Individualized production in die-based manufacturing processes using numerical optimization

Siegbert, Roland; Yesildag, Nafi; Frings, Markus; Schmidt, Frank L.; Elgeti, Stefanie; Sauerland, Henning; Behr, Marek; Windeck, Christian; Hopmann, Christian; Queudeville, Yann; Vroomen, Uwe; Bührig–Polaczek, Andreas

doi:10.1007/s00170-015-7003-8

Cited by 20 publications

(10 citation statements)

References 30 publications

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“…To tackle this problem more efficiently, numerical modeling has been extensively investigated in recent years with commercial or publicly available softwares such as Ansys Polyflow, OpenFOAM, and Dieflow. [50,107,[164][165][166][167][168][169][170][171][172] Currently, the status can be mainly summarized according to two methodologies: i) achieving exit flow uniformity or balancing at the die exit by the modification of the upstream flow channel or design variables, such as the cross section of the flow channel, parallel zone, or die land, [166,170,173] as shown in Figure 16a,b; [164] and ii) using the desired extrudate shape as a constraint for the integration of the kinematic equations for the free surface flow by modifying the die lips. For example, a solution algorithm is implemented in the Polyflow program for a reverse extrusion problem.…”

Section: Reverse Engineering: Die Design For a Controlled Swelling Fomentioning

confidence: 99%

State of the‐Art for Extrudate Swell of Molten Polymers: From Fundamental Understanding at Molecular Scale toward Optimal Die Design at Final Product Scale

Tang

Marchesini

Cardon

et al. 2020

Macro Materials & Eng

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Exit velocity profiles for parallel die and die optimized using conical widening or narrowing. Reproduced permission. [164] Copyright 2010, Elsevier Ltd. c) Average velocity of each outflow subsection of the slit die (width = 100 mm and height = 1.5 mm), Reproduced with permission. [173] Copyright 2012, Elsevier Ltd. d,e) The geometry profiles of the Catheter cross section covered as case study. f) The velocity distribution at the die outflow for different trials-T0: the original die profile; T1: r 2 varies from 0.9 to 0.95 mm; T2: r 2 varies from 0.95 to 0.9 mm, θ 2 varies from 45° to 43°; T3: r 2 varies from 0.9 to 0.95 mm; T4: θ 2 varies from 43° to 40°; T5: r 3 varies from 0.7 to 0.65 mm. Reproduced with permission. [166] Copyright 2013, Elsevier. g) The corresponding objective function evolution in consecutive trials. Reproduced with permission. [166] Copyright 2013, Elsevier Ltd.

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Section: Reverse Engineering: Die Design For a Controlled Swelling Fomentioning

confidence: 99%

State of the‐Art for Extrudate Swell of Molten Polymers: From Fundamental Understanding at Molecular Scale toward Optimal Die Design at Final Product Scale

Tang

Marchesini

Cardon

et al. 2020

Macro Materials & Eng

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“…objective functions). [57][58][59][60][61][62][63] The extrusion through a capillary die, sketched in Figure 6, is particularly interesting in spite of its simple geometry, because it gives an insight into the complex flow phenomena of the polymeric materials. There are different flow regimes in polymer extrusion.…”

Section: Modelling the Flow Of Polymers In Extrusionmentioning

confidence: 99%

Multiphysics modelling of manufacturing processes: A review

Jabbari

Baran

Mohanty³

et al. 2018

Advances in Mechanical Engineering

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Numerical modelling is increasingly supporting the analysis and optimization of manufacturing processes in the production industry. Even if being mostly applied to multistep processes, single process steps may be so complex by nature that the needed models to describe them must include multiphysics. On the other hand, processes which inherently may seem multiphysical by nature might sometimes be modelled by considerably simpler models if the problem at hand can be somehow adequately simplified. In the present article, examples of this will be presented. The cases are chosen with the aim of showing the diversity in the field of modelling of manufacturing processes as regards process, materials, generic disciplines as well as length scales: (1) modelling of tape casting for thin ceramic layers, (2) modelling the flow of polymers in extrusion, (3) modelling the deformation process of flexible stamps for nanoimprint lithography, (4) modelling manufacturing of composite parts and (5) modelling the selective laser melting process. For all five examples, the emphasis is on modelling results as well as describing the models in brief mathematical details. Alongside with relevant references to the original work, proper comparison with experiments is given in some examples for model validation.

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“…the bionic method [29], and mathematical method [30]; 3) optimization algorithms (e.g. optimization review [31], and optimization means [32]), and optimal control decisions (e.g. decision index [33], [34]); and 4) energy managements [35].…”

Section: Introductionmentioning

confidence: 99%

A Graph Theory-Based Optimization Design for Complex Manufacturing Processes

et al. 2020

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The manufacturing process of modern equipment becomes very complex due to features such as mass units, multiple machining, and complicated coupling-relationships, posing a big challenge for determining the manufacturing scheme. This paper addresses the challenge by proposing a graph theory-based optimization design for the complex manufacturing process. A detailed analysis of a serial of graph models built according to the manufacturing process features reveals that the Hamilton graph is suitable for modeling the manufacturing process system. Some model weight assignment functions are extracted for the quantitative study. Further the optimal scheme for an optimization design of the complex manufacturing process is solved using the full link graph feature algorithm -a search optimization algorithm. A manufacturing model matrix is constructed, and penalty number and divisor are formulated to simplify the matrix and improve the algorithm efficiency in the process of algorithm design. An example is provided to demonstrate feasibility and effectiveness of the proposed method.INDEX TERMS Graph model, graph theory, manufacturing process, model weight, optimization design.ZHONG HAN received the B.S. degree in computer application technology and the M.S. degree in computer science and technology from the University of Electronical Science and Technology, Chengdu, China, respectively, and the Ph.D. degree in mechanical engineering and automation from Xi'an Jiaotong University, Xi'an, China.He was a Postdoctoral Researcher of intelligent manufacturing technology with the Xi'an Jiaotong University of China. He is currently an Associate

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Individualized production in die-based manufacturing processes using numerical optimization

Cited by 20 publications

References 30 publications

State of the‐Art for Extrudate Swell of Molten Polymers: From Fundamental Understanding at Molecular Scale toward Optimal Die Design at Final Product Scale

State of the‐Art for Extrudate Swell of Molten Polymers: From Fundamental Understanding at Molecular Scale toward Optimal Die Design at Final Product Scale

Multiphysics modelling of manufacturing processes: A review

A Graph Theory-Based Optimization Design for Complex Manufacturing Processes

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