Sökning: onr:"swepub:oai:DiVA.org:hv-18671" >
Beam Offset Detecti...
Beam Offset Detection in Laser Stake Welding of Tee Joints Using Machine Learning and Spectrometer Measurements
-
- Jadidi, Aydin, Postdoktor (författare)
- Högskolan Väst,Avdelningen för produktionssystem (PS),PTW
-
- Mi, Yongcui, 1986- (författare)
- Högskolan Väst,Avdelningen för produktionssystem (PS),PTW
-
- Sikström, Fredrik, 1971- (författare)
- Högskolan Väst,Avdelningen för produktionssystem (PS),PTW
-
visa fler...
-
- Nilsen, Morgan (författare)
- Högskolan Väst,Avdelningen för produktionssystem (PS),PTW
-
- Ancona, Antonio, 1974- (författare)
- Högskolan Väst,Avdelningen för produktionssystem (PS),Physics Department, University of Bari,Bari (ITA),PTW
-
visa färre...
-
(creator_code:org_t)
- 2022-05-20
- 2022
- Engelska.
-
Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:10
- Relaterad länk:
-
https://doi.org/10.3...
-
visa fler...
-
https://hv.diva-port... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.3...
-
visa färre...
Abstract
Ämnesord
Stäng
- Laser beam welding offers high productivity and relatively low heat input and is one key enabler for efficient manufacturing of sandwich constructions. However, the process is sensitive to how the laser beam is positioned with regards to the joint, and even a small deviation of the laser beam from the correct joint position (beam offset) can cause severe defects in the produced part. With tee joints, the joint is not visible from top side, therefore traditional seam tracking methods are not applicable since they rely on visual information of the joint. Hence, there is a need for a monitoring system that can give early detection of beam offsets and stop the process to avoid defects and reduce scrap. In this paper, a monitoring system using a spectrometer is suggested and the aim is to find correlations between the spectral emissions from the process and beam offsets. The spectrometer produces high dimensional data and it is not obvious how this is related to the beam offsets. A machine learning approach is therefore suggested to find these correlations. A multi-layer perceptron neural network (MLPNN), support vector machine (SVM), learning vector quantization (LVQ), logistic regression (LR), decision tree (DT) and random forest (RF) were evaluated as classifiers. Feature selection by using random forest and non-dominated sorting genetic algorithm II (NSGAII) was applied before feeding the data to the classifiers and the obtained results of the classifiers are compared subsequently. After testing different offsets, an accuracy of 94% was achieved for real-time detection of the laser beam deviations greater than 0.9 mm from the joint center-line.
Ämnesord
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
Nyckelord
- laser beam offset; feature selection; laser beam welding; machine learning; spectrometer; tee joint
- Production Technology
- Produktionsteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
-
Sensors
(Sök värdpublikationen i LIBRIS)
Till lärosätets databas