Research Opportunities

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Towards Smart Machine Tools A NIST Perspective of Research Opportunities Greg Vogl Intelligent Systems Division Engineering Laboratory National Institute of Standards and

Certain commercial products, some of which are either registered or trademarked, are identified in this presentation in order to adequately specify certain procedures.

1 Towards Smart Machine Tools A NIST Perspective of Research Opportunities Greg Vogl Intelligent Systems Division Engineering Laboratory National Institute of Standards and Technology (NIST) Official contribution of the National Institute of Standards and Technology (NIST); not subject to copyright in the United States. Certain commercial products, some of which are either registered or trademarked, are identified in this presentation in order to adequately specify certain procedures. In no case does such identification imply recommendation or endorsement by NIST, nor does it imply that the materials, equipment, or software identified are necessarily the best available for the purpose.

technology 6,100 Employees/Associates NIST partners with about 1,300

2 NIST Economic Growth NIST within U.S. Department of Commerce NIST promotes U.S. innovation by advancing measurement science, standards, and technology 6,100 Employees/Associates NIST partners with about 1,300 manufacturing specialists through 400 manufacturing extensions 2

Manufacturing = Economic Growth Manufacturing = 12.5M U.S.

but U.S. machine tool spending 18% in 2016 (to $8.

Tool Survey U.S. Manufacturing Jobs Per Year 13.5M 12.5M 11.

3 Manufacturing = Economic Growth Manufacturing = 12.5M U.S. jobs & about 60% exports World machine tool consumption 4% in 2016, but U.S. machine tool spending 18% in 2016 (to $8.7B) over World Machine Tool Survey 2017 World Machine Tool Survey U.S. Manufacturing Jobs Per Year 13.5M 12.5M 11.5M 2008 Modern Machine Shop, 2015 Capital Spending Survey & Forecast 2018 U.S. Bureau of Labor Statistics 3

4 Machine Tools are Vital for Production 100s of machine tools used in plants to mill precision parts 3+ axis motion Hyundai Wia Plant 5-Axis Machine Cutting of Helmet (Daishin Seiki Corporation) 4

) Machine tool degradation causes performance changes and unplanned

5 Problem = Unplanned Downtime Faults/failures 10s of $Billions per year (> new machines!) Machine tool degradation causes performance changes and unplanned downtime Wear Machinery Lubrication (2004), Wear in Rolling Element Bearings and Gears Reliabilityweb.com (2018), Lubrication FMEA: The Big Picture 5

6 Why Not Measure Health? Major manufacturers say routine tracking of performance is too expensive Accuracy a pro, but setup and operation time/cost a con Offline Lack of periodic data Expensive Cap probes hours Laser 1-2 days IBS Precision Engineering Ballbar 1 hour API Renishaw 6

7 GOAL: Smart Machine Tools Industry challenge: Machine health in 5 min? On-machine measurement science to diagnose performance and root-causes Offline Online Lack of periodic data Data-rich Expensive Inexpensive Linear Axis Health Tracking [How?] Sensors Sensors Spindle Health Tracking [How?] Squareness Health Tracking [How?] 7

GOAL: Smart Machine Tools Make machine tools self-aware with diagnostics of performance & root causes Predict part errors based on health tracking &

8 GOAL: Smart Machine Tools Make machine tools self-aware with diagnostics of performance & root causes Predict part errors based on health tracking & optimize asset management Tablet Machine #1 Axis 1 15 µm range Spalling detected Spindle Axis 2 70 µrad range Hyundai Wia Plant Optimum Machine: #5 8

9 IMU for Linear Axis Monitoring 9 cm Vogl G.W., Donmez M.A., and Archenti A. (2016) Diagnostics for geometric performance of machine tool linear axes. Annals of the CIRP 65(1):

10 IMU Data Collection Each run uses 3 different axis speeds IMU can live within machine tool for usage with no setup Moderate Slow Fast Speed Speed (but sped up in video) 10

11 NIST Linear Axis Testbed Testbed to study IMU-based method & diagnostics / root-cause analysis Error vs. Position Stage Stage 7 Stage 11 Stage Position (m) IMU Position (m) 0.25 REF 11

degradation stage 15 Roll Pitch IMU Yaw rad Damage area m) 10 ( 5 0

12 NIST Linear Axis Testbed Testbed to study IMU-based method & diagnostics / root-cause analysis Metric vs. degradation stage 15 Roll Pitch IMU Yaw rad Damage area m) 10 ( Roll Pitch REF Yaw rad Damage area m) 10 ( Degradation stage

13 Root-Cause Analysis for Rail Wear Find root cause of changing error motions 4 possible physical causes: inner/outer raceway damage on Rail 1 or 2 Root-cause analysis correctly identified spalling on inner raceway of Rail 1 13

14 IMU for Linear Axis Health Tracking Research Opportunities to use IMU for Comprehensive Root-Cause Analysis 14

15 GOAL: Smart Machine Tools Industry challenge: Machine health in 5 min? On-machine measurement science to diagnose performance and root-causes Offline Online Lack of periodic data Data-rich Expensive Inexpensive Linear Axis Health Tracking [How?] IMU Sensors Sensors Spindle Health Tracking [How?] TBD Squareness Health Tracking [How?] IMU 15

error motion > 2 µm and > 6 µrad Physical quantities can be measured Verify and validate If possible!

16 Lesson #1 Smart & Metrological Traceable Data is traceable to NIST Sensors calibrated along measurement chain to NIST Dimensional Results are physical quantities Inspired by international machining standards Tracking error motion > 2 µm and > 6 µrad Physical quantities can be measured Verify and validate If possible! Compare results to those from traceable independent reference Even complicated diagnostics can be shown to be correct Y Z X 16

Lesson #2 Smart & Simple Simple analytics

robust because tested more easily Easier

17 Lesson #2 Smart & Simple Simple analytics Can be explained and standardized More robust because tested more easily Easier to implement for great adoption Goal- or physics-based thresholds Simple user setup Plug and play solutions Vendor neutral for flexibility Spindle Metric ISO/TR :2014 D B C A Operation Time

Lesson #3 Future Directions Make smart machine tools with online, data-rich, and inexpensive diagnostics & prognostics of performance & root causes of faults/failures Predict part errors based on

18 Lesson #3 Future Directions Make smart machine tools with online, data-rich, and inexpensive diagnostics & prognostics of performance & root causes of faults/failures Predict part errors based on health tracking & optimize asset management Manufacturers also need MTConnect/Industry 4.0 networked factories, to enable actionable intelligence about system interactions/relationships Close-the-Loop Solutions from process to part using subsystem performance at each level (machine tools & robots final part inspection) Hyundai Wia Plant 18

Gregory W. Vogl Intelligent Systems Division gvogl@nist.

19 Gregory W. Vogl Intelligent Systems Division (301) NIST 100 Bureau Drive, MS 8230 Gaithersburg, MD Health Management, and Control

Smart Manufacturing and Standards: The NIST Role

Smart Manufacturing and Standards: The NIST Role Al Wavering, Chief Intelligent Systems Division Engineering Laboratory National Institute of Standards and Technology U.S. Department of Commerce October