Ford Global Technolgoies, LLC

Ford Motor Company Limited

James Calvey Carnes

Sing C Tang

Gintaras Vincent Puskorius

Lee Albert Feldkamp

Gerald Jesion

A method is disclosed for developing a sheet metal forming process prediction method which is based on the application of an anisotropic hardening rule of plasticity in the mathematic theory.

Method of applying an anisotropic hardening rule of plasticity to sheet

A electronic engine control (EEC) module executes a generic neural network processing program to perform one or more neural network control funtions. Each neural network funtion is defined by a unitary data structure which defines the network architecture, including the number of node layers, the number of nodes per layer, and the interconnections between nodes. In addition, the data structure holds weight values which determine the manner in which network signals are combined. The network definition data structures are created by a network training system which utilizes an external training processor which employs gradient methods to derive network weight values in accordance with a cost function which quantitatively defines system objectives and an identification network which is pretrained to provide gradient signals representative the behavior of the physical plant. The training processor executes training cycles asynchronously with the operation of the EEC module in a representative test vehicle.

Generic neural network training and processing system

Method for predicting post-buckling deformation of a sheet of metal during a draw forming process, for use with a computer including memory, by introducing a set of springs to stabilize the sheet metal close to the onset of buckling, thereby enhancing convergence of numerical solutions. The method is for use with sheet metal forming tools including a draw die, a punch and binder having surfaces designed to form the sheet metal into a part, the sheet metal being represented as a mesh including a plurality of nodes. The method includes applying a displacement increment to the sheet metal nodes and identifying a singularity in a tangent stiffness matrix associated with the sheet metal close to the onset of buckling. The method also includes introducing a plurality of springs at the sheet metal nodes so as to eliminate the singularity and enhance convergence of the numerical solution of the displacement increment.

Method and apparatus for predicting post-buckling deformation of sheet

Method and system for evaluating sheet metal forming tooling design, for use with a draw die including a punch and binder designed to form the sheet metal into a part, utilizing improved implicit time integration methods that reduce numerical instability, thereby enhancing convergence of numerical solutions. The sheet metal and tool surface of the punch are each represented as a mesh having a plurality of nodes. Contact nodes between the sheet metal mesh nodes and the tool surface mesh can be identified. A first embodiment includes minimizing discontinuities generated by unloading by determining a stress increment of a sampling point in the sheet metal mesh according to an incremental deformation theory of plasticity. A second embodiment includes modelling a draw-bead as a plurality of nonlinear elastic springs to minimize discontinuities in the spring force during unloading. A third embodiment includes filtering a relative velocity vector of at least one contact node with respect to the tool surface, to avoid frictional force oscillations due to the change in direction of the relative velocity vector during formation of the part.

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Willis, MI, United States of America

James Calvey Carnes