Method for analyzing and optimizing a machining process
First Claim
1. A method for optimizing machining parameters for a milling process performed on a work piece using a cutting tool, the method comprising:
- performing finite element analysis of cutting tool and work material interaction to determine an optimized combination of the machining parameters by varying work material properties, cutting tool geometry, cutting speed and feed rate;
performing mechanistic modeling of the milling process, using results of the finite element analysis, to provide optimized machining parameters for improved rate of material removal and tool life;
using a two-stage artificial neural network, wherein a first stage of the network provides output parameters including peak tool temperature and cutting forces in X and Y directions, for a combination of input reference parameters including tool rake angle, material cutting speed, and feed rate;
wherein the output parameters obtained from the first stage are considered to be baseline parameters;
applying acceptance criteria to the baseline parameters determine acceptable baseline parameters;
using the acceptable baseline parameters as input to a second stage of the network;
obtaining optimized values of machining parameters, including surface speed and feed rate, from the second stage of the network; and
utilizing the optimized values to improve work piece material removal rate and cutting tool life.
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Abstract
A method for optimizing machining parameters for a cutting process performed on a work piece. Finite element analysis of cutting tool and work material interaction is initially performed. Mechanistic modeling of the cutting process, using results of the finite element analysis, is then performed to provide optimized machining parameters for improved rate of material removal and tool life. Optionally, a two-stage artificial neural network may be supplementally employed, wherein a first stage of the network provides output parameters including peak tool temperature and cutting forces in X and Y directions, for a combination of input reference parameters including tool rake angle, material cutting speed, and feed rate.
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Citations
14 Claims
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1. A method for optimizing machining parameters for a milling process performed on a work piece using a cutting tool, the method comprising:
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performing finite element analysis of cutting tool and work material interaction to determine an optimized combination of the machining parameters by varying work material properties, cutting tool geometry, cutting speed and feed rate; performing mechanistic modeling of the milling process, using results of the finite element analysis, to provide optimized machining parameters for improved rate of material removal and tool life; using a two-stage artificial neural network, wherein a first stage of the network provides output parameters including peak tool temperature and cutting forces in X and Y directions, for a combination of input reference parameters including tool rake angle, material cutting speed, and feed rate; wherein the output parameters obtained from the first stage are considered to be baseline parameters; applying acceptance criteria to the baseline parameters determine acceptable baseline parameters; using the acceptable baseline parameters as input to a second stage of the network; obtaining optimized values of machining parameters, including surface speed and feed rate, from the second stage of the network; and utilizing the optimized values to improve work piece material removal rate and cutting tool life. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for optimizing machining parameters for a turning process performed on a work piece using a cutting tool, the method comprising:
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performing finite element analysis of cutting tool and work material interaction to determine an optimized combination of the machining parameters, using a finite element model, by varying work material properties, cutting tool geometry, cutting speed and feed per tooth; performing mechanistic modeling of the turning process, using results of the finite element analysis, to provide optimized machining parameters for improved rate of material removal and tool life; using a two-stage artificial neural network, wherein a first stage of the network provides output parameters including peak tool temperature and cutting forces in X and Y directions, for a combination of input reference parameters including tool rake angle, material cutting speed, and feed per tooth; wherein the output parameters obtained from the first stage are considered to be baseline parameters; applying acceptance criteria to the baseline parameters determine acceptable baseline parameters; using the acceptable baseline parameters as input to a second stage of the network; obtaining optimized values of machining parameters, including surface speed and feed per tooth, from the second stage of the network; and utilizing the optimized values to improve work piece material removal rate and cutting tool life. - View Dependent Claims (9, 10, 11, 12, 13)
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14. A method for optimizing machining parameters for a cutting process performed on a work piece using a cutting tool, the method comprising:
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performing finite element analysis of cutting tool and work material interaction; performing mechanistic modeling and analysis of the cutting process, using results of the finite element analysis, to provide machining parameters for an improved rate of material removal and tool life; determining an optimized combination of the machining parameters, using a finite element model, by varying work material properties, cutting tool geometry, cutting speed and feed rate; using a two-stage artificial neural network, wherein a first stage of the network provides output parameters including peak tool temperature and cutting forces in X and Y directions, for a combination of input reference parameters including tool rake angle, material cutting speed, and feed rate; wherein the output parameters obtained from the first stage are considered to be baseline parameters; applying acceptance criteria to the baseline parameters determine acceptable baseline parameters; using the acceptable baseline parameters as input to a second stage of the network; obtaining optimized values of machining parameters, including surface speed and feed rate, from the second stage of the network; and utilizing the optimized values to improve work piece material removal rate and cutting tool life.
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Specification