Post on 16-May-2020
Proceedings of the 1st Iberic Conference on Theoretical and Experimental Mechanics and Materials /
11th National Congress on Experimental Mechanics. Porto/Portugal 4-7 November 2018.
Ed. J.F. Silva Gomes. INEGI/FEUP (2018); ISBN: 978-989-20-8771-9; pp. 739-744.
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PAPER REF: 7375
BIM-BASED ROBOTIC WELDING SYSTEM CAPABLE OF DEALING
WITH SMALL BATCHES OF STRUCTURAL STEEL ASSEMBLIES
Vítor Ferreira1(*)
, Paulo J. Morais1, Helena Gouveia
1, Margarida Pinto
1, Luís Rocha
2, Germano Veiga
2,
Pedro Malaca3, José Oliveira
3, José Pinto
4, José Melo
4, Nuno Oliveira
4
1ISQ - Instituto de Soldadura e Qualidade, Av. Prof. Dr. Cavaco Silva 33, 2740-120 Porto Salvo, Portugal 2INESC-TEC, R. Dr. Roberto Frias, 4200-465 Porto, Portugal 3Sarkkis Robotics, Rua Alfredo Allen 461, 4200-135 Porto, Portugal 4Norfersteel, R. Comendador Arlindo Soares de Pinho 1910, 3730-901 Vale de Cambra, Portugal (*)
Email: vmferreira@isq.pt
ABSTRACT
The CoopWeld project combined robots, sensors and automatic offline programming to
successfully achieve a robotic welding system capable of dealing with frequent small batches
and one-off productions in the fabrication of structural steel assemblies, while minimizing
input from robotic cell operators.
Keywords: BIM, robotic welding, offline programming, projection mapping, sensors, seam
finding, seam tracking.
INTRODUCTION
The vast majority of traditional robotic welding applications, such as found in the automotive
industry for either arc or spot welding, rely heavily on the use of welding jigs and fixtures for
the repetitive and accurate positioning of parts and joints. Therefore, they do not require
sensing devices to locate joints or perform seam tracking. Thanks to large batch productions
of identical items, they allow for economically viable online and offline programming.
More recently, the right combination of robots, sensors and automatic offline programming
has begun to prove itself to be effective in dealing with production factors that traditionally
prevented the use of robots for welding small-batch structural assemblies. However, most
well-proven systems are conceptually sophisticated, expensive and aimed at high-level end-
users such as large and state-of-the-art shipyards.
The CoopWeld project succeeded in developing a low-cost robotic welding system capable of
dealing with frequent small batches and one-off productions through automatic offline
programming, minimizing input from robotic cell operators. The robotic system is aimed at
SMEs and limited to well-characterized and standardized non-generic product families. More
specifically, the system has been designed for the prefabrication of welded steel assemblies
such as pillars, roof beams and end frame beams used in the construction of industrial steel
buildings and warehouses (Figure 1).
Track-E: Civil and Structural Engineering Applications
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Fig. 1 - Typical assemblies welded in the CoopWeld robotic cell.
DESCRIPTION
Robot path generation is done through automatic offline programming using assembly models
produced in a BIM-enabled 3D CAD software (e.g. Tekla Structures). Besides providing an
accurate virtual geometry and virtual collision detection, these information-rich models
contain process-specific information needed to support fabrication, such as welding-related
data (Figure 2).
Fig. 2 - The CoopWeld robotic cell features a gantry-mounted welding robot on a long linear track
and a flipper beam rotator.
Proceedings TEMM2018 / CNME2018
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Unlike some costly high-end systems, CoopWeld relies on collaborative interaction with
human operators for the fit-up of the steel assemblies to be welded as well as for validating
the proposed welding sequence and parameters. An intuitive human-machine interface (HMI)
is an essential feature of the CoopWeld robotic cell, allowing operators to validate
automatically generated robot paths (Figure 3).
Fig. 3 - Human-machine interface displaying robot path generation for a structural steel assembly.
A decision support system for setting all relevant welding process parameters is fully
integrated with the main HMI, allowing to deal with the different weld joint configurations
and preparation details that are typically found in actual fabrication. MAG (Metal Active Gas)
welding is the natural and obvious choice regarding welding process technology to be
employed in robotic welding of structural steel assemblies. Therefore, suitable values for
parameters such as wire feed speed, welding speed and possibly also torch oscillation must be
automatically generated for each and every joint, as dictated by design specifications, material
thickness and welding position (Figure 4).
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Fig. 4 - Welding parameters are automatically generated by a dedicated tool integrated with the
main human-machine interface.
Projection mapping using a laser mounted on the robot gantry frame assists the positioning
and tack welding operations that must be performed manually by a welder during fit-up, prior
to the robotic welding. Laser-generated lines and contour shapes are projected on the surfaces
of the main structural section, providing a valuable visual aid for the location of all the
different parts that should be manually tack welded onto the section. Projection mapping not
only saves a great deal of time during fit-up, it also provides a means to increase general
accuracy and process reliability (Figure 5).
Fig. 5 - Projection mapping system at work, showing the location of a stiffener on the main
structural section.
Proceedings TEMM2018 / CNME2018
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Advanced sensors are integrated with design data to adaptively handle geometric variations.
Laser-based sensing is used to calibrate the cell for each new steel assembly and also to
perform joint location prior to welding. During the welding itself, through-the-arc seam
tracking (TAST) further compensates for part misplacement and warping (Figures 6 and 7).
Fig. 6 - The laser-based seam finding system uses 3D search routines to acquire the actual positioning of
manually tack welded parts, ensuring the weld beads are precisely deposited in the joints during robotic
final welding.
Track-E: Civil and Structural Engineering Applications
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Fig. 7 - Weld joint details of a stiffener welded to the main section.
CONCLUSION
While introducing robotics in production environments that traditionally rely on manual
welding, the cost-wise CoopWeld cell addresses the budget limitations often found in SMEs
concerned with fabrication and erection of structural steel.
The overall concept applies to a variety of well-characterized and standardized steel product
families, such as beams and columns for building structures, casings for medium- and high-
voltage transformers, small assemblies for shipbuilding, etc.
ACKNOWLEDGMENTS