Open AccessOPTIMIZATION OF CNC PARAMETERS ACCORDING TO PRODUCTIVITY CRITERIA USING A MACHINE MODEL BASED ON NEURAL NETWORKS
Author(s) -
Javier Arenas López,
Rosa Basagoiti,
Maite Beamurgia Bengoa,
Jorge de Alegría Sáenz de Martínez Castillo
Publication year - 2020
Publication title -
dyna
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.177
H-Index - 11
eISSN - 1989-1490
pISSN - 0012-7361
DOI - 10.6036/9399
Subject(s) - artificial neural network , numerical control , machine tool , computer science , machining , smoothness , process (computing) , productivity , set (abstract data type) , parametrization (atmospheric modeling) , engineering , artificial intelligence , mechanical engineering , mathematics , mathematical analysis , physics , quantum mechanics , radiative transfer , economics , macroeconomics , programming language , operating system
Every machine-tool user wants to maximize the productivity of their machines looking for balance between speed, precision and lifetime of mechanical components. Nevertheless, because CNCs have wide-ranging use, their correct parametrization for each case is key to achieving the desired objectives; on the other hand, minimizing the numbers of experimental tests to be performed on the machine is essential to reduce time and costs of the set-up process. In order to solve both difficulties, this paper presents a tool to give final user necessary information to properly adjust CNC parameters according to productivity criteria. The method makes use of experimental data to obtain a model of the machine based on neural networks. With this model machining time, geometric error and smoothness of any piece to be manufactured can be predicted, and therefore minimizing test on the real machine and recommending the appropriate values for the CNC.Keywords: optimization, CNC, neural network, model, machine tool, productivity criteria.