Method for analyzing and optimizing a machining process

A method for optimizing machining parameters for a turning 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, 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.

A method for optimizing machining parameters for a cutting process performed on a work piece using a cutting tool, the method comprising:

• 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.