Serial robotic plasma additive manufacturing on complex NURBS surface

IF 1.9 4区 计算机科学 Q3 ENGINEERING, INDUSTRIAL Industrial Robot-The International Journal of Robotics Research and Application Pub Date : 2022-09-13 DOI:10.1108/ir-04-2022-0097
Zhaoqin Wang, Yu Shi, Xiaorong Wang
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Abstract

Purpose This paper aims to investigate the additive manufacturing (AM) approach of a spatial complex curve feature (SCCF, mapped from two-dimensional nonuniform rational B-splines [2D-NURBS] curve) on a complex surface based on a serial robot using plasma built-up welding, and lays a foundation for plasma AM SCCFs on complex surfaces by combining the NURBS theory with the serial robotic kinematics. Design/methodology/approach Combining serial robotic kinematics and NURBS theory, a SCCF mapped from a square-like 2D-NURBS curve is prepared on a predefined complex NURBS surface using serial robotic plasma AM. The interpolation points C (ui) on the square-like 2D-NURBS curve are obtained using the equi-chord length interpolation method, and mapped on a predefined NURBS surface to get mapped points S (ui, vj). The homogeneous transformation matrix T = [n o a S (ui, vj)] of the plasma torch is calculated using the mapped points S (ui, vj) and the designated posture [n o a]. Using the inverse kinematics of the serial robot, the joint vector θ of the serial robot can be computed. After that, the AM programs are generated and transferred into the serial robotic controller and carried out by the serial robot of Motoman-UP6. The 2D-NURBS curve (square-like) is considered as AM trajectory planning curve, while its corresponding SCCF mapped from the 2D-NURBS curve as AM trajectory. Findings Simulation and experiments show that the preparation of SCCF (mapped from 2D-NURBS curve) on complex NURBS surface using robotic plasma AM is feasible and effective. Originality/value A SCCF mapped from a 2D-NURBS curve is prepared on a complex NURBS surface using the serial robotic plasma AM for the first time. It provides a theoretical and technical basis for plasma AM to produce SCCFs on complex surfaces. With the increasing demand for surface remanufacturing of complex parts, the serial robotic plasma AM of SCCFs on complex NURBS surfaces has a broad application prospect in aero-engine components, high-speed rail power components, nuclear industry components and complex molds.
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复杂NURBS表面连续机器人等离子体增材制造
目的研究基于等离子体堆焊串联机器人的复杂曲面空间复杂曲线特征(SCCF,从二维非均匀有理b样条曲线[2D-NURBS]曲线映射)的增材制造方法,并将NURBS理论与串联机器人运动学相结合,为复杂曲面等离子体增材制造SCCF奠定基础。设计/方法/方法结合连续机器人运动学和NURBS理论,利用连续机器人等离子体增材制造技术在预定义的复杂NURBS曲面上制备了由方形2D-NURBS曲线映射的SCCF。采用等弦长插值方法在类方形2D-NURBS曲线上获得插值点C (ui),并将其映射到预定义的NURBS曲面上,得到映射点S (ui, vj)。利用映射点S (ui, vj)和指定姿态[n o a]计算等离子炬的齐次变换矩阵T = [n o a S (ui, vj)]。利用串联机器人的运动学逆解,可以计算出串联机器人的关节向量θ。然后生成调幅程序,并将其传输到串行机器人控制器中,由Motoman-UP6串行机器人执行。将2D-NURBS曲线(方形)视为AM轨迹规划曲线,其对应的SCCF从2D-NURBS曲线映射为AM轨迹。仿真和实验结果表明,利用机器人等离子体增材制造复杂NURBS曲面上的SCCF (2D-NURBS曲线映射)是可行和有效的。首次使用串行机器人等离子体AM在复杂的NURBS表面上制备了从2D-NURBS曲线映射的SCCF。为等离子体增材制造复杂表面上的sccf提供了理论和技术基础。随着复杂零件表面再制造需求的不断增加,在复杂NURBS表面上连续机器人等离子体增材制造sccf在航空发动机部件、高铁动力部件、核工业部件和复杂模具等领域具有广阔的应用前景。
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来源期刊
CiteScore
4.50
自引率
16.70%
发文量
86
审稿时长
5.7 months
期刊介绍: Industrial Robot publishes peer reviewed research articles, technology reviews and specially commissioned case studies. Each issue includes high quality content covering all aspects of robotic technology, and reflecting the most interesting and strategically important research and development activities from around the world. The journal’s policy of not publishing work that has only been tested in simulation means that only the very best and most practical research articles are included. This ensures that the material that is published has real relevance and value for commercial manufacturing and research organizations. Industrial Robot''s coverage includes, but is not restricted to: Automatic assembly Flexible manufacturing Programming optimisation Simulation and offline programming Service robots Autonomous robots Swarm intelligence Humanoid robots Prosthetics and exoskeletons Machine intelligence Military robots Underwater and aerial robots Cooperative robots Flexible grippers and tactile sensing Robot vision Teleoperation Mobile robots Search and rescue robots Robot welding Collision avoidance Robotic machining Surgical robots Call for Papers 2020 AI for Autonomous Unmanned Systems Agricultural Robot Brain-Computer Interfaces for Human-Robot Interaction Cooperative Robots Robots for Environmental Monitoring Rehabilitation Robots Wearable Robotics/Exoskeletons.
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