Math-Based Algorithms and Software for Virtual Product Realization Implemented in Automotive Paint Shops

Edelvik, Fredrik; Mark, Andreas; Karlsson, Nicklas; Johnson, Tomas; Carlson, Johan S.

doi:10.1007/978-3-319-63957-4_11

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…The software has been previously used to e.g. efficiently simulate robot application of sealing material and adhesives on automotive industry [49][50][51][52].…”

Section: Methodsmentioning

confidence: 99%

Simulations of 3D bioprinting: predicting bioprintability of nanofibrillar inks

et al. 2018

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3D bioprinting with cell containing bioinks show great promise in the biofabrication of patient specific tissue constructs. To fulfil the multiple requirements of a bioink, a wide range of materials and bioink composition are being developed and evaluated with regard to cell viability, mechanical performance and printability. It is essential that the printability and printing fidelity is not neglected since failure in printing the targeted architecture may be catastrophic for the survival of the cells and consequently the function of the printed tissue. However, experimental evaluation of bioinks printability is time-consuming and must be kept at a minimum, especially when 3D bioprinting with cells that are valuable and costly. This paper demonstrates how experimental evaluation could be complemented with computer based simulations to evaluate newly developed bioinks. Here, a computational fluid dynamics simulation tool was used to study the influence of different printing parameters and evaluate the predictability of the printing process. Based on data from oscillation frequency measurements of the evaluated bioinks, a full stress rheology model was used, where the viscoelastic behaviour of the material was captured. Simulation of the 3D bioprinting process is a powerful tool and will help in reducing the time and cost in the development and evaluation of bioinks. Moreover, it gives the opportunity to isolate parameters such as printing speed, nozzle height, flow rate and printing path to study their influence on the printing fidelity and the viscoelastic stresses within the bioink. The ability to study these features more extensively by simulating the printing process will result in a better understanding of what influences the viability of cells in 3D bioprinted tissue constructs.

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“…The software has been previously used to e.g. efficiently simulate robot application of sealing material and adhesives on automotive industry [49][50][51][52].…”

Section: Methodsmentioning

confidence: 99%

Simulations of 3D bioprinting: predicting bioprintability of nanofibrillar inks

et al. 2018

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“…1), which are fully implemented in the software IPS Virtual Paint developed and maintained by Fraunhofer-Chalmers Centre. [3,4] The simulations require a virtual representation of the painting cell, which is why the geometry of the painting booth with relevant system components, as well as the CAD model of the workpiece are loaded first. To generate an initial path a so-called raster plane method is utilized, which is based on a meander (correct word?)…”

Section: Modules 2 and 3: Path Generation And Paintingmentioning

confidence: 99%

“…4d). This method is based on a multi-physics simulation that couples the fluid dynamics, electrostatics and motion of charged paint droplets [3]. To drastically reduce the computational effort and computing time for this simulation, the Near-Bell results stored in the database are utilized as input.…”

Section: Modules 2 and 3: Path Generation And Paintingmentioning

confidence: 99%

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

et al. 2021

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The increasing diversity of products and variants requires a flexible and fast path generation in robot-based painting processes. In the state of the art, path generation in the painting industry is a time consuming and cost-intensive iteration process in which the generated paths are evaluated and optimized via painting trials. In this paper, we present a novel concept for a self-programming painting cell, which is based on the key technologies 3D-scanning, multi-physics painting simulations, and a contactless film thickness measurement using terahertz technology. The core element of this cyber-physical painting system is a unique combination of numerical painting simulations with a gradientbased multi-objective optimization method, to virtually compute painting paths that produce a homogeneous thickness on the painted object. In order to drastically reduce the time and computationally intensive numerical fluid dynamic simulations, a step-by-step coupling of an offline and online simulation was implemented. In a final step, a collision free robot motion without singularities is generated automatically from the computed painting path. The concept was validated under pilot plant conditions by the painting of a fender using an electrostatically assisted high-speed rotary bell atomizer. The paint film thickness, measured with terahertz technology was used as the target and validation criterion, as it shows a strong correlation to other quality values. The results show that the achieved film thickness was within the process specification, although deviations between simulated and measured film thicknesses were found in the edge zones of the workpiece.

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“…Paint deposition modelling is a field which has received attention from both industry and academia, and multiple different models have been developed and are used in different settings. These models include simple cover checking, projection methods using for example a composite Gaussian formulation [5], or even full dynamic simulation of electrically charged paint droplets dispersed from a rotary bell nozzle [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

Generating Optimized Trajectories for Robotic Spray Painting

Gleeson

Jakobsson²,

Salman

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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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

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Math-Based Algorithms and Software for Virtual Product Realization Implemented in Automotive Paint Shops

Cited by 6 publications

References 16 publications

Simulations of 3D bioprinting: predicting bioprintability of nanofibrillar inks

Simulations of 3D bioprinting: predicting bioprintability of nanofibrillar inks

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

Generating Optimized Trajectories for Robotic Spray Painting

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