A Robot-Driven 3D Shape Measurement System for Automatic Quality Inspection of Thermal Objects on a Forging Production Line

The three-dimensional (3D) geometric evaluation of large thermal forging parts online is critical to quality control and energy conservation. However, this online 3D measurement task is extremely challenging for commercially available 3D sensors because of the enormous amount of heat radiation and complexity of the online environment. To this end, an automatic and accurate 3D shape measurement system integrated with a fringe projection-based 3D scanner and an industrial robot is presented. To resist thermal radiation, a double filter set and an intelligent temperature control loop are employed in the system. In addition, a time-division-multiplexing trigger is implemented in the system to accelerate pattern projection and capture, and an improved multi-frequency phase-shifting method is proposed to reduce the number of patterns required for 3D reconstruction. Thus, the 3D measurement efficiency is drastically improved and the exposure to the thermal environment is reduced. To perform data alignment in a complex online environment, a view integration method is used in the system to align non-overlapping 3D data from different views based on the repeatability of the robot motion. Meanwhile, a robust 3D registration algorithm is used to align 3D data accurately in the presence of irrelevant background data. These components and algorithms were evaluated by experiments. The system was deployed in a forging factory on a production line and performed a stable online 3D quality inspection for thermal axles.

[1]  Shijie Feng,et al.  High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection , 2013 .

[2]  G. Tan,et al.  A review of thermoelectric cooling: Materials, modeling and applications , 2014 .

[3]  Zhenyuan Jia,et al.  An improved online dimensional measurement method of large hot cylindrical forging , 2012 .

[4]  Ken Young,et al.  Accuracy assessment of the modern industrial robot , 2000 .

[5]  Sheng Liu,et al.  In Situ 3D Monitoring of Geometric Signatures in the Powder-Bed-Fusion Additive Manufacturing Process via Vision Sensing Methods , 2018, Sensors.

[6]  Yu-cun Zhang,et al.  An online measurement method based on line laser scanning for large forgings , 2014 .

[7]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[8]  Xiang Peng,et al.  Calibration of fringe projection profilometry with bundle adjustment strategy. , 2012, Optics letters.

[9]  Z. Du,et al.  Simple three-dimensional laser radar measuring method and model reconstruction for hot heavy forgings , 2012 .

[10]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Frank-Thomas Lentes,et al.  Blue glass lens elements used as IR cut filter in a camera design and the impact of inner quality onto lens performance , 2012, Optical Systems Design.

[12]  Qian Chen,et al.  Phase shifting algorithms for fringe projection profilometry: A review , 2018, Optics and Lasers in Engineering.

[13]  George Michalos,et al.  A Machine Learning Approach for Visual Recognition of Complex Parts in Robotic Manipulation , 2017 .

[14]  George Chryssolouris,et al.  Design and simulation of assembly systems with mobile robots , 2014 .

[15]  Zhongwei Li,et al.  Accurate calibration method for a structured light system , 2008 .

[16]  Ilian A. Bonev,et al.  Assessment of the positioning performance of an industrial robot , 2012, Ind. Robot.

[17]  Pavel Krsek,et al.  The Trimmed Iterative Closest Point algorithm , 2002, Object recognition supported by user interaction for service robots.

[18]  Youssef Bokhabrine,et al.  3D characterization of hot metallic shells during industrial forging , 2010, Machine Vision and Applications.

[19]  Zhengchun Du,et al.  Measurement system for hot heavy forgings and its calibration , 2011, Optical Metrology.

[20]  Zhang Huayu,et al.  Measuring the 3D shape of high temperature objects using blue sinusoidal structured light , 2015 .

[21]  Sotiris Makris,et al.  An approach for implementing power and force limiting in sensorless industrial robots , 2018 .

[22]  Zhengyuan Jia,et al.  Fast dimensional measurement method and experiment of the forgings under high temperature , 2011 .

[23]  Yu-cun Zhang,et al.  Measurement and control technology of the size for large hot forgings , 2014 .

[24]  Feng Gao,et al.  Dimension measurement of hot large forgings with a novel time-of-flight system , 2009 .

[25]  Zhenyuan Jia,et al.  An improved image acquisition method for measuring hot forgings using machine vision , 2016 .

[26]  Tomasz Trzepieciński,et al.  The repeatability positioning analysis of the industrial robot arm , 2014 .

[27]  A. Boubaris,et al.  Experimental survey on active thermoelectric cooling driven by PWM techniques , 2017, 2017 Panhellenic Conference on Electronics and Telecommunications (PACET).