Paper Info
Title
Single and ensemble classifiers for defect prediction in sheet metal forming under variability
Abstract
This paper presents an approach, based on machine learning techniques, to predict the occurrence of defects in sheet metal forming processes, exposed to sources of scatter in the material properties and process parameters. An empirical analysis of performance of ML techniques is presented, considering both single learning and ensemble models. These are trained using data sets populated with numerical simulation results of two sheet metal forming processes: U-Channel and Square Cup. Data sets were built for three distinct steel sheets. A total of eleven input features, related to the mechanical properties, sheet thickness and process parameters, were considered; also, two types of defects (outputs) were analysed for each process. The sampling data were generated, assuming that the variability of each input feature is described by a normal distribution. For a given type of defect, most single classifiers show similar performances, regardless of the material. When comparing single learning and ensemble models, the latter can provide an efficient alternative. The fact that ensemble predictive models present relatively high performances, combined with the possibility of reconciling model bias and variance, offer a promising direction for its application in industrial environment.
Year
DOI
Venue
2020
10.1007/s00521-019-04651-6
NEURAL COMPUTING & APPLICATIONS
Keywords
DocType
Volume
Machine learning,Ensemble learning,Defect prediction,Sheet metal forming
Journal
32.0
Issue
ISSN
Citations 
16
0941-0643
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
M. A. Dib110.35
N. J. Oliveira210.35
A. E. Marques310.35
M. C. Oliveira441.71
J. V. Fernandes510.69
Bernardete Ribeiro675882.07
P. A. Prates710.69