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Open die forging is a process in which products are made through repeated, incremental plastic deformations of a workpiece. Typically, the workpiece is held by a manipulator, which can position the workpiece through program control between the dies of a press. The part programs are generated with an empirically derived parameter, called the spread coefficient, whose value is subject to some contention. In this work, we demonstrate how process information can be used in real time to derive the actual spread coefficient for a given workpiece as it is being formed. These measurements and calculations occur in real time, and can be used to regenerate part programs to optimize the forming process, or can be used to adaptively control each incremental deformation of the workpiece.

Tube hydroforming is an attractive manufacturing technology which is now widely used in many industries, especially the automobile industry. The purpose of this study is to develop a method to analyze the effects of the forming parameters on the quality of part formability and determine the optimal combination of the forming parameters for the process. The effects of the forming parameters on the tube hydroforming process are studied by finite element analysis and the Taguchi method. The Taguchi method is applied to design an orthogonal experimental array, and the virtual experiments are analyzed by the use of the finite element method (FEM). The predicted results are then analyzed by the use of the Taguchi method from which the effect of each parameter on the hydroformed tube is given. In this work, a free bulging tube hydroforming process is employed to find the optimal forming parameters combination for the highest bulge ratio and the lowest thinning ratio. A multiobjective optimization approach is proposed by simultaneously maximizing the bulge ratio and minimizing the thinning ratio. The optimization problem is solved by using a goal attainment method. An example is given to illustrate the practicality of this approach and ease of use by the designers and process engineers.

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A decision analytic model often comprises a significant part of a health technology assessment. As health technology assessment in the hospital setting evolves, there is an increased need for modeling methods that account for patient care pathways and interactions between patients and their environment. For example, an evaluation of a computed tomography (CT) scanner for a new indication would need to consider the current and increased demand of the machine and how that may affect service in other areas of the hospital. This problem solving approach views “problems” through a systems perspective.