An inverse method for prediction of the required temperature distribution in the creep forming process

Moreau, Philippe; Lochegnies, Dominique; Oudin, J.

doi:10.1243/0954408981529259

Cited by 11 publications

(9 citation statements)

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“…For instance a numerical optimisation method to find the optimum tool geometry in a model for glass pressing was introduced in [44]. Optimisation methods have also been developed to estimate heat transfer coefficients [11] or the initial temperature distribution in glass forming simulation models [43]. An engineering approach to find the optimum preform shape for glass blowing was addressed in [41].…”

Section: Process Simulationmentioning

confidence: 99%

Mathematical Modelling of Glass Forming Processes

Groot

Mattheij

Laevsky³

2010

Lecture Notes in Mathematics

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Section: Process Simulationmentioning

confidence: 99%

Mathematical Modelling of Glass Forming Processes

Groot

Mattheij

Laevsky³

2010

Lecture Notes in Mathematics

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“…In engineering design, inverse models are often created to allow designers to identify the necessary design parameters to yield a specific set of specifications. For instance, Moreau et al [15] present a method to determine the initial temperature distribution necessary to post-creep glass dimensions to specification. Lu et al [16] present a Backward Mapping Methodology for Design Synthesis that breaks the design space into feature-based subregions and then fits linear approximations between the each sub-region and each performance variable.…”

Section: Related Workmentioning

confidence: 99%

“…• Problem Size: [5,2], [15,2] • Mapping: Many-to-One, One-to-One As in the parameter robustness study, the scale factor F and the cross-over ratio CR were set to 0.5 and 0.9, respectively, and the values of NP and NS were set to 10 and 5, respectively based on the results of the robustness study. For the Manyto_One problems, all but 2 inputs are designated as position variables.…”

Section: Test Problems For Detailed Assessment Studymentioning

confidence: 99%

Improving the Performance of Visual Steering Commands for Multi-Dimensional Trade Space Exploration

Congdon

Carlsen

Simpson

et al. 2008

Volume 1: 34th Design Automation Conference, Parts a and B

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1234Designers perform many tasks when developing new products and systems, and making decisions may be among the most important of these tasks. The trade space exploration process advocated in this work provides a visual and intuitive approach for formulating and solving single-and multiobjective optimization problems to support design decisionmaking. In this paper, we introduce an advanced sampling method to improve the performance of the visual steering commands that have been developed to explore and navigate the trade space. This method combines speciation and crowding operations used within the Differential Evolution (DE) algorithm to generate new samples near the region of interest. The accuracy and diversity of the resulting samples are compared against simple Monte Carlo sampling as well as the current implementation of the visual steering commands using a suite of test problems and an engineering application. The proposed method substantially increases the efficiency and effectiveness of the sampling process while maintaining diversity within the trade space. INTRODUCTIONDesigners perform many tasks when developing new products and systems, and making decisions may be among the most important of these tasks, given the impact that these decisions ultimately have on the product's or system's cost, performance, time-to-market, etc. These decisions typically involve tradeoffs between competing or conflicting objectives, and many designers employ optimization-based approaches and techniques to try and help them resolve these tradeoffs. Unfortunately, most designers do not really know their preferences when they start this process [1], or perhaps more importantly understand the implications of their preferences until they have been able to evaluate some preliminary design alternatives to form "realistic expectations of what is possible". In fact, Balling [1] has noted that the traditional optimizationbased design process of "1) formulate the design problem, 2) obtain/develop analysis models, and 3) execute an optimization algorithm" often leaves designers unsatisfied with their results.Consequently, we are investigating ways to help designers formulate and solve single-and multi-objective optimization problems in a more visual and intuitive manner. This process, which we refer to as trade space exploration, is an embodiment of the Design by Shopping paradigm advocated by Balling [1]: designers want to be able to "shop" for the best design, to gain intuition about trades, to see what is feasible and what is not, and to learn about their alternatives first before making a decision. Our trade space exploration process combines a multidimensional data visualization tool -the Applied Research Laboratory's Trade Space Visualizer, or ATSV [2] -along with visual steering commands [3,4] to put designers "back-in-theloop" when performing design optimization. For example, designers can now sample new designs near any point or region of interest within the trade space by placing one or more attractors directly w...

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“…Since the glass sheet has been preheated before entering to the main chamber of creep forming, the time of forming does not usually exceed 60 s. For producing complex shapes and because of economical reasons, using variable furnace temperatures seem to be the rational solution [15]. After selection of suitable finite element software and coupling it with another program, it has been tried to predict the acceptable temperature variations.…”

Section: Temperature Variationmentioning

confidence: 99%