Novel calibration method for robotic bottom-up vat polymerization additive manufacturing systems

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Evan Rolland , Yasser Krim , Ahmed Joubair , Ilian A. Bonev , Evan Jones , Pengpeng Zhang , Cheng Sun , Nanzhu Zhao
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引用次数: 0

Abstract

This article presents a new affordable calibration method for a 7-axis robotic system used for vat polymerization 3D printing. The method employs three metrology elements: a calibration robot end-effector with three precision balls, a measurement probe composed of three linear gauges, and, notably, a kinematic coupling allowing the precise positioning of the probe onto the resin tank in three locations. The robotic system comprises a Mecademic Meca500 6-axis industrial robot mounted on a Zaber X-LRQ300AP linear guide. The calibration method consists of automatically aligning the centers of each of the three precision balls with the probe origin. This alignment is performed with different robot joint angles and linear guide displacements, and for all three locations of the probe. After calibration, the relative accuracy of the 7-axis robotic system with respect to the resin tank, as validated using a laser tracker, is improved from 1.272 mm to 0.271 mm, which is comparable to what can be achieved with significantly more expensive metrology equipment.
机器人自底向上还原聚合增材制造系统标定新方法
本文提出了一种用于还原聚合3D打印的7轴机器人系统的新的经济实惠的校准方法。该方法采用三个计量元件:一个带有三个精密球的校准机器人末端执行器,一个由三个线性量具组成的测量探头,值得注意的是,一个运动耦合允许探头在三个位置精确定位到树脂罐上。机器人系统包括安装在Zaber X-LRQ300AP直线导轨上的Meca500 6轴工业机器人。标定方法是将三个精密球的中心与测头原点自动对齐。这种校准是在不同的机器人关节角度和线性导轨位移下进行的,并且适用于探头的所有三个位置。校准后,使用激光跟踪器验证的7轴机器人系统相对于树脂罐的相对精度从1.272 mm提高到0.271 mm,这与使用更昂贵的计量设备所能达到的效果相当。
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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
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