Precision Assembly in the Digital Age

3D printing offers many advantages over conventional machining and its applications in industrial manufacturing is growing. However, existing additive technologies present limitations in workspace volume, accuracy and surface quality. These limitations could be overcome by combining both additive and subtractive processes. Such hybrid approaches allow layer-by-layer construction, alternating fast and rough material deposition with machining steps, when the layer’s geometry is finished. Despite its potential, the development and industrial application of hybrid machines is slow. Particularly, no systems exist for the construction of large parts. The project KRAKEN is wellsituated in this context, aiming at the development of a novel, fully automated, all-in-one platform for large volume hybrid manufacturing. This powerful tool will not only combine additive with subtractive processes, but it will also include both metal and non-metal 3D printing, resulting in a completely new machine for the construction of large, multi-material parts. A control approach based on direct measurement of the end-effector position will allow a combination of large workspace (up to 20 m) and high manufacturing accuracy (tolerances < 0.3 mm, surface roughness Ra < 0.1 μm). This paper presents the preliminary steps toward the development of this robotic platform, focusing on the use of the real-time feedback of an absolute laser tracker to control motion and positioning of the manufacturing robot. The proposed control strategy is presented and discussed. Finally, the use of an Extended Kalman Filter to fuse the laser measurement with the robot position sensors is presented and discussed based on offline evaluation.

[1]  Agostino Poggi,et al.  JADE - A Java Agent Development Framework , 2005, Multi-Agent Programming.

[2]  Bruno Siciliano,et al.  Modelling and Control of Robot Manipulators , 1997, Advanced Textbooks in Control and Signal Processing.

[3]  Jaime C. Fonseca,et al.  Off-Line Robot Programming Framework , 2005, Joint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services - (icas-isns'05).

[4]  José Luis Pons Rovira,et al.  Machine Vision and Applications Manuscript-nr. a Vision System Based on a Laser Range--nder Applied to Robotic Fruit Harvesting , 2022 .

[5]  Brian Logan,et al.  Evolvable Assembly Systems: A Distributed Architecture for Intelligent Manufacturing , 2015 .

[6]  Keum Shik Hong,et al.  PC-based off-line programming using VRML for welding robots in shipbuilding , 2004, IEEE Conference on Robotics, Automation and Mechatronics, 2004..

[7]  A. Tharumarajah,et al.  Comparison of the bionic, fractal and holonic manufacturing system concepts , 1996 .

[8]  Suresh P. Sethi,et al.  Flexibility in manufacturing: A survey , 1990 .

[9]  Euripides G. M. Petrakis,et al.  A survey on industrial vision systems, applications, tools , 2003, Image Vis. Comput..

[10]  Ralph Johnson,et al.  design patterns elements of reusable object oriented software , 2019 .

[11]  Thomas A. Fuhlbrigge,et al.  Automated industrial robot path planning for spray painting process: A review , 2008, 2008 IEEE International Conference on Automation Science and Engineering.

[12]  Andrew K. C. Wong,et al.  Robotic vision: 3D object recognition and pose determination , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[13]  Guilherme Galante,et al.  A Survey on Cloud Computing Elasticity , 2012, 2012 IEEE Fifth International Conference on Utility and Cloud Computing.

[14]  Samuel Kounev,et al.  Elasticity in Cloud Computing: What It Is, and What It Is Not , 2013, ICAC.

[15]  Nicholas R. Jennings,et al.  On agent-based software engineering , 2000, Artif. Intell..

[16]  Lutz Sommer,et al.  Industrial revolution - industry 4.0: Are German manufacturing SMEs the first victims of this revolution? , 2015 .

[17]  José Barbosa,et al.  Dynamic self-organization in holonic multi-agent manufacturing systems: The ADACOR evolution , 2015, Comput. Ind..

[18]  Luc Bongaerts,et al.  Reference architecture for holonic manufacturing systems: PROSA , 1998 .

[19]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[20]  Emanuel Ferreira Coutinho,et al.  Elasticity in cloud computing: a survey , 2014, annals of telecommunications - annales des télécommunications.

[21]  John Norrish,et al.  Recent Progress on Programming Methods for Industrial Robots , 2010, ISR/ROBOTIK.

[22]  Svetan Ratchev,et al.  A multi-agent framework for capability-based reconfiguration of industrial assembly systems , 2017, Int. J. Prod. Res..

[23]  Kagermann Henning Recommendations for implementing the strategic initiative INDUSTRIE 4.0 , 2013 .

[24]  Feng Wen,et al.  Rapid generation of spraying instructions for painting robot basing on automatic programming technology , 2014, 2014 IEEE International Conference on Mechatronics and Automation.

[25]  Liangyu Lei A machine vision system for inspecting bearing-diameter , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).