Affiliations: [a] Department of Civil Engineering, University of Burgos, Burgos, Spain | [b] Faculty of Mathematics and Information Science, Warsaw University of Technology, Warszawa, Poland
Correspondence:
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Corresponding author: Maciej Grzenda, Faculty of Mathematics and Information Science, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warszawa, Poland. Tel.: +48 22 621 93 12; Fax: +48 22 625 74 60; E-mail:[email protected]
Abstract: Machine-learning techniques frequently predict the
results of machining processes, based on pre-determined cutting tool
settings. By doing so, key parameters of a machined product can be predicted
before production begins. Nevertheless, a prediction model cannot capture
all the features of interest under real-life industrial conditions.
Moreover, careful assessment of prediction credibility is necessary for
accurate calibration; aspects that should be addressed through appropriate
modeling and visualization techniques. A machine process test problem is
proposed to analyze data-visualization techniques, in which a real data set
is analyzed that describes deep-drilling under different cutting and cooling
conditions. The main objective is the efficient fusion of visualization
techniques with the knowledge of industrial engineers. Common modeling and
visualization techniques were first surveyed, to contrast standard practice
with our novel approach. A hybrid technique combining conditional inference
trees with dimensionality reduction was then examined. The results show that
a process engineer will be able to estimate overall model accuracy and to
verify the extent to which accuracy depends on industrial process settings
and the statistical significance of model predictions. Moreover, evaluation
of the data set in terms of its sufficiency for modeling purposes will help
assess the credibility of these decisions.
Keywords: Visualization, deep drilling, machining processes, prediction, dimensionality reduction
