A self-programming painting cell "Equation missing" SelfPaint"Equation missing" : Simulation-based path generation with automized quality control for painting in small lot sizes

Guettler, Nico; Sandgren, Niklas; Weber, Stefan; Knee, Philipp; Salman, Raad; Klier, Jens; Edelvik, Fredrik; Tiedje, Oliver

doi:10.1007/978-3-662-62962-8_35

Cited by 2 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…10. We refer to [19] for an explanation of the physical testing setup, while providing an overview of the results here. The graphs show that the optimization model has, based on the projection-based deposition model, provided a painting trajectory which gives a fairly uniform paint thickness close to the target of 50 μm.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Generating Optimized Trajectories for Robotic Spray Painting

Gleeson

Jakobsson²,

Salman

et al. 2022

IEEE Trans. Automat. Sci. Eng.

Self Cite

View full text Add to dashboard Cite

In the manufacturing industry, spray painting is often an important part of the manufacturing process. Especially in the automotive industry, the perceived quality of the final product is closely linked to the exactness and smoothness of the painting process. For complex products or low batch size production, manual spray painting is often used. But in large scale production with a high degree of automation, the painting is usually performed by industrial robots. There is a need to improve and simplify the generation of robot trajectories used in industrial paint booths. A novel method for spray paint optimization is presented, which can be used to smooth out a generated initial trajectory and minimize paint thickness deviations from a target thickness. The smoothed out trajectory is found by solving, using an interior point solver, a continuous non-linear optimization problem. A two-dimensional reference function of the applied paint thickness is selected by fitting a spline function to experimental data. This applicator footprint profile is then projected to the geometry and used as a paint deposition model. After generating an initial trajectory, the position and duration of each trajectory segment are used as optimization variables. The primary goal of the optimization is to obtain a paint applicator trajectory, which would closely match a target paint thickness when executed. The algorithm has been shown to produce satisfactory results on both a simple 2-dimensional test example, and a non-trivial industrial case of

show abstract

Section: Resultsmentioning

confidence: 99%

“…The trajectory optimization has been included as one of the modules of the self-programming painting cell. The full SelfPaint workflow [19] also includes earlier modules such as point cloud scanning with CAD model fitting, and later modules that include physics-based simulations as well as physical testing and verification.…”

Section: Problem Descriptionmentioning

confidence: 99%