Abstract:
Manufacturing processes of mechanical parts by removal of material have extensive use in
aeronautic and automobile industry. The components obtained using these methods must satisfy
geometric properties, metallurgical and quality characteristics. To meet these requirements,
several experimental tests based on the selection of cutting conditions are often necessary before
manufacturing.
The objective of this thesis is to provide the tools to choose the conditions of intelligently
cutting from a sufficient number of experiments taking into account the relationship between the
cutting parameters (Vc, f and ap) and response variables (cutting forces, the surface condition and
wear of the cutting tool) through the response surface methodology (RSM) and the genetic
algorithm is analyzed and modeled. The resulting models are types: quadratic, linear, exponential,
Gilbert and genetic algorithm.
Quadratic models of response surface methodology associated with the multiple response
optimization technique is used to find the optimal values of cutting parameters with respect to the
minimization of cutting force, roughness and the tool wear.
