GAMHE

Group of advanced Automation of Machines, Highly complex processes and Environments

Participant: Rodolfo Haber

Conference: IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014), December 9-12, 2014, Orlando, Florida, USA.

Summary: This paper presents one strategy for modeling and optimization of a microdilling process. Experimental work has been carried out for measuring the thrust force for five different commonly used alloys, under several cutting conditions. An artificial neural network-based model was implemented for modelling the thrust force. Neural model showed a high goodness of fit and appropriate generalization capability. The optimization process was executed by considered two different and conflicting objectives: the unit machining time and the thrust force (based on the previously obtained model). A multiobjective genetic algorithm was used for solving the optimization problem and a set of non-dominated solutions was obtained. The Pareto‚Äôs front representation was depicted and used for assisting the decision making process.