Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Implementation of a robot control architecture for additive manufacturing applications

Implementation of a robot control architecture for additive manufacturing applications Additive manufacturing (AM) technologies have recently turned into a mainstream production method in many industries. The adoption of new manufacturing scenarios led to the necessity of cross-disciplinary developments by combining several fields such as materials, robotics and computer programming. This paper aims to describe an innovative solution for implementing robotic simulation for AM experiments using a robot cell, which is controlled through a system control application (SCA).Design/methodology/approachFor this purpose, the emulation of the AM tasks was executed by creating a robot working station in RoboDK software, which is responsible for the automatic administration of additive tasks. This is done by interpreting gcode from the Slic3r software environment. Posteriorly, all the SCA and relevant graphical user interface (GUI) were developed in Python to control the AM tasks from the RoboDK software environment. As an extra feature, Slic3r was embedded in the SCA to enable the generation of gcode automatically, without using the original user interface of the software. To sum up, this paper adds a new insight in the field of AM as it demonstrates the possibility of simulating and controlling AM tasks into a robot station.FindingsThe purpose of this paper is to contribute to the AM field by introducing and implementing an SCA capable of executing/simulating robotic AM tasks. It also shows how an advanced user can integrate advanced simulation technologies with a real AM system, creating in this way a powerful system for R&D and operational manufacturing tasks. As demonstrated, the creation of the AM environment was only possible by using the RoboDk software that allows the creation of a robot working station and its main operations.Originality/valueAlthough the AM simulation was satisfactory, it was necessary to develop an SCA capable of controlling the whole simulation through simple commands instructed by users. As described in this work, the development of SCA was entirely implemented in Python by using official libraries. The solution was presented in the form of an application capable of controlling the AM operation through a server/client socket connection. In summary, a system architecture that is capable of controlling an AM simulation was presented. Moreover, implementation of commands in a simple GUI was shown as a step forward in implementation of modern AM process controls. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Robot: The International Journal of Robotics Research and Application Emerald Publishing

Implementation of a robot control architecture for additive manufacturing applications

Download PDF
10 pages

Loading next page...
 
/lp/emerald-publishing/implementation-of-a-robot-control-architecture-for-additive-zLs7SSPmQ0
Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0143-991X
DOI
10.1108/ir-11-2018-0226
Publisher site
See Article on Publisher Site

Abstract

Additive manufacturing (AM) technologies have recently turned into a mainstream production method in many industries. The adoption of new manufacturing scenarios led to the necessity of cross-disciplinary developments by combining several fields such as materials, robotics and computer programming. This paper aims to describe an innovative solution for implementing robotic simulation for AM experiments using a robot cell, which is controlled through a system control application (SCA).Design/methodology/approachFor this purpose, the emulation of the AM tasks was executed by creating a robot working station in RoboDK software, which is responsible for the automatic administration of additive tasks. This is done by interpreting gcode from the Slic3r software environment. Posteriorly, all the SCA and relevant graphical user interface (GUI) were developed in Python to control the AM tasks from the RoboDK software environment. As an extra feature, Slic3r was embedded in the SCA to enable the generation of gcode automatically, without using the original user interface of the software. To sum up, this paper adds a new insight in the field of AM as it demonstrates the possibility of simulating and controlling AM tasks into a robot station.FindingsThe purpose of this paper is to contribute to the AM field by introducing and implementing an SCA capable of executing/simulating robotic AM tasks. It also shows how an advanced user can integrate advanced simulation technologies with a real AM system, creating in this way a powerful system for R&D and operational manufacturing tasks. As demonstrated, the creation of the AM environment was only possible by using the RoboDk software that allows the creation of a robot working station and its main operations.Originality/valueAlthough the AM simulation was satisfactory, it was necessary to develop an SCA capable of controlling the whole simulation through simple commands instructed by users. As described in this work, the development of SCA was entirely implemented in Python by using official libraries. The solution was presented in the form of an application capable of controlling the AM operation through a server/client socket connection. In summary, a system architecture that is capable of controlling an AM simulation was presented. Moreover, implementation of commands in a simple GUI was shown as a step forward in implementation of modern AM process controls.

Journal

Industrial Robot: The International Journal of Robotics Research and Application – Emerald Publishing

Published: Apr 12, 2019

Keywords: Robotics; Additive manufacturing; Additive manufacturing simulation

References

  • Chapter 6.7: Impressão 3D de peças metálicas: método numérico Para seleção dos parâmetros do processo
    Azar, A.S.; Pires, J.N.
  • Additive manufacturing of concrete in construction: potentials and challenges of 3D concrete printing
    Bos, F.; Wolfs, R.; Ahmed, Z.; Salet, T.
  • Additive manufacturing of polymer-derived ceramics
    Eckel, Z.C.; Zhou, C.; Martin, J.H.; Jacobsen, A.J.; Carter, W.B.; Schaedler, T.A.
  • Additive manufacturing by robot manipulator: an overview of the state-of-the-art and proof-of-concept results
    Evjemo, L.D.; Moe, S.; Gravdahl, J.T.; Roulet-Dubonnet, O.; Gellein, L.T.
  • Additive manufacturing technologies: state of the art and trends
    Gardan, J.
  • Concurrency in programming languages: a survey
    Ghezzi, C.
  • Additive manufacturing: technology, applications and research needs
    Guo, N.; Leu, M.C.
  • State of the art and emerging trends in additive manufacturing: from multi-material processes to 3D printed electronics
    Lehmhus, D.; Busse, M.; Von Hehl, A.; Jägle, E.
  • Robot programming
    Lozano-Perez, T.
  • Additive manufacturing of tissues and organs
    Melchels, F.P.; Domingos, M.A.; Klein, T.J.; Malda, J.; Bartolo, P.J.; Hutmacher, D.W.
  • Patents and the fourth industrial revolution: the inventions behind the digital transformation
    Ménière, Y.
  • Offline programming software for industrial robots from RoboDK offers hundreds of virtual industrial robots from top robotics companies
    Montaqim, A.
  • Additive manufacturing technologies: rapid prototyping to direct digital manufacturing
    Mueller, B.
  • Metal fabrication by additive manufacturing using laser and electron beam melting technologies
    Murr, L.E.; Gaytan, S.M.; Ramirez, D.A.; Martinez, E.; Hernandez, J.; Amato, K.N.; Shindo, P.W.; Medina, F.R.; Wicker, R.B.
  • PEP 371 – addition of the multiprocessing package to the standard library
    Noller, J.; Oudkerk, R.
  • The future of robot off-line programming
    Nubiola, A.
  • Rapid curing and additive manufacturing of thermoset systems using scanning microwave heating of carbon nanotube/epoxy composites
    Odom, M.G.; Sweeney, C.B.; Parviz, D.; Sill, L.P.; Saed, M.A.; Green, M.J.
  • Client-server model
    Oluwatosin, H.S.
  • PEP 20 – The zen of Python
    Peters, T.
  • Force control experiments for industrial applications: a test case using an industrial deburring example
    Pires, J.N.
  • HYROMAN – Hybrid robotic additive HYROMAN – Hybrid robotic additive manufacturing platform for agile production of large multi-metal components
    Pires, J.N.; Azar, A.S.
  • 3D printing of large metallic parts
    Pires, J.N.; Azar, A.S.
  • Advances in robotics for additive/hybrid manufacturing: robot control, speech interface and path planning
    Pires, J.N.; Azar, A.S.
  • Welding robots: technology, system issues and applications
    Pires, J.N.
  • RobotDK API – robolink module
  • PEP 3156 – asynchronous IO support rebooted
    Rossum, G.V.
  • Simulation of the laser beam melting process–approaches for an efficient modelling of the beam-material interaction
    Seidel, C.; Zaeh, M.; Wunderer, M.; Weirather, J.; Krol, T.; Ott, M.
  • Stack overflow developer survey 2018
  • Metallic powder-bed based 3D printing of cellular scaffolds for orthopaedic implants: a state-of-the-art review on manufacturing, topological design, mechanical properties and biocompatibility
    Tan, X.; Tan, Y.; Chow, C.; Tor, S.; Yeong, W.
  • Highly oriented carbon fiber–polymer composites via additive manufacturing
    Tekinalp, H.L.; Kunc, V.; Velez-Garcia, G.M.; Duty, C.E.; Love, L.J.; Naskar, A.K.; Blue, C.A.; Ozcan, S.
  • Precision additive metal manufacturing
    Witvrouw, A.; Metelkova, J.; Ranjan, R.; Bayat, M.; De, D.; Baere, M.M.; Tosello, G.; Solheid, J.; Charles, A.; Scholz, S.
  • The socket programming and software design for communication based on client/server
    Xue, M.; Zhu, C.