A methodological approach to multisensor classification for innovative laser material processing units

Author

Alippi, Cesare ; Braione, Pietro ; Piuri, Vincenzo ; Scotti, Fabio

Author_Institution

Dept. of Electron. & Inf., Politecnico di Milano, Italy

Volume

3

fYear

2001

fDate

2001

Firstpage

1762

Abstract

Online quality detection and online laser beam control are important research topics to improve the overall quality of presentday laser beam material processing units. In both cases innovative units are being studied where the state is monitored by a set of heterogeneous in-process sensors conveying a large amount of information. However, low experimental reproducibility, lack of dominion knowledge and high costs greatly limit our ability to find an optimal solution. In this paper we propose a methodology to guide the engineer´s design choices towards an optimal implementation of the inductive classifier

Keywords

Bayes methods; design of experiments; feature extraction; inference mechanisms; laser beam machining; laser beam welding; neural nets; pattern classification; process monitoring; sensor fusion; statistical process control; Bayesian theory; design choices; experiment design; feature extraction; heterogeneous in-process sensors; inductive classifier; innovative units; laser cutting; laser material processing units; laser welding; methodological approach; multisensor classification; neural network; online laser beam control; online quality detection; optimal implementation; sensor fusion; Geometrical optics; Laser beams; Laser fusion; Laser tuning; Materials processing; Optical materials; Power lasers; Reflectivity; Reproducibility of results; Spot welding;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 2001. IMTC 2001. Proceedings of the 18th IEEE

Conference_Location

Budapest

ISSN

1091-5281

Print_ISBN

0-7803-6646-8

Type

conf

DOI

10.1109/IMTC.2001.929503

Filename

929503