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Computer Aided 5-Axis Machining
Published in Cornelius Leondes, Computer-Aided Design, Engineering, and Manufacturing, 2019
Andrew Warkentin, Paul Hoskins, Fathy Ismail, Sanjeev Bedi
The tool path used to machine a surface is generally produced in three stages. First, tool path planning is used to determine the path the tool will take as it machines a surface. Tool path planning research is primarily concerned with the spacing between points on the tool path and determining the tool pass interval. Second, tool positioning strategies are used to determine the cutter location and orientation at specific points on the tool path. The objective of a tool positioning strategy is to minimize the material remaining between the tool and the design surface as the tool moves along the tool path. Finally, gouge detection and correction algorithms are used to determine if the tool has penetrated the desired surface and eliminate this penetration.
A Multi-objective Tool Path Optimization Methodology for Sculptured Surfaces Based on Experimental Data and Heuristic Search
Published in Kaushik Kumar, Divya Zindani, J. Paulo Davim, Digital Manufacturing and Assembly Systems in Industry 4.0, 2019
N. A. Fountas, N. M. Vaxevanidis, C. I. Stergiou, R. Benhadj-Djilali
It is very likely that a “perfect” algorithm to generate optimal tool paths for sculptured surfaces might never exist. Based on this assumption, the work proposed in this paper moves toward a new direction of optimizing sculptured surface tool paths by modifying them after their initial generation from typical CAM systems. This allows for taking into account the entire surface and its basic geometrical properties (i.e., curvature) at once so that a meta-heuristic may sequentially retrieve major attributes and exchange information with CAM software through automation, until stopping criteria are met. Key entities constituting the tool path, such as cutter location data, are examined along with their corresponding topology in terms of tool positioning vectors, which determine also the effective cutting shapes (or postures) the tool will have in each point. A three-objective, generalized optimization problem is formulated to be heuristically solved using a modern evolutionary algorithm. The criteria are the true mean of machining error (as the combined effect of scallop heights and chord errors among pairs of connected-interpolated tool path points), the standard deviation of the machining error to quantify its distribution throughout the entire tool path and maintain it smooth, and finally the number of cutter location data for minimizing tool path time. The algorithm selected for optimizing the sculptured surface machining problem builds chromosomes by considering all influential parameters, that is, cutting tool geometry, stepover, lead angle, tilt angle, and the maximum discretization step (step forward to feed direction). The expressions adopted to predict scallop heights and chord errors have been experimentally validated for their generalized result whilst they have been programmed as utilities within the boundaries of the open programming architecture (API) of a cutting-edge CAM system.
Globally optimal tool paths for sculptured surfaces with emphasis to machining error and cutting posture smoothness
Published in International Journal of Production Research, 2019
Nikolaos A. Fountas, Nikolaos M. Vaxevanidis, Constantinos I. Stergiou, Redha Benhadj-Djilali
In sculptured surface CNC machining, cutter location as well as orientation vary along the multi-axis tool path with respect to the part surface. Consecutively the values of tool path parameters; stepover, lead and tilt angles and maximum discretization step, alter the resulting work piece-engagement boundaries at each of cutter contact points; suggesting different tool path postures. Cutter location data formulate an mxn pattern of points covering the entire sculptured surface represented in the u, v parametric space. A unique cutter location (CL) point is determined as CLP(x, y, z, i, j, k, c1, c2), where (x, y, z) are the coordinates of the machining axis system (G54) whilst (i, j, k) is the unit normal vector representing the tool’s position for that CLP. Finally, c1 and c2 are the two principal curvatures of the surface for u and v, respectively, responsible for the tool’s inclined position to the CLP.
A review on tool orientation planning in multi-axis machining
Published in International Journal of Production Research, 2021
Fusheng Liang, Chengwei Kang, Fengzhou Fang
To clarify the geometric location of cutter, the local coordinate system at cutter contact (CC) point CC(ui,vi) on free-form surface S is built, as shown in Figure 2. xyz is the coordinate system on surface S, in which axis x is in iso-parametric direction of u, and axis z is in direction along surface normal at CC point and axis y is derived from cross product of axes x and z. XLYLZL is a tool path coordinate system with axes XL and ZLoriented in the feed direction and surface normal, respectively. Similarly, axis YL is obtained by crossing axes XL and ZL. xtytzt is a cutter coordinate system, where xt is the projection of cutter axis on tangent plane of CC point. The direction of axis zt coincides with normal of surface S. yt is the cross product of axes xt and zt. Cutter position can be determined according to cutter location (CL) point and tool orientation. The position of CL point is related to cutter geometry. Particularly, for flat-end cutter, CL point is the center of the bottom circle. There are some different approaches to define tool orientation, which is determined by rotating tool axis around axis YL to an angle of α first, and then rotating around axis ZL to an angle of β. In this paper, angle α is titled with inclination angle and angle β is titled with screw angle. In the following sections, all geometry parameters are named as the illustration above unless otherwise specified.
Prediction of cutting force for ball end mill in sculptured surface based on analytic model of CWE and ICCE
Published in Machining Science and Technology, 2019
Z. C. Wei, M. L. Guo, M. J. Wang, S. Q. Li, J. Wang
The accuracy of ICCE is important to the prediction of cutting force. The solid modeling method based on Boolean operation is inefficient, but its accuracy is sufficient. Simulating the fourth sampling point on sinusoidal path machining based on the analytic model in this article, the upper and lower bounds of ICCE during a cutter revolution are shown in Figure 8. At specific cutter edge position angles including 90°(270°), 100°(280°), 110°(290°), 120°(300°), 130°(310°), 140°(320°) and 150°(330°), they match well to the ICCE of solid modeling based on Boolean operations and the absolute maximum error is only 1.41%. In order to further investigate the accuracy for eight cutter location points on sinusoidal path, the ICCE accuracy of analytical method relative to solid model at the cutter edge position angle corresponding to the longest ICCE were calculated, as shown in Figure 9. The correlative theory researched by Wei et al. (2017) shows that there are some errors when the analytic algorithm of ICCE is applied to a complex surface. Because the algorithm is based on inclined plane machining, each cutter location point is an approximate oblique plane, and the accuracy of the algorithm is determined by the curvature radius of the tool path, the curvature radius of the workpiece surface and the curvature radius of the tool path sequence surface. Moreover, the larger the curvature radius is, the closer the cutter location point is to the ideal oblique plane, and the smaller the error is. In the simulation example, the main reason this affects the accuracy of ICCE is the curvature radius of the tool path. The curvature radius of eight cutter location points along the tool path increases first and then decreases, which is consistent with the trend of the accuracy simulation results. The plus or minus accuracy indicates that the theoretical value of the ICCE is larger or smaller than the actual value. The maximum accuracy simulation error is about 3.5%, which is acceptable for cutting force prediction. Overall, the analytic algorithm of ICCE could apply to complex surface machining effectively, its calculation efficiency is high without loss of accuracy.