Method and system for designing a cutting tool

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.

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.

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.

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.

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.

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.