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    Feed Rate Optimization for Five Axis Milling with an Iso-scallop Tool Path
    (2022-01-01)
    Eranga, Ashan
    ;
    Dissanayaka, Supun
    ;
    Hamontree, Chaowalit
    Five-axis milling tool path generation and feed rate optimization are challenging tasks. Our previous attempt provided a solution for an iso-scallop initial path determination using an optimal feeding direction (OFD) method that was efficient and maintained the quality of the machining surface. This paper extended the method with feed rate optimization to improve the machining time. Maximal allowable feed rate concept was used to define the objective function which was depended on A and B axis of the machine. To achieve smooth rotary axes motion, two B-splines were used to represent each axis. Then the non-linear optimization problem was solved using the interior-point algorithm with the constraint on global collision. Finally, the predetermined iso-scallop path was combined with the optimized tool orientation. The orientation optimization strategy was compared with constant lead angle and tool orientation smoothing method. Simulation results showed that our variable feed rate assignment decrease the machining time.
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    An estimating parameter of nonparametric regression model based on smoothing techniques
    (2019-01-01)
    Araveeporn, Autcha
    This paper studies the estimating parameter of a nonparametric regression model that consists of the function of independent variables and observation of dependent variables. The smoothing spline, penalized spline, and B-spline methods in a class of smoothing techniques are considered for estimating the unknown parameter on nonparametric regression model. These methods use a smoothing parameter to control the smoothing performance on data set by using a cross-validation method. We also compare these methods by fitting a nonparametric regression model on simulation data and real data. The nonlinear model is a simulation data which is generated in two different models in terms of mathematical function based on statistical distribution. According to the results, the smoothing spline, the penalized spline, and the B-spline methods have a good performance to fit nonlinear data by considering the hypothesis testing of biased estimator. However the penalized spline method shows the minimum mean square errors on two models. As real data, we use the data from a light detection and ranging (LIDAR) experiment that contained the range distance travelled before the light as an independent variable and the logarithm of the ratio of received light from two laser sources as a dependent variable. From the mean square errors of fitting data, the penalized spline again shows the minimum values.
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    Shape matching using set of curve geometric invariant point
    (2005-12-01)
    Pintavirooj, C.
    ;
    Nantivatana, P.
    ;
    Putjarupong, P.
    ;
    Withayachumnankul, W.
    ;
    Sangworasil, M.
    We introduce a non-iterative geometric-based method for shape matching using a novel set of geometric landmarks residing on a 2D contours. These landmarks are intrinsic and are computed from the differential geometry of the curve. We exploit the invariant properties of geometric landmarks that are local and preserved under the affine and some perspective transformation. Geometric invariant exploits coplanar five-point invariant and ration of area constructed from a sequence of consecutive landmarks. These invariants are preserved not only in affine map but weak perspective map as well. To reduce the sensitivity of the landmarks to noise, we use a B-Spline surface representation that smoothes out the curve prior to the computation of the landmarks. The matching is achieved by establishing correspondences between the landmarks after a conformal sorting based on derived absolute invariant and registering the contours. The experiments have shown that the purposed methods are robust and promising even in the presence of noise. Copyright UNION Agency - Science Press.