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Big Data Analytics for Process and Quality Monitoring in Gas Metal Arc Welding of Automotive PartsJoining Smart Technologies International Automotive Conference 08.05.2019

Luttmer, Michael; Thaler, Heiko; Dongus, Jürgen; Process Engineering

Fully automated optical weld seam inspection systems (e.g.VIRO VSI from VITRONIC)

Big Data Analytics for Process and Quality Monitoring in Gas Metal Arc Welding of Automotive Parts | TF/VRT | 08.05.2019 2

BasicsMotivation Database Analytics

• Only optical evaluation of theweld seam

• Measuring principle: numerically error based

• Additional inspection station

• No contribution to increaseprocess capability




• Measuring principle: numerically error based



Data Analytics on process data to increaseweld seam quality and process capability

Fully automated optical weld seam inspection systems (e.g. VIRO VSI from VITRONIC)




• Errors based on meassuringprinciples



Data Analytics on process data to increaseweld seam quality and process capability

Fully automated optical weld seam inspection trough systems such as the

VIRO VSI from VITRONIC



Consumption [ltr/100km] 10 15

Time [min] 60 45

Distance Stuttgart - Karlsruhe

Approach: Find features with significant correlation to your target

Target Who was speeding?



Color yellow blue

Add-ons climatronic bending lights

Distance Stuttgart - Karlsruhe

Approach: Find features with an significant correlation to your target



The „modern“ gas metal arc welding process is a mixture of different process types




• Controlable heatinput

• Stable arc length

• Better gapbridgeability

• Evenly rippled surface



How does our Database look like?

• Voltage• Current

• Quality seam

• Pores/Holes• Undercuts

• Offset seam

Big Data Analytics for Process and Quality Monitoring in Gas Metal Arc Welding of Automotive Parts | TF/VRT | 08.05.2019


Procedure: From process data to weld seam quality

Segmentation Classification Extraction Correlation Result

Automaticsegmentation ofsimilar process

cycles

11





Classification ofthe segments

withunsupervised

learning

12



Route: From process data to weld seam quality


Extraction ofstatistical and

process specificfeatures

• Mean• Variance• Skewness• Kurtosis

• Base time• Pulse time• Base current• Pulse current

13





Analysis ofcorrelation

between processand quality

features

14




Time [min] 60 45

Color yellow blue

Add-ons climatronic bending lights

Basics: The correlation coefficient measures the relation between features and target

Target Who was speeding?15



Consumption [ltr/100km] High

Time [min] High Correlation-coefficientColor Low

Add-ons Low


Target Who was speeding?




Time [min] 60 45

Color Pink Blue

Add-ons parkingassistance

bending lights


Target Which car was driven by a „hipster“?



Consumption [ltr/100km]

Time [min] Correlation-coefficientColor

Add-ons


Target Which car was driven by a „hipster“?18



Consumption [ltr/100km] Low

Time [min] Low Correlation-coefficientColor High

Add-ons High


Target Which car was driven by a „hipster“?19





Selection ofhigh coefficient

20





HighPulse_meanUCoef. ~0.28

21





HighPulse_meanUCoef. ~0.28

22





HighPulse_stdPCoef. ~0.23

23





stdUCoef. ~0.06

24

Summary

25

First steps are done- next ones have to follow

• Feasibility studies

• Features in time domains

• Criteria: IO, IOoN, IOmN


Summary



• Feasability studies



• Statistical significance

• Features in frequency domains

• Determination of error types

Outlook



• Feasability studies



• Statistical significance

• Features in frequency domains

• Determination of error causes

Bigger data basis, more classified welding errors

Headline in CorpoA (Überschriften) 30 pt. Thank you very much for your attention!

Big Data Analytics for Process and Quality Monitoring in ... · Big Data Analytics for Process and...

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