Numerical design of distributive mixing elements

Hube, Sebastian; Behr, Marek; Elgeti, Stefanie; Schön, Malte; Sasse, Jana; Hopmann, Christian

doi:10.1016/j.finel.2022.103733

Cited by 10 publications

(9 citation statements)

References 23 publications

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“…To adjust the cavity geometry in a flexible and efficient way, we use a spline-based free-form deformation approach [13,15,28]. In particular, our deformation spline is a B-spline.…”

Section: Spline-based Free-form Deformationmentioning

confidence: 99%

Using Bayesian optimization for warpage compensation in injection molding

Tillmann,

Behr,

Elgeti

2024

Materialwissenschaft Werkst

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In injection molding, shrinkage and warpage lead to a deformation of the produced parts with respect to the cavity shape. One method to mitigate this effect is to adapt the cavity shape to the expected deformation. This deformation can be determined using appropriate simulation models, which then also serve as a basis for determining the optimal cavity shape. Shape optimization usually requires a sequence of forward simulations, which can be computationally expensive. To reduce this computational cost, we use Bayesian optimization which uses Gaussian process regression as a reduced order model. Additionally, Gaussian process regression has the benefit that it allows to account for uncertainty in the model parameters and thus provides a means to investigate their influence on the optimization result. We present a Gaussian process regression trained with samples from a finite‐element solid‐body model. It predicts the deformation of the product after solidification and, together with Bayesian optimization, allows for efficient cavity optimization.

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“…To adjust the cavity geometry in a flexible and efficient way, we use a spline-based free-form deformation approach [13,15,28]. In particular, our deformation spline is a B-spline.…”

Section: Spline-based Free-form Deformationmentioning

confidence: 99%

Using Bayesian optimization for warpage compensation in injection molding

Tillmann,

Behr,

Elgeti

2024

Materialwissenschaft Werkst

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“…Previous works have already proven the value and accuracy of numerical analysis, e.g. via the finite element method, in the context of plastics profile extrusion [2,3]. Unfortunately, these methods cannot be directly employed in process control, since numerical computations require some expert preparation and are often cost intensive in terms of computational time and resources.…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis and Reduced‐Order Modeling of Plastic Profile Extrusion Processes

Hilger

Sasse

Hopmann

et al. 2023

Proc Appl Math and Mech

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We present the data-driven standardized non-intrusive reduced-order modeling strategy, sniROM, applied in context of plastics profile extrusion. After a brief introduction to the process and the governing physics, we give a short overview of the methodology. The ROM is then used to predict temperature distribution within a plastics profile inside the calibration/cooling unit of the extrusion process. The simulation based model predictions are compared with sensor data captured by an infrared camera. Finally, we give an outlook on how both ROM and measurement data can be used to enable a digital shadow of industrial processes.

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“…For example, one can represent the boundary of the geometry using splines [2], or one can embed the entire geometry in a box spline and perform free‐form deformation (FFD). FFD originates from the field of computer graphics [3], but has also been intensively used in shape optimization [4, 5]. It is easily applicable to complex shapes, since it only requires a box spline and a connection of each node in the mesh to a local coordinate of the spline.…”

Section: Introductionmentioning

confidence: 99%

Shape‐optimization of extrusion‐dies via parameterized physics‐informed neural networks

Tillmann,

Hilger,

Hosters

et al. 2023

Proc Appl Math and Mech

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In this paper, we present an approach to efficiently optimize the design of extrusion dies. Extrusion dies, which are relevant to the manufacturing process of plastics profile extrusion, traditionally require time‐consuming iterations between manual testing and die adjustments. As an alternative, numerical optimization can be used to obtain a high quality initial design and thereby reduce the number of adjustments to the actual die. However, numerical optimization can be computationally expensive, so the use of surrogate models can be helpful to improve efficiency. The latter is the goal of this work. Our method uses physics‐informed neural networks (PINNs) that directly incorporate a free‐form deformation (FFD) approach to allow for geometric variations. The FFD approach allows for a wide range of domain deformations, while the fully trained PINN ensures fast evaluation of the objective function. Using a two‐dimensional model of an extrusion die for demonstration, we detail the integration of the FFD method into the PINN model and discuss its potential in the three‐dimensional context.

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Numerical design of distributive mixing elements

Cited by 10 publications

References 23 publications

Using Bayesian optimization for warpage compensation in injection molding

Using Bayesian optimization for warpage compensation in injection molding

Numerical Analysis and Reduced‐Order Modeling of Plastic Profile Extrusion Processes

Shape‐optimization of extrusion‐dies via parameterized physics‐informed neural networks

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