Modelling of a thermomechanically coupled forming process based on functional outputs from a finite element analysis and from experimental measurements

Wagner, Tobias; Bröcker, C.; Saba, Nicolas; Biermann, Dirk; Matzenmiller, Anton; Steinhoff, Kurt

doi:10.1007/s10182-010-0149-7

Cited by 8 publications

(7 citation statements)

References 29 publications

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“…The surrogate model selection, however, is a challenging task, due to a large number of modern statistical learning techniques with individual strengths and drawbacks. For components with graded properties, DACE models have proven to show a very good fit and prediction quality [9], [10]. With a surrogate model for each process, entire process chains can be simulated.…”

Section: Process Chain Optimisation By Empirical Modelsmentioning

confidence: 99%

A framework for the computer-aided planning and optimisation of manufacturing processes for components with functional graded properties

Biermann

Gausemeier

Heim

et al. 2014

AIP Conference Proceedings

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In this contribution a framework for the computer-aided planning and optimisation of functional graded components is presented. The framework is divided into three modules -the "Component Description", the "Expert System" for the synthetisation of several process chains and the "Modelling and Process Chain Optimisation". The Component Description module enhances a standard computer-aided design (CAD) model by a voxel-based representation of the graded properties. The Expert System synthesises process steps stored in the knowledge base to generate several alternative process chains. Each process chain is capable of producing components according to the enhanced CAD model and usually consists of a sequence of heating-, cooling-, and forming processes. The dependencies between the component and the applied manufacturing processes as well as between the processes themselves need to be considered. The Expert System utilises an ontology for that purpose. The ontology represents all dependencies in a structured way and connects the information of the knowledge base via relations. The third module performs the evaluation of the generated process chains. To accomplish this, the parameters of each process are optimised with respect to the component specification, whereby the result of the best parameterisation is used as representative value. Finally, the process chain which is capable of manufacturing a functionally graded component in an optimal way regarding to the property distributions of the component description is presented by means of a dedicated specification technique.

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Section: Process Chain Optimisation By Empirical Modelsmentioning

confidence: 99%

A framework for the computer-aided planning and optimisation of manufacturing processes for components with functional graded properties

Biermann

Gausemeier

Heim

et al. 2014

AIP Conference Proceedings

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show abstract

“…This model selection is a challenging part due to the large number of modern statistical learning techniques [3]. Prior research figured out that DACE models often show a very good fit and prediction quality for estimating functionally graded properties [4], [5].…”

Section: Empirical Modelingmentioning

confidence: 99%

Optimization of thermomechanical processes for the functional gradation of polymers by means of advanced empirical modeling techniques

Biermann

Heß

Ries

et al. 2014

AIP Conference Proceedings

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A framework for the computer-aided planning and optimisation of manufacturing processes for components with functional graded properties AIP Conference Proceedings 1593, 762 (2014); 10.1063/1.4873887The optimization of Blister Disk geometry for mixing performance in co-rotating twin-screw extruder AIP Conference Proceedings 1664, 020006 (2015) Abstract I In this paper, an optimization procedure for complex manufacturing processes is presented. The procedure is based on advanced empirical modeling techniques and will be presented in two parts. The first part comprises the selection and generation of the empirical surrogate models. The process organization and the design of experiments are taken into account. In order to analyze and optimize the processes based on the empirical models, advanced methods and tools are presented in the second part. These tools include visualization methods and a sensitivity and robustness analysis. Moreover, the obtained surrogate models are used for a model-based multi-objective optimization in order to explore the gradation potential of the processes. The procedure is applied to two thermo-mechanical processes for the functional gradation of polymers -a monoxiale stretching of polycarbonate films and a compression moulding process for polypropylene sheets.

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“…-not only to reduce to computational time spent in FEM calculations required for filling the design space, as in deterministic hierarchical models (Hino et al, 2006); -not only to enrich the data base and yield a better overall model identification, as demonstrated in (Wagner et al, 2011); -but also and especially to reduce the time and effort required for calibrating the FEM models.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper a hierarchical, multi-stage metamodeling approach is proposed, similar to the one used in (Wagner et al, 2011), and combined to an optimization procedure. The goal of the present paper is to demonstrate that hierarchical metamodels can be used:…”

Section: Introductionmentioning

confidence: 99%

“…These models have been more recently and therefore less frequently applied and they are suited not just for merging the results of Hi-Fi and Lo-Fi simulations, but for the fusion of computer experiments and physical experiments. The only available example of application in the metal forming industry, to the authors knowledge, is given in (Wagner et al, 2011), where the goal was to accurately model the temperature distribution of a work piece in a forging process. The results of experimental tests are inherently considered fully accurate, truly Hi-Fi, except for the only approximation due to the measurement system, but they are stochastic in nature and they are usually available in limited numbers, due to their high cost.…”

Section: Introductionmentioning

confidence: 99%

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Reduction of calibration effort in FEM-based optimization via numerical and experimental data fusion

Colosimo

Pagani

Strano

2014

Struct Multidisc Optim

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In this paper a fusion metamodeling approach is suggested as a method for reducing the experimental and computational effort generally required for calibrating the parameters of FEM simulations models. The metamodel is used inside an optimization routine for linking data coming from two different sources: simulations and experiments. The method is applied to a real problem: the optimal design of a metal foam filled tube to be used as an anti-intrusion bar in vehicles. The model is hierarchical, in the sense that one set of data (the experiments) is considered to be more reliable and it is labeled as "high-fidelity" and the other set (the simulations) is labeled as "low-fidelity". In the proposed approach, Gaussian models are used to describe results of computer experiments because they are flexible and they can easily interpolate data coming from deterministic simulations. Since the results of experiments are obviously fully accurate, but aleatory, a second stage ("linkage") model is used, which adjusts the prediction provided by the first model to more accurately represent the real experimental data. In the paper, the modeling and prediction ability of the method is first demonstrated and explained by means of artificially generated data and then applied to the optimization of foam filled tubular structures. The fusion metamodel yields comparable predictions (and optimal solution) if built over calibrated simulations vs. non-calibrated FEM models.

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Modelling of a thermomechanically coupled forming process based on functional outputs from a finite element analysis and from experimental measurements

Abstract: Computer experiments, Finite element analysis (FEA), Functional data analysis, Functionally graded material (FGM), Gaussian process (GP), Multiple fidelity, Prediction, Sample selection,

Cited by 8 publications

References 29 publications

A framework for the computer-aided planning and optimisation of manufacturing processes for components with functional graded properties

A framework for the computer-aided planning and optimisation of manufacturing processes for components with functional graded properties

Optimization of thermomechanical processes for the functional gradation of polymers by means of advanced empirical modeling techniques

Reduction of calibration effort in FEM-based optimization via numerical and experimental data fusion

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