Evaluation of Techniques to Capture Nominal Data for Verification and Kinematic Calibration of Articulated Arm Coordinate Measuring Machines

2009 ◽  
Author(s):  
Jorge Santolaria ◽  
Agustín Brau ◽  
Francisco Javier Brosed ◽  
Juan José Aguilar ◽  
Vicente Jesus Segui
Keyword(s):  
Kinematic Calibration ◽  
Coordinate Measuring Machines ◽  
Nominal Data

Evaluation Of Different Probing Systems Used In Articulated Arm Coordinate Measuring Machines

2014 ◽  
Vol 21 (2) ◽  
pp. 233-246 ◽  
Author(s):  
Agustín Brau ◽  
Margarita Valenzuela ◽  
Jorge Santolaria ◽  
Juan José Aguilar
Keyword(s):  
Parameter Identification ◽  
Kinematic Parameter ◽  
Coordinate Measuring Machines ◽  
Nominal Data ◽  
Measurement Volume ◽  
Spherical Probe ◽  
Active Probe ◽  
System Configurations ◽  
Accuracy And Repeatability ◽  
Kinematic Parameter Identification

Abstract This paper presents a comparison of different techniques to capture nominal data for its use in later verification and kinematic parameter identification procedures for articulated arm coordinate measuring machines (AACMM). By using four different probing systems (passive spherical probe, active spherical probe, self-centering passive probe and self-centering active probe) the accuracy and repeatability of captured points has been evaluated by comparing these points to nominal points materialized by a ball-bar gauge distributed in several positions of the measurement volume. Then, by comparing these systems it is possible to characterize the influence of the force over the final results for each of the gauge and probing system configurations. The results with each of the systems studied show the advantages and original accuracy obtained by active probes, and thus their suitability in verification (active probes) and kinematic parameter identification (self-centering active probes) procedures.

Sebaran Peluang Acak Kontinu, Distribusi Normal, Distribusi Normal Baku, Distribusi T, Distribusi Chi Square, dan Distribusi F

2020 ◽  
Author(s):  
Ahmad Sudi Pratikno
Keyword(s):  
Hypothesis Test ◽  
Standard Normal Distribution ◽  
Process Data ◽  
Nominal Data ◽  
Chi Square ◽  
Stable Curve ◽  
Standard Normal ◽  
Research Findings ◽  
F Distribution ◽  
T Distribution

In statistics, there are various terms that may feel unfamiliar to researcher who is not accustomed to discussing it. However, despite all of many functions and benefits that we can get as researchers to process data, it will later be interpreted into a conclusion. And then researcher can digest and understand the research findings. The distribution of continuous random opportunities illustrates obtaining opportunities with some detection of time, weather, and other data obtained from the field. The standard normal distribution represents a stable curve with zero mean and standard deviation 1, while the t distribution is used as a statistical test in the hypothesis test. Chi square deals with the comparative test on two variables with a nominal data scale, while the f distribution is often used in the ANOVA test and regression analysis.

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