Method and system for designing a cutting tool

• US 5,377,116 A
• Filed: 07/01/1991
• Issued: 12/27/1994
• Est. Priority Date: 07/01/1991
• Status: Expired due to Fees

• Current Assignee: Valenite LLC (Sandvik AB)
• Original Assignee: Valenite Inc.
• Inventors: Zimmerman, Charles E., Wayne, Steven F., O'Neil, David A., Val, Yefim
• Primary Examiner(s): Smith, Jerry
• Assistant Examiner(s): BROWN, THOMAS
• Application Number:
  US07/724,305
• Time in Patent Office: 1,275 Days
• Field of Search: 364/474.02, 364/474.24, 364/474.15, 364/474.16, 364/474.17, 364/474.24, 364/474.26, 364/468, 364/578, 364/551.01, 364/551.02, 395/902-904, 395/911-912, 395/914
• US Class Current: 700/175
• CPC Class Codes: G05B 19/4097 characterised by using design data to control NC machines, e.g. CAD/CAM G05B19/4093 takes precedence
• CPC Class Codes: G05B 2219/35044 Tool, design of tool, mold, die tooling
• CPC Class Codes: G06F 2119/08 Thermal analysis or thermal optimisation
• CPC Class Codes: G06F 30/23 using finite element methods [FEM] or finite difference methods [FDM]
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
• CPC Class Codes: Y10S 706/904 Manufacturing or machine, e.g. agricultural machinery, machine tool

Abstract

A method of designing a cutting tool uses finite element numerical models to predict a response of the tool during a simulated cutting operation on a workpiece, and to simulate a chip-flow phenomenon occurring during the cutting operation. The chip-flow model incorporates representations of a fracture mechanism describing a chip separation phenomenon, a heat-generating mechanism describing a thermal coupling phenomenon, and a shear localization mechanism describing a shearing phenomenon wherein these phenomena occur during the cutting operation. The predicted tool response and the chip-flow simulation are evaluated by an artificial intelligence system to render rule-based judgments which are embodied in recommendations for continuously modifying the models and input design variables until the simulation and response converge to an optimal result.

Claims

1. A method of designing a cutting tool based on a simulated cutting operation characterized by linear and non-linear physical phenomenon defining the cutting operation of a tool on a workpiece, comprising the steps of:

representing said linear phenomenon with a modifiable linear model which simulates said linear phenomenon in response to value assignments for modifiable input parameters;

representing said non-linear phenomenon with a modifiable non-linear model which simulates said non-linear phenomenon in response to value assignments for modifiable input parameters;

exercising said linear model to derive the simulation of said linear phenomenon, and exercising said non-linear model to derive the simulation of said non-linear phenomenon,

wherein said models predict a response of said tool and simulate a chip-flow phenomenon;

evaluating said simulations with an adaptive judgment base in a dynamic learning environment;

integrating said simulations and evaluation into said learning environment; and

continuously modifying said input parameters in accordance with said evaluation until an acceptable evaluation corresponding to an optimized tool design which generates a desired chip-flow is achieved.

2. The method as recited in claim 1 wherein

said non-linear model incorporates a representation of a fracture mechanism describing a chip separation phenomenon.

3. The method as recited in claim 2 wherein

said non-linear model incorporates a representation of a heat generating mechanism describing a thermal coupling phenomenon.

4. The method as recited in claim 3 wherein

said non-linear model incorporates a representation of a shear localization mechanism describing a shearing phenomenon.

5. The method as recited in claim 2 wherein

said fracture mechanism defines a release of said chip from the workpiece and is represented by a debonding finite element in said non-linear model.

6. The method as recited in claim 3 wherein

said heat-generating mechanism represents permanent deformation of said chip, fracture of said chip from said workpiece, and sliding of said chip with friction along a contact face of said cutting tool.

7. The method as recited in claim 6 further includes the step of:

modeling said permanent deformation by representing in the non-linear model that a fraction of heat generated by said deformation is introduced into said deforming chip;

modeling said fracture by representing in the non-linear model that a fraction of heat liberated by said debonding element upon release from said workpiece is introduced into said chip; and

modeling said chip sliding by representing in the non-linear model that a selected fraction of heat which is generated by said sliding is introduced into said chip while the remaining fraction is introduced into said tool.

8. The method as recited in claim 4 wherein

said heat localization mechanism represents a shear strain discontinuity in a region of said workpiece and is represented by shear discontinuity finite elements in said non-linear model.

9. The method as recited in claim 8 further includes the steps of:

selecting said shear discontinuity finite elements so as to properly describe said shear localization mechanism;

discretizing said discontinuity elements; and

modifying material property-related parameters in said non-linear to be temperature-dependent.

10. The method as recited in claim 1 wherein said models mathematically relate an input vector of tool parameters to an output vector of performance indices together constituting said tool response, and mathematically relate an input vector of chip-flow parameters to an output vector of performance indices together constituting said chip-flow simulation.

11. The method as recited in claim 10 wherein the step of performing an evaluation further includes the steps of:

applying said performance indices to an adaptive set of rules to render judgments; and

accessing cutting information from an updatable database, wherein said database includes machining parameters, results of previous cutting operations, and material properties.

12. The method as recited in claim 11 includes the step of manufacturing a cutting tool based on said optimal tool design.

13. A method of designing a cutting tool, comprising the steps of:

providing a modifiable finite element tool model which predicts a response of a tool during a simulated cutting operation on a workpiece, wherein said tool model has an input tool vector of adjustable tool parameters;

providing a modifiable finite element chip-flow model which simulates a chip-flow phenomenon occurring during said cutting operation, wherein said chip-flow model has an input chip-flow vector of adjustable chip-flow parameters;

deriving the predicted response of said tool by exercising said tool model with said input tool vector, and deriving the simulation of said chip-flow phenomenon by exercising said chip-flow model with said input chip-flow vector;

performing an evaluation of the predicted tool response and the chip-flow simulation;

based on said evaluation, continuously adjusting the input tool vectors and the input chip-flow vector, and continuously modifying the tool model and the chip-flow model until said response and simulation converge to a desired result corresponding to an optimal tool design,

wherein said tool model mathematically relates said input vector of tool parameters to an output vector of performance indices together constituting said tool response, and said chip-flow model mathematically relates said input vector of chip-flow parameters to an output vector of performance indices together constituting said simulation;

applying said performance indices to an adaptive set of rules to render judgments; and

accessing cutting information from an updatable database, wherein said database includes results of previous cutting operations.

14. The method as recited in claim 13 wherein

said chip-flow model incorporates a representation of a fracture mechanism describing a chip separation phenomenon.

15. The method as recited in claim 14 wherein

said chip-flow model incorporates a representation of a heat generating mechanism describing a thermal coupling phenomenon.

16. The method as recited in claim 15 wherein

said chip-flow model incorporates a representation of a shear localization mechanism describing a shearing phenomenon.

17. The method as recited in claim 14 wherein

said fracture mechanism defines a release of said chip from the workpiece and is represented by a debonding finite element in said chip-flow model.

18. The method as recited in claim 15 wherein

said heat-generating mechanism represents permanent deformation of said chip, fracture of said chip from said workpiece, and sliding of said chip with friction along a contact face of said cutting tool.

19. The method as recited in claim 18 further includes the step of:

modeling said permanent deformation by representing in the chip-flow model that a fraction of heat generated by said deformation is introduced into said deforming chip;

modeling said fracture by representing in the chip-flow model that a fraction of heat liberated by said debonding element upon release from said workpiece is introduced into said chip; and

modeling said chip sliding by representing in the chip-flow model that a selected fraction of heat which is generated by said sliding is introduced into said chip while the remaining fraction is introduced into said tool.

20. The method as recited in claim 16 wherein

said heat localization mechanism represents a shear strain discontinuity in a region of said workpiece and is represented by shear discontinuity finite elements in said chip-flow model.

21. The method as recited in claim 20 further includes the steps of:

selecting said shear discontinuity finite elements so as to properly describe said shear localization mechanism;

discretizing said discontinuity elements; and

modifying material property-related parameters in said chip-flow to be temperature-dependent.

22. The method as recited in claim 13 includes the step of manufacturing a cutting tool based on said optimal tool design.

23. A method of designing a cutting tool, comprising the steps of:

creating a finite element mesh from a tool design;

constructing from said finite element mesh a finite element tool model which predicts a response of said tool during a simulated cutting operation on a workpiece;

wherein said tool model mathematically relates a plurality of adjustable tool parameters to a plurality of performance indices together constituting said tool response;

constructing a non-linear finite element chip-flow model which simulates a chip-flow phenomenon occurring during said cutting operation;

wherein said chip-flow model mathematically relates a plurality of adjustable chip-flow parameters to a plurality of performance indices together constituting said simulation;

said chip-flow model incorporating representations of a fracture mechanism describing a chip separation phenomenon, a heat-generating mechanism describing a thermal coupling phenomenon, and a shear localization mechanism describing a shearing phenomenon, wherein said phenomena occur during said cutting operation;

(a) deriving the predicted response of said tool by exercising said tool model with said tool parameters, and deriving the simulation of said chip-flow phenomenon by exercising said chip-flow model with said chip-flow parameters;

(b) evaluating the predicted tool response and the chip-flow simulation by applying said performance indices to an adaptive set of rules embodying predetermined acceptability criteria, whereby said rules generate a correlation measure between said indices and said criteria;

(c) said evaluating step including accessing cutting information from an updatable database which includes results of past cutting operations;

(d) rendering judgments based on said correlation measure for assigning acceptability levels to said performance indices;

(e) providing a recommendation embodying said judgments;

(f) modifying said rules in accordance with said recommendation;

(g) adjusting said tool parameters and said chip-flow parameters in accordance with said recommendation to generate a tool design;

(h) updating said database with information representing the predicted response of said tool and the simulation of said chip formation process; and

repeating steps (a) through (h) using said generated tool design until optimal correlation measures are achieved.

24. The method as recited in claim 23 wherein the step of deriving the simulation of said chip formation process includes the step of:

performing an explicit time integration analysis.

25. The method as recited in claim 23 wherein the step of deriving the simulation of said chip formation process includes the step of:

performing an implicit time integration analysis.

26. The method as recited in claim 23 includes the steps of:

storing parameters from the derived response of said tool and the simulation of said chip formation process in said database.

27. The method as recited in claim 26 includes the steps of:

interactively viewing an animated rendering of said cutting process and concurrently monitoring the performance indices of said tool response and chip-flow simulation.

28. The method as recited in claim 23 includes the steps of:

prior to constructing said cutting tool model and said chip-flow model, producing line drawings of said tool for visual inspection using computer-aided designing.

29. The method as recited in claim 23 wherein

said fracture mechanism defines a release of said chip from the workpiece and is represented by a debonding finite element in said chip-flow model.

30. The method as recited in claim 29 wherein

said heat-generating mechanism represents permanent deformation of said chip, fracture of said chip from said workpiece, and sliding of said chip with friction along a contact face of said cutting tool.

31. The method as recited in claim 30 further includes the step of:

modeling said permanent deformation by representing in the chip-flow model that a fraction of heat generated by said deformation is introduced into said deforming chip;

modeling said fracture by representing in the chip-flow model that a fraction of heat liberated by said debonding element upon release from said workpiece is introduced into said chip; and

modeling said chip sliding by representing in the chip-flow model that a selected fraction of heat which is generated by said sliding is introduced into said chip while the remaining fraction is introduced into said tool.

32. The method as recited in claim 23 wherein

said shear localization mechanism represents a shear strain discontinuity in a region of said workpiece and is represented by shear discontinuity finite elements in said chip-flow model.

33. The method as recited in claim 32 further includes the steps of:

selecting said shear discontinuity finite elements so as to properly describe said shear localization mechanism;

discretizing said discontinuity elements; and

modifying material property-related parameters in said chip-flow model to be temperature-dependent.

34. The method as recited in claim 23 further includes the step of:

constructing a cutting tool based on said optimal tool design in a computer-aided manufacturing operation.

35. The method as recited in claim 34 includes the steps of:

producing a punch and die combination in accordance with said optimal tool design;

pressing a powder material to form a green body using said punch and die combination;

densifying said green body; and

grinding and then coating said densified body to produce said cutting tool.

36. The method as recited in claim 23 further includes the step of:

modifying said tool model and said chip-flow model.

37. A method of manufacturing a cutting tool, comprising the steps of:

performing a tool study based on a tool design, comprising the steps of;

creating a finite element mesh from said tool design;

constructing from said finite element mesh a finite element tool model which predicts a response of said tool during a simulated cutting operation on a workpiece;

wherein said tool model mathematically relates a plurality of adjustable tool parameters to a plurality of performance indices together constituting said tool response;

constructing a non-linear finite element chip-flow model which simulates a chip-flow phenomenon occurring during said cutting operation;

wherein said chip-flow model mathematically relates a plurality of adjustable chip flow parameters to a plurality of performance indices together constituting said simulation;

said chip-flow model incorporating representations of a fracture mechanism describing a chip separation phenomenon, a heat-generating mechanism describing a thermal coupling phenomenon, and a shear localization mechanism describing a shearing phenomenon, wherein said phenomena occur during said cutting operation;

deriving the predicted response of said tool by exercising said tool model with said tool parameters, and deriving the simulation of said chip-flow phenomenon by exercising said chip-flow model with said chip-flow parameters;

evaluating the predicted tool response and the chip-flow simulation by applying said performance indices to an adaptive set of rules embodying predetermined acceptability criteria, whereby said rules generate correlation measures between said indices and said criteria;

said evaluating step including accessing cutting information from an updatable database which includes results of past cutting operations;

rendering judgments based on said correlation measure for assigning acceptability levels to said performance indices;

providing a recommendation embodying said judgments;

modifying said rules in accordance with said recommendation;

adjusting said tool parameters and said chip-flow parameters in accordance with said recommendation, wherein said parameter adjustments generate a new tool design;

modifying said tool model and said chip-flow model in accordance with said recommendation; and

updating said database with information representing the predicted response of said tool and the simulation of said chip formation process;

repeating the step of performing a tool study using said generated tool design until optimal correlation measures are achieved; and

constructing a cutting tool based on a tool design having said optimal correlation measures.

38. A system for designing a cutting tool based on a simulated cutting operation characterized by linear and non-linear physical phenomenon defining said cutting operation, wherein said linear phenomenon is represented by a modifiable linear model which simulates said linear phenomenon in response to modifiable input parameters, comprising:

model means for representing said non-linear phenomenon with a modifiable non-linear model which simulates said non-linear phenomenon in response to modifiable input parameters;

means for exercising said linear model to derive the simulation of said linear phenomenon, and for exercising said non-linear model to derive the simulation of said non-linear phenomenon,

wherein said model means and said exercising means predict a response of said tool and simulate a chip-flow phenomenon;

means for evaluating said simulations with an adaptive judgment base in a dynamic learning environment;

means for integrating said simulations and evaluation into said learning environment; and

means responsive to said evaluation for continuously modifying said input parameters until an acceptable evaluation corresponding to an optimized tool design which generates a desired chip-flow is achieved.

39. The system as recited in claim 38 wherein

said non-linear model incorporates a representation of a fracture mechanism describing a chip separation phenomenon.

40. The system as recited in claim 39 wherein

said non-linear model incorporates a representation of a heat generating mechanism describing a thermal coupling phenomenon.

41. The system as recited in claim 40 wherein

said non-linear model incorporates a representation of a shear localization mechanism describing a shearing phenomenon.

42. The system as recited in claim 39 wherein

said fracture mechanism defines a release of said chip from the workpiece and is represented by a debonding finite element in said non-linear model.

43. The system as recited in claim 40 wherein

said heat-generating mechanism represents permanent deformation of said chip, fracture of said chip from said workpiece, and sliding of said chip with friction along a contact face of said cutting tool.

44. The system as recited in claim 43 further comprises:

means for modeling said permanent deformation by representing in the non-linear model that a fraction of heat generated by said deformation is introduced into said deforming chip;

means for modeling said fracture by representing in the non-linear model that a fraction of heat liberated by said debonding element upon release from said workpiece is introduced into said chip; and

means for modeling said chip sliding by representing in the non-linear model that a selected fraction of heat which is generated by said sliding is introduced into said chip while the remaining fraction is introduced into said tool.

45. The system as recited in claim 41 wherein

said heat localization mechanism represents a shear strain discontinuity in a region of said workpiece and is represented by shear discontinuity finite elements in said non-linear model.

46. The system as recited in claim 45 further comprises:

means for selecting said shear discontinuity finite elements so as to properly describe said shear localization mechanism;

means for discretizing said discontinuity elements; and

means for modifying material property-related parameters in said non-linear to be temperature-dependent.

47. The system as recited in claim 38 wherein said tool model mathematically relates said input vector of tool parameters to an output vector of performance indices together constituting said tool response;

and

said non-linear model mathematically relates said input vector of non-linear parameters to an output vector of performance indices together constituting said chip-flow simulation.

48. The system as recited in claim 47 wherein said evaluation means comprises:

means for applying said performance indices to an adaptive set of rules to render judgments; and

means for accessing cutting information from an updatable database, wherein said database includes results of previous cutting operations.

49. The system as recited in claim 48 further includes:

means for manufacturing a cutting tool based on said optimal tool design.

50. A system for designing a cutting tool, comprising:

means for providing a modifiable cutting tool finite element simulation model which predicts a response of a tool during a simulated cutting operation on a workpiece, wherein said cutting tool model has an input tool vector of adjustable tool parameters;

means for providing a modifiable finite element chip-flow model which simulates a chip-flow phenomenon occurring during said cutting operation, wherein said chip-flow model has an input chip-flow vector of adjustable chip-flow parameters;

first analysis means responsive to said input tool vector for exercising said tool model and for outputting the predicted tool response;

second analysis means responsive to said input chip-flow vector for exercising said chip-flow model and for outputting the chip-flow simulation;

optimization means coupled to said first and second analysis means for converging said predicted tool response and said chip-flow simulation to a desired result corresponding to an optimal tool design;

said optimization means including artificial intelligence means for evaluating said predicted tool response and said chip-flow simulation, and for providing a recommendation; and

said optimization means further including means coupled to said first and second analysis means and responsive to said recommendation for adjusting the input tool vector and the input chip-flow vector, and for modifying said tool model and said chip-flow model, thereby prompting said first and second analysis means to reexercise said tool and chip-flow models, respectively.

51. The system as recited in claim 50 wherein said artificial intelligence means includes

rule means for comparing said predicted tool response and said chip-flow simulation with predetermined acceptability criteria;

and

means responsive to said comparisons for rendering judgments embodied in said recommendation.

52. The system as recited in claim 51 further comprising:

a computer-aided manufacturing subsystem operatively coupled to said optimization means for producing a tool based on said optimal tool design.

53. A system for designing a cutting tool, comprising:

means for creating a finite element mesh from a tool design;

means responsive to said finite element mesh for providing a finite element tool model which predicts a response of said tool during a simulated cutting operation on a workpiece;

wherein said tool model mathematically relates an input vector of adjustable tool parameters to an output vector of performance indices together constituting said tool response;

means for providing a non-linear finite element chip-flow model which simulates a chip-flow phenomenon occurring during said cutting operation;

wherein said chip-flow model mathematically relates an input vector of adjustable chip flow parameters to an output vector of performance indices together constituting said chip-flow simulation;

said chip-flow model incorporating representations of a fracture mechanism describing a chip separation phenomenon, a heat-generating mechanism describing a thermal coupling phenomenon, and a shear localization mechanism describing a shearing phenomenon, wherein said phenomena occur during said cutting operation;

means for exercising said tool model and said chip-flow model to derive the predicted response of said tool and the simulation of said chip-flow phenomenon;

artificial intelligence means for evaluating the predicted tool response and the chip-flow simulation by applying said performance indices to an adaptive set of rules embodying predetermined acceptability criteria, whereby said rules generate correlation measures between said indices and said criteria;

means operatively coupled to said artificial intelligence means for accessing cutting information from an updatable database which includes results of past cutting operations;

means responsive to said correlation measures for rendering judgments which assign acceptability levels to said performance indices;

means for providing a recommendation embodying said judgments;

means responsive to said recommendation for modifying said rules;

means coupled to said exercising means and responsive to said recommendation for adjusting said tool parameters and said chip-flow parameters so as to reexercise said tool model and said chip-flow model; and

means for updating said database with information representing the predicted tool response and the chip-flow simulation.