This paper presents geometric algorithms for developing a re-configurable tooling system for fabrication of freeform objects. The proposed method involves a mold block, with n faces, in which the mold cavity is formed by moving a set of discrete pins on each face of the block. The part surfaces are approximated in the mold cavity using the pins from the suitable mold block faces. The geometric algorithms detailed in this paper analyze the part and determine the face of mold block from which the part model is approximated best. Further, the algorithms detect possible interference between pins from different faces, and suitably alter the approximating face to alleviate interferences. By moving these pins in and out of the mold block, the shape of the mold cavity is reconfigured rapidly to suit the changes in part geometry. Since the proposed method approximates free-form objects with discrete pins, a surface-error calculation method is also developed to control the accuracy. Computer implementation and examples are also presented in this paper.
This paper presents an integrated physiconeural network approach for the modeling and optimization of a vertical MOCVD reactor. The basic concept is to utilize the solutions obtained from a physical model to build an accurate neural network (NN) model The resulting model has the attractive features of self-adaptiveness and speed of prediction and is an ideal starting tool for process optimization and control. Following this approach, a first-principles physical model for the reactor was solved numerically using the Fluid Dynamics Analysis Package (FIDAP). This transient model included property variation and thermodiffusion effects. Using software developed in house, neural networks were then trained using FIDAP simulations for combinations of process parameters determined by the statistical Design of Experiments (DOE) methodology. The outputs were the average and local deposition rates. It is shown that the trained NN model predicts the behavior of the reactor accurately. Optimum process conditions to obtain a uniform thickness of the deposited film were determined and tested using the physical model. The results demonstrate the power and robustness of NNs for obtaining fast responses to changing input conditions. A procedure for developing equipment models based on physiconeural network models is also described.
This paper describes geometric algorithms for manufacturing freeform objects using a re-configurable mold system. The proposed process involves a mold block, with n faces, in which the mold cavity is formed. Each face of the mold block holds a uniform grid of pins, which are used to approximate the part surfaces. The geometric algorithms presented in this paper analyze the part and determine the face of mold block from which the part model is approximated best using the pins from that face. By moving these pins in and out of the mold block, the shape of the mold cavity can be configured dynamically to suit the changes in the part geometry. Since the proposed process approximates free-form objects with discrete pins, a surface-error calculation method is also developed to control the accuracy. Computer implementation and examples are also provided in this paper.
PurposeThe objective of this paper is to develop geometric algorithms and planning strategies to enable the development of a novel hybrid manufacturing process, which combines rapidly re‐configurable mold tooling and multi‐axis machining.Design/methodology/approachThe presented hybrid process combines advantages of both reconfigurable molding and machining processes. The mold's re‐configurability is based on the concept of using an array of discrete pins. By positioning the pins, the reconfigurable molding process allows forming the mold cavity directly from the object's 3D design model, without any human intervention. After a segment of the part is molded using the reconfigurable molding process, a multi‐axis machining operation is used to create accurate parts with better surface finish. Geometric algorithms are developed to decompose the design model into segments based on the part's moldability and machinability. The decomposed features are used for planning the reconfigurable molding and the multi‐axis machining operations.FindingsComputer implementation and illustrative examples are also presented in this paper. The results showed that the developed algorithms enable the proposed hybrid re‐configurable molding and multi‐axis machining process. The developed decomposition and planning algorithms are used for planning the reconfigurable molding and the multi‐axis machining operations. Owing to the decomposition strategy, more geometrically complex parts can be fabricated using the developed hybrid process.Originality/valueThis paper presents geometric analysis and planning to enable the development of a novel hybrid manufacturing process, which combines rapidly re‐configurable mold tooling and multi‐axis machining. It is expected that the proposed hybrid manufacturing process can produce highly customized parts with better surface finish, and part accuracy, with shorter build times, and reduced setup and tooling costs.
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