Iso-material contour representation for process planning of heterogeneous object model

Additive manufacturing is emerging as the preferred process for making heterogeneous objects. Planning the deposition of material is more complex for heterogeneous objects as the material variation has to be tracked along the path. This paper proposes an iso-material contour representation to generate the process plan for additive manufacturing given a smooth representation of heterogeneous object model. These contours represent the iso-material paths for deposition. As these paths shift along the direction of the gradation of material distribution, the deposition respects the gradient of the designed material distribution unlike iso-oriented paths generated by a raster scan method. Since the paths have the same material composition, material frequent change in the material composition is avoided, which, in turn, avoids the uneven deposition caused by the frequent start and stop of deposition while the material is being changed along the paths generated by the traditional raster scan. Associativity between the contours and the corresponding designed material feature is maintained, and therefore, changes in material composition are automatically propagated to the process plan.

Applications of PNC in Artificial Intelligence

Interpolation methods and curve fitting represent so huge problem that each individual interpolation is exceptional and requires specific solutions. Presented method is such a new possibility for curve fitting and interpolation when specific data (for example handwritten symbol or character) starts up with no rules for polynomial interpolation. The method of Probabilistic Nodes Combination (PNC) enables interpolation and modeling of two-dimensional curves using nodes combinations and different coefficients γ. This probabilistic view is novel approach a problem of modeling and interpolation. Computer vision and pattern recognition are interested in appropriate methods of shape representation and curve modeling. PNC method represents the possibilities of shape reconstruction and curve interpolation via the choice of nodes combination and probability distribution function for interpolated points. It seems to be quite new look at the problem of contour representation and curve modeling in artificial intelligence and computer vision.

Applications of PNC in Artificial Intelligence

Interpolation methods and curve fitting represent so huge problem that each individual interpolation is exceptional and requires specific solutions. Presented method is such a new possibility for curve fitting and interpolation when specific data (for example handwritten symbol or character) starts up with no rules for polynomial interpolation. The method of Probabilistic Nodes Combination (PNC) enables interpolation and modeling of two-dimensional curves using nodes combinations and different coefficients ?. This probabilistic view is novel approach a problem of modeling and interpolation. Computer vision and pattern recognition are interested in appropriate methods of shape representation and curve modeling. PNC method represents the possibilities of shape reconstruction and curve interpolation via the choice of nodes combination and probability distribution function for interpolated points. It seems to be quite new look at the problem of contour representation and curve modeling in artificial intelligence and computer vision.

On contour representation of two dimensional patterns

Two-dimensional patterns are used in many research areas in computer science, ranging from image processing to specification and verification of complex software systems (via scenarios). The contribution of this paper is twofold. First, we present the basis of a new formal representation of two-dimensional patterns based on contours and their compositions. Then, we present efficient algorithms to verify correctness of the contourrepresentation. Finally, we briefly discuss possible applications, in particular using them as a basic instrument in developing software tools for handling two dimensional words.