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Combined Sheet and Bulk Forming of High Value Added Components in Manufacturing

  • Atsushi DannoEmail author
Reference work entry

Abstract

The combined sheet and bulk forming process is a key technology for the near-net-shape forming of lightweight metal components with thin and multiwall thickness. In this forming process, bulk forming process, i.e., a compressive forming process, is applied to the sheet or plate workpiece in combination with the conventional sheet forming (stamping) process to minimize the material waste and manufacturing cost. “Spin forming” (flow forming, shear forming) as well as “combined stamping and forging” are key technologies in this type of the process.

The flow forming process can reduce the wall thickness of rotating cylindrical hollow workpiece into an intended value incrementally, and shear forming can form various kinds of conical/semispherical/cup-shaped hollow components from flat disk-shaped workpiece of sheet metal. The advantages of spin forming are low cost, simple tooling, easy lubrication during forming, high flexibility in forming, and applicability to cold forming of high-strength and difficult-to-form materials. The optimization of the forming process, forming parameters, tooling design, and initial workpiece design is required for successful spin forming.

The combined stamping and forging process includes the local compression of workpiece in its thickness direction for reducing the wall thickness locally in combination with some forming mode of conventional stamping of sheet metal. In this process, various kinds of thin-walled 2D/3D components with multi thicknesses, ribs, pins, and webs can be formed in near net shape from sheet workpiece of uniform thickness. The optimizations in forming sequence in multistep forming and tooling design are essential for successful forming practice.

Keywords

Deep Drawing Metal Flow Frictional Condition Small Cone Angle Major Process Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Singapore Institute of Manufacturing TechnologySingaporeSingapore

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