Aluminum Extrusion Technology

2000 ◽  
Author(s):  
Pradip K. Saha
Keyword(s):  
Theory And Practice ◽  
Die Design ◽  
Hard Alloys ◽  
Aluminum Extrusion ◽  
Statistical Process ◽  
Print Version ◽  
Strain And Strain Rate ◽  
Friction Pressure ◽  
Key Variables ◽  
Extrusion Technology

Aluminum Extrusion Technology covers the theory and practice of extrusion and its application in the production of aluminum alloy parts. The first few chapters discuss the mechanics and thermodynamics of direct and indirect extrusion processes and the effect of key variables such as strain and strain rate, friction, pressure, flow stress, and temperature. Subsequent chapters explain how to implement and maintain industrial-scale aluminum extrusion processes. The chapters cover extrusion presses and equipment, tooling and die design, billet casting, and process control. They also provide information on the extrusion characteristics of soft, medium, and hard alloys and discuss the use of statistical process and quality control. For information on the print version, ISBN 978-0-87170-644-7, follow this link.

Seam Welds Modeling and Mechanical Properties Prediction in the Extrusion of AA6082 Alloy

2008 ◽  
Vol 367 ◽  
pp. 125-136 ◽  
Author(s):  
Lorenzo Donati ◽  
Luca Tomesani
Keyword(s):  
Mechanical Properties ◽  
High Speed ◽  
Fem Simulation ◽  
Die Design ◽  
Welding Parameters ◽  
Middle Section ◽  
3D Fem ◽  
Strain And Strain Rate ◽  
Mechanical Properties Prediction ◽  
Temperature Time

This work summarizes the outcome of recent research by the authors on modeling the formation of seam welds in aluminum extrusion and on evaluating the related mechanical properties on the final products. A profile with a seam weld in the middle section was produced with different die designs in order to investigate the relation between die design and local welding parameters, such as contact pressure, temperature, time of contact, strain and strain rate paths. The local welding conditions were evaluated by complete thermo-mechanical 3D FEM simulation of the processes. Specimens were extracted from the profiles and tensile tested, the resulting mechanical properties being discussed with respect to the local welding conditions. The possibility to adopt criteria for assessing the welding quality is discussed, together with the effect of high speed damage cracking.

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

2010 ◽  
Vol 443 ◽  
pp. 207-212 ◽  
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.

Statistical Process Control: Theory and Practice.

1992 ◽  
Vol 87 (419) ◽  
pp. 905
Author(s):  
Robert G. Easterling ◽  
G. Barrie Wetherill ◽  
Don W. Brown
Keyword(s):  
Process Control ◽  
Control Theory ◽  
Statistical Process Control ◽  
Theory And Practice ◽  
Statistical Process

scholarly journals 4th International aluminum extrusion technology seminar and exposition.

1989 ◽  
Vol 39 (5) ◽  
pp. 396-402
Author(s):  
Study Team Of International Aluminu ◽  
Mitsugu TOKIZAWA ◽  
Akira YAMAMOTO ◽  
Shigeyoshi TAKAGI
Keyword(s):  
Aluminum Extrusion ◽  
Extrusion Technology

Third International Aluminum Extrusion Technology Seminar

JOM ◽  
1984 ◽  
Vol 36 (12) ◽  
pp. 25-28 ◽  
Author(s):  
D. M. Bowden
Keyword(s):  
Aluminum Extrusion ◽  
Extrusion Technology
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