PACMAN Error Inventory OUTLINE The PACMAN task The Inventory of contributors The Evaluation Method Critical Areas Outcome 08/10/2015 by Iordan Doytchinov

To define: axis with respect reference in common coordinate system The Measurement Tasks 2 To define: a) MBQ Magnetic b) BPM Electric axis with respect to the MBQ assembly reference (the WPS fiducials) in the common coordinate system defined by a CMM. = Target: Ucm = 12µm Target: Uce = 7µm d’

X,Y,Z WPS MBQ + axis The Measurement Tasks X,Y Z X,Y,Z Wire + linear stages

Sense magnetic OR electric field Register coordinates of stretched wire The measurement process 4 X Y Z Reference Fiducial reference frame Change of CMM Tool Evaluate CMM reference frame + Wire

Evaluation of Axis - Error Inventory 5 Evaluate CMM reference frame Sensorial System Environment Magnet Factors Method factors Data Analysis Uc > MPEE Leitz

Error Inventory – Sensorial System 6 Sensorial System Wire type and parameters uncertainty (CTE, R,E) Ucte = 0.6 µm Square Shape Signal/current generator waveform uncertainty µA Wire vibration Detection sensors Uncertainty 0.3 – 0.5 microns Gaussian Direct: CMM + None contact sensor to wire Indirect: Linear stages registration + CMM + calibration Sensors alignment errors Registration method Sensors repeatability Coordinate Registration X Y Z

Direct: CMM + none contact 7 Direct: CMM + None contact sensor to wire Environment Wire Factors – 0.4 µm Square shape Measurement Strategy Sampling Strategy 21 rigid body errors Quasi Static Errors Dynamics Errors CMM Scales resolution Stability drift Numbers and location of measurement points 0.05 up to 0.5? Square Number of coordinates registered Spinning none contact axis sensor CMM Geometry Errors

Direct: CMM + none contact 8 Environment Spinning none contact axis sensor Rotated sensor type uncertainty 0.3 µm Gaussian Measurement mode Calibration Strategy Air bearing runout error 0.1 µm Gaussian Air bearing runout error 0.1 µm Gaussian Repeatability Environment Calibration Uncertainty PSU stability Repeatability of the system 0.5 µm Sampling strategy/Filtering of data Vibration Spectrum Thermal effects on CMM and on none contact sensor Lighting Ventilation of the room PSU variations

Indirect 9 Indirect: Linear stages registration + CMM + calibration Linear stages registration Calibration Environment Sampling Strategy Wire stretchers rigid body Errors: 2x Stretchers 13 Parameters each Quasi static Errors Scales resolution 0.05 µm Square shape CMM + None contact sensor to wire Thermal Effects due to dT Vibration Spectrum Drift Number of coordinates registered To Wire stretchers To wire CMM + Tactile sensor Change of tool Calibration

Indirect 10 Sensorial System Coordinate Registration Method Wire type and parameters uncertainty (form error?, CTE, R,E) 0.6 µm Square Shape Wire type and parameters uncertainty (form error?, CTE, R,E) 0.6 µm Square Shape Signal/current generator waveform uncertainty µA Wire vibration Detection sensors Uncertainty 0.3 – 0.5 microns Gaussian Direct: CMM + None contact sensor continuous Indirect: Linear stages registration + CMM Sensors alignment errors Registration method Sensors X Y Z X Y

Indirect VS Direct 11 Indirect Direct + High measurement speed = lower drift Benefit from wire stretchers higher resolution _ Calibration with the use of the CMM with both None contact and Contact probes + No Calibration required Direct registration in the coordinate frame of the CMM _ Significant measurment time increase = larger drift

Environment 12 Environment Atmospheric pressure Fiducials drift WRS magnet axis due dT Power variation of magnet PSU Ventilation of the room below 0.1 m/s “not sensitive to wire” Ventilation of the room below 0.1 m/s “not sensitive to wire” PSU variations Humidity Vibration – 0.4 nm Square shape

Magnet, Method and Data Analysis Factors 13 Magnet Factors Repeatability of axis returning to the same location µm Square shape Axis distortion due to assembly, disassembly - transport Errors due to bad magnet assembly Method factors Number of tension variation iterations Data Analysis Standard deviation of single measurement to least square best fit to theoretical model Extrapolation to infinite tension uncertainty µm Gaussian

Traceable Change of tool 14 Kinematic coupling uncertainty µm Change of CMM Tool Calibration for traceable shift of reference frame due to sensor type

CMM with Tactile probe 15 Reference Fiducial reference frame U> MPEE? dT due Concentrated heat source Magnetisation effects

Method 16  To Create Monte Carlo Model representing the measurement process in MATLAB. To start a sensitivity study with the current state of art knowledge of the uncertainties and their PDE’s (type B currently). To do that by inputting MIN to MAX in order to see influence at final uncertainty.  To update information in critical areas by Type A analysis and by performing experiments. Example: To use low CTE artefact as reference during continuous evaluation of the magnet assembly fiducials drift with magnet. Min -Max First measurement model architecture example showed in the excel file

Critical Areas to evaluate 17 Most Critical:  To establish traceable uncertainty of the change of tool coordinate registration.  To evaluate the repeatability of the magnetic axis shift between 10% - 100% of nominal power (distinguished from the wire stretchers repetability)  Measurement strategy greatly influences the uncertainty!!!!!! dt, Number of wire evaluations… NOTE!  Drift within assembly measurement frame (fiducials VS magnet axis!) – f(dt, vibration,…)! Currently the CLIC assembly bench does not have active metrology frame!  Assumptions due to method such as (extrapolation to infinite tension uncertainty)

Outcome 18 Most Critical:  Monte Carlo Script to be started from next week  Possible first artefact for CMM uncertainty study available by the end of October  To organize as soon as possible thermal study of the magnet  To organize as soon as possible drift study of magnet and fiducial assembly within the CMM  To work continuously with other students – system owners do open their uncertainty black boxes – to develop experiments

Than You 19