Interpreting tree-based prediction models and their data in machining processes

Andres Bustillo , Maciej Grzenda , Bohdan Macukow

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.
Author Andres Bustillo
Andres Bustillo,,
-
, Maciej Grzenda ZSPI
Maciej Grzenda,,
- Department of Information Processing Systems
, Bohdan Macukow ZSPI
Bohdan Macukow,,
- Department of Information Processing Systems
Journal seriesIntegrated Computer-Aided Engineering, ISSN 1069-2509
Issue year2016
Vol23
No4
Pages349-367
Publication size in sheets0.9
Keywords in EnglishVisualization, deep drilling, machining processes, prediction, dimensionality reduction
Abstract in PolishTechniki uczenia maszynowego często pozwalają na przewidywanie wyniku procesu przemysłowego z wykorzystaniem wiedzy o ustawieniach urządzenia. Niestety, model urządzenia wielokrotnie nie może oddać wszystkich cech istotnych z punktu widzenia warunków przemysłowych. Ponadto, kluczowym zagadnieniem jest wiarygodność predykcji wykonywanej dla różnych ustawień urządzenia. Praca poddaje analizie techniki modelowania i wizualizacji oraz proponuje nowe rozwiązania bazujące na integracji redukcji wymiarowości oraz drzew warunkowego wnioskowania. Zaproponowane metody umożliwiają m.in. ocenę statystycznej wiarygodności prognoz generowanych przez model bazujący na oryginalnych danych i danych poddanych transformacji.
DOIDOI:10.3233/ICA-160513
URL http://content.iospress.com/articles/integrated-computer-aided-engineering/ica513
Languageen angielski
Score (nominal)40
ScoreMinisterial score = 35.0, 28-11-2017, ArticleFromJournal
Ministerial score (2013-2016) = 40.0, 28-11-2017, ArticleFromJournal
Publication indicators WoS Impact Factor: 2016 = 5.264 (2) - 2016=3.06 (5)
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