PMID- 35632290
OWN - NLM
STAT- MEDLINE
DCOM- 20220531
LR  - 20220716
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 10
DP  - 2022 May 20
TI  - Beam Offset Detection in Laser Stake Welding of Tee Joints Using Machine Learning 
      and Spectrometer Measurements.
LID - 10.3390/s22103881 [doi]
LID - 3881
AB  - 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.
FAU - Jadidi, Aydin
AU  - Jadidi A
AD  - Department of Engineering Science, University West, 461-32 Trollhattan, Sweden.
FAU - Mi, Yongcui
AU  - Mi Y
AD  - Department of Engineering Science, University West, 461-32 Trollhattan, Sweden.
FAU - Sikstrom, Fredrik
AU  - Sikstrom F
AUID- ORCID: 0000-0001-5734-294X
AD  - Department of Engineering Science, University West, 461-32 Trollhattan, Sweden.
FAU - Nilsen, Morgan
AU  - Nilsen M
AUID- ORCID: 0000-0002-8771-7404
AD  - Department of Engineering Science, University West, 461-32 Trollhattan, Sweden.
FAU - Ancona, Antonio
AU  - Ancona A
AUID- ORCID: 0000-0002-6247-5429
AD  - Department of Engineering Science, University West, 461-32 Trollhattan, Sweden.
AD  - Physics Department, University of Bari, Via Orabona 4, 70126 Bari, Italy.
LA  - eng
GR  - 20170315/Swedish Knowledge Foundation, project AdOpt/
PT  - Journal Article
DEP - 20220520
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Lasers
MH  - Machine Learning
MH  - Neural Networks, Computer
MH  - Support Vector Machine
MH  - *Welding
PMC - PMC9146067
OTO - NOTNLM
OT  - feature selection
OT  - laser beam offset
OT  - laser beam welding
OT  - machine learning
OT  - spectrometer
OT  - tee joint
COIS- The authors declare no conflict of interest.
EDAT- 2022/05/29 06:00
MHDA- 2022/06/01 06:00
PMCR- 2022/05/20
CRDT- 2022/05/28 01:43
PHST- 2022/03/15 00:00 [received]
PHST- 2022/04/25 00:00 [revised]
PHST- 2022/05/09 00:00 [accepted]
PHST- 2022/05/28 01:43 [entrez]
PHST- 2022/05/29 06:00 [pubmed]
PHST- 2022/06/01 06:00 [medline]
PHST- 2022/05/20 00:00 [pmc-release]
AID - s22103881 [pii]
AID - sensors-22-03881 [pii]
AID - 10.3390/s22103881 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 May 20;22(10):3881. doi: 10.3390/s22103881.