System and method for extracting parameters of a cutting tool

1. A method for extracting parameters of a tool, comprising:

obtaining a measurement data set having a point cloud corresponding to a surface of the tool;

virtually slicing the point cloud at-least one pre-determined section to obtain a set of points on the pre-determined section;

generating a plurality of curves through the set of points and optimizing the plurality of curves to generate optimized fitting curves; and

extracting the parameters of the tool from the optimized fitting curves.

2. The method of claim 1 , wherein obtaining the measurement data comprises scanning a surface of the tool via a three-dimensional (3D) measurement system.

3. The method of claim 2 , wherein the 3D measurement system comprises a laser scanner, or a white light 3D scanner, or scanning contact probes.

4. The method of claim 1 , wherein the tool comprises a ball end mill, or a flat end mill, or a drill, or a reamer.

5. The method of claim 4 , wherein the parameters for the tool comprise a rake angle, or a primary relief angle, or a secondary clearance, or a land width, or a shank diameter, or a cutting diameter, or a core diameter, or combinations thereof.

6. The method of claim 1 , wherein virtually slicing the point cloud comprises a plane slicing, or a surface slicing of the point cloud.

7. The method of claim 6 , wherein virtually slicing the point cloud comprises radial slicing, or axial slicing, or cylindrical slicing, or a pre-determined direction slicing of the point cloud.

8. The method of claim 1 , wherein generating the plurality of curves comprises segmenting a plurality of key features for the pre-determined section.

9. The method of claim 8 , further comprising determining a number of curves for the plurality of key features at the pre-determined section.

10. The method of claim 8 , wherein generating the plurality of curves comprises generating an arc, or a spline, or a line through the set of points through a least-square curve fitting technique.

11. The method of claim 10 , further comprising creating constraints between adjacent curves for the generated plurality of curves.

12. The method of claim 1 , wherein optimizing the plurality of curves comprises iteratively finding global minimum deviations between set of points and the plurality of curves via Levenberg-Marquardt method.

13. The method of claim 1 , wherein extracting the parameters comprises extracting the parameters via a rotary axial projection technique.

14. A processor based system for extracting parameters of a tool, comprising:

a 3D measurement device configured to scan a surface of the tool and to acquire a point cloud corresponding to the surface;

a virtual slicing module configured to slice the point cloud corresponding to a surface of the tool to obtain a set of points at-least one pre-determined section;

a curve fitting and optimization module configured to generate a plurality of curves through the set of points and to optimize the plurality of curves by minimizing deviations between the set of points and the plurality of curves; and

a parameter extraction module configured to extract the parameters of the tool from the optimized curves.

15. The system of claim 14 , wherein the virtual slicing module is configured to slice a point cloud, or a triangular mesh to obtain the set of points corresponding to the pre-determined section.

16. The system of claim 15 , wherein the density of the point cloud is substantially high.

17. The system of claim 15 , wherein the virtual slicing module is configured to slice the point cloud or the triangular mesh based upon a defined slicing thickness, or a user-defined section.

18. The system of claim 14 , wherein the virtual slicing module is configured to perform a radial slicing, or an axial slicing, or a pre-determined direction slicing or a cylindrical slicing of the point cloud.

19. The system of claim 18 , wherein the cylindrical slicing of the point cloud facilitates estimation of flute number points on a cutting edge of the tool.

20. The system of claim 14 , further comprising a pre-processing module configured to pre-process the point cloud via noise filtering, or data compensation, or multi-view data merge, or combinations thereof.

21. The system of claim 14 , wherein the tool comprises a ball end mill, or a flat end mill, or a drill, or a reamer.

22. The system of claim 21 , wherein the parameters for the tool comprise a rake angle, or a primary relief angle, or a secondary clearance, or a land width, or a shank diameter, or a cutting diameter, or a core diameter, or combinations thereof.

23. A method for virtually slicing a point cloud at a pre-determined section corresponding to a surface of a tool, comprising:

pre-processing the point cloud via a filter to generate a first set of points;

selecting a slicing plane for segmenting the first set of points;

projecting the first set of points to the selected slicing plane to generate a second set of points at a specified section; and

determining parameters corresponding to the tool based upon the second set of points.

24. The method of claim 23 , wherein the slicing plane comprises an axial plane, or a radial plane, or a cylindrical plane.

25. The method of claim 24 , further comprising specifying a slicing thickness for the slicing plane based upon a point density of the set of points.

26. The of method claim 23 , wherein the point cloud comprises a triangular mesh model.

27. The method of claim 26 , further comprising estimating intersection points between the slicing plane and borders of each triangle of the triangular mesh model.

28. A memory storing a computer program for estimating parameters of a tool, comprising:

code for virtually slicing a point cloud corresponding to a surface of the tool at a pre-determined section to obtain a set of points on the pre-determined section;

code for generating a plurality of curves through the set of points and optimizing the plurality of curves to generate optimized fitting curves; and

code for extracting the parameters from the optimized fitting curves.

29. The memory of claim 28 , further comprising code for pre-processing the point cloud via noise filtering, or data compensation, or multi-view data merge, or combinations thereof.