Viscoplastic Deformation Analysis and Extrusion Die Design by FEM

A viscoplastic model for extrusion is discussed which simultaneously predicts the deformation field, optimal die geometry, and plastic boundaries. The die geometry and plastic boundaries are expressed in terms of chosen trial functions that satisfy certain geometrical and physical constraints. The variational power integral is minimized in the trial plastic domain using FEM technique to determine the deformation field and shape coefficients for the die contour and plastic boundaries. The proposed method is implemented for the optimal design of an axisymmetric streamlined die. The predicted values are in reasonable agreement with the experimental observations and are in conformity with the results published earlier.

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Polymer melt rheology and flow simulations applied to cast film extrusion die design: An industrial perspective

10.1063/1.4982988 ◽

2017 ◽

Cited By ~ 1

Author(s):

Olivier Catherine

Keyword(s):

Polymer Melt ◽

Die Design ◽

Melt Rheology ◽

Flow Simulations ◽

Film Extrusion ◽

Extrusion Die ◽

Cast Film ◽

Extrusion Die Design

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Robust low cost meta-modeling optimization algorithm based on meta-heuristic and knowledge databases approach: Application to polymer extrusion die design

Finite Elements in Analysis and Design ◽

10.1016/j.finel.2019.05.004 ◽

2019 ◽

Vol 162 ◽

pp. 51-66 ◽

Cited By ~ 2

Author(s):

N. Lebaal

Keyword(s):

Optimization Algorithm ◽

Low Cost ◽

Die Design ◽

Polymer Extrusion ◽

Extrusion Die ◽

Extrusion Die Design ◽

Meta Modeling

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Collaborative Aluminum Profile Design to Adaptable Die Process Planning Using Neural Networks

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.443.207 ◽

2010 ◽

Vol 443 ◽

pp. 207-212 ◽

Cited By ~ 2

Author(s):

Suthep Butdee ◽

Chaiwat Noomtong ◽

Serge Tichkiewitch

Keyword(s):

Neural Network ◽

Process Planning ◽

Collaborative Design ◽

Die Design ◽

Machining Process ◽

Aluminum Extrusion ◽

Product Data ◽

Die Geometry ◽

Extrusion Die ◽

Profile Design

Aluminum extrusion die manufacturing is a critical task for productive improvement and increasing potential of competition in aluminum extrusion industry. It causes to meet the efficiency not only consistent quality but also time and production cost reduction. Die manufacturing consists first of die design and process planning in order to make a die for extruding the customer’s requirement products. The efficiency of die design and process planning are based on the knowledge and experience of die design and die manufacturer experts. This knowledge has been formulated into a computer system called the knowledge-based system. It can be reused to support a new die design and process planning. Such knowledge can be extracted directly from die geometry which is composed of die features. These features are stored in die feature library to be prepared for producing a new die manufacturing. Die geometry is defined according to the characteristics of the profile, is called product data, so we can reuse die features from the previous similar profile design cases. This paper presents the artificial neural network to assist aluminum extrusion die design and process planning based on collaborative design methodology. Product data can be shared and distributed in die design team members via computer network technology. This product data is used to support die design and process planning. Die manufacturing cases in the case library would be retrieved with searching and learning method by neural network for reusing or revising it to build a die design and process planning when a new case is similar with the previous die manufacturing cases. The results of the system are dies design and machining process.

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