Integrated remote control of the process capability and the accuracy of vision calibration

Abstract This study aims at jointly controlling two critical process parameters from a remote site, of which include the process capability of robotic assembly operations and the accuracy of vision calibration. The process capability is regarded as the indication of robot positioning accuracy. When the robot is driven by the vision camera, the process capability becomes mainly dependent on the calibration accuracy of vision-guided robot system. Even though newly commissioned, high precision assembly robots typically display excellent positioning accuracies under normal working conditions, the imperfect mathematical conversion of vision coordinates into robot coordinates imparts the accuracy problems. In this study, a novel vision calibration method is proposed that effectively rectifies the inherent complications associated with lens distortions. Our analysis shows that the degree of lens distortion appears very differently along the vision field of view. Because of this non-uniform distortion, a single mathematical equation for vision calibration is deemed ineffective. The proposed methodology significantly improves the positioning accuracy, which can be performed over the network from a remote site. This is better suited for today׳s global manufacturing companies, where fast product cycles and geographically distributed production lines dictate more efficient and effective quality control strategies.

[1]  Samuel Kotz,et al.  An overview of theory and practice on process capability indices for quality assurance , 2009 .

[2]  Soichi Ibaraki,et al.  Graphical presentation of error motions of rotary axes on a five-axis machine tool by static R-test with separating the influence of squareness errors of linear axes , 2012 .

[3]  W. L. Pearn,et al.  One‐sided Process Capability Assessment in the Presence of Measurement Errors , 2006, Qual. Reliab. Eng. Int..

[4]  Yongjin Kwon,et al.  Improvement of vision guided robotic accuracy using Kalman filter , 2013, Comput. Ind. Eng..

[5]  Soichi Ibaraki,et al.  Calibration of location errors of rotary axes on five-axis machine tools by on-the-machine measurement using a touch-trigger probe , 2012 .

[6]  Zachary G. Stoumbos Process capability indices: overview and extensions , 2002 .

[7]  Tien-Fu Lu,et al.  An on-line relative position and orientation error calibration methodology for workcell robot operations , 1997 .

[8]  Lynn Huh,et al.  Kalman Filter for Beginners: with MATLAB Examples , 2011 .

[9]  David Avishay,et al.  Review: Designing and testing a calibrating procedure for combining the coordination systems of a handling robot and a stationed video camera , 2011 .

[10]  Harshal A. Chavan,et al.  TOLERANCE STACK UP ANALYSIS AND SIMULATION USING VISUALIZATION VSA , 2011 .

[11]  Richard Chiou,et al.  Java programming for online quality control laboratory integrated with remote robot , 2008 .

[12]  Ping Zhang,et al.  Online robot calibration based on vision measurement , 2013 .

[13]  Shreepud Rauniar,et al.  E-Quality for Manufacturing (EQM) Within the Framework of Internet-Based Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[14]  J. Santolaria,et al.  Uncertainty estimation in robot kinematic calibration , 2013 .

[15]  Yongjin Kwon,et al.  Enhancing e-quality for manufacture using Kalman Filter calibrated visual robotic control , 2011 .

[16]  Dragan Milutinovic,et al.  Universal compliant device based on SCARA concept , 1997 .

[17]  Yongjin Kwon,et al.  Sensor-based Remote Quality Control Application in Automotive Components Assembly , 2010, Concurr. Eng. Res. Appl..

[18]  Davood Shishebori,et al.  PROPERTIES OF MULTIVARIATE PROCESS CAPABILITY IN THE PRESENCE OF GAUGE MEASUREMENT ERRORS AND DEPENDENCY MEASURE OF PROCESS VARIABLES , 2010 .

[19]  W. L. Pearn,et al.  Measuring process capability based on CPK with gauge measurement errors , 2005, Microelectron. Reliab..

[20]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .

[21]  Nathan R. Soderborg,et al.  Design for Six Sigma at Ford , 2004 .

[22]  Nasreddin Dhafr,et al.  Improvement of quality performance in manufacturing organizations by minimization of production defects , 2006 .

[23]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[24]  Hanqi Zhuang,et al.  Autonomous robot calibration using vision technology , 2007 .