EPP Europe P2.2023

» TEST & QUALITY ASSURANCE
Source: Göpel electronic
AI evaluation of a QFN pin solder joint in a 3D X-ray image with AI advisor
of prismatic battery cells for foreign objects or the
AXI inspection of sheath thermocouples directly in
the production line. As a human being, you often see
certain defect features quickly – but the machine
needs an expert who has mastered the parameterisation
of an inspection function in order to be able
to evaluate a feature correctly. This is where AI can
help. The AI system is trained using example images.
This results in an AI model that carries out the evaluation
without any setting parameters. This is exemplified
in the evaluation of welded joints on sheath
thermocouples.
Zusammenfassung
Noch sind wir nicht bei autonomen Inspektionssystemen
angekommen, doch KI-basierte Technologien in der Inspektion
ermöglichen bereits heute die vollautomatische
Erstellung sowie Optimierung von Prüfprogrammen.
Résumé
Bien que nous n’ayons pas encore de système
d’inspection automatique, les technologies basées sur l’IA
permettent déjà aujourd’hui de créer et d’optimiser des
programmes de contrôle de manière entièrement
automatique.
Резюме
Хотя мы еще не пришли к автономным системам
контроля, тем не менее технологии на основе
искусственного интеллекта в области контроля уже
сегодня обеспечивают полностью
автоматизированное изготовление и оптимизацию
программ проверки.
Further AI applications are in the works. In x-ray
inspection, AI solutions for void detection, BGA
head-in-pillow detection and short-circuit control
are already being developed. AI approaches are
emerging in predictive maintenance and for the
analysis of results data from inspection systems
across the whole SMT line. “There’s a long way to go
before we achieve fully-automated inspection but
we shouldn‘t forget how far we’ve come,” says Tuerk.
AI at the verification station
AI is not only saving human resources in the creation
of inspection programmes. There is also the potential
to save time and money at verification and repair
stations. Goepel electronic relies upon the AI advisor
software module for this purpose. Based on pretrained
models, the AI advisor makes its own decision
for each anomaly found by the inspection system. The
AI‘s decision is based on the verification decisions
made by humans in comparable cases in the past.
Now there are different options for deploying AI decision-making.
Anomalies that are classified with high
confidence as pseudo errors can be evaluated directly
by the AI – without human intervention. This saves
time. The AI advisor can also warn us about whether
a human error could lead to a so-called human slip. If
an operator incorrectly classifies a real error as a
pseudo error, the user is asked to reconsider his decision.
Especially in the case of X-ray images – where
error interpretation is often more demanding – the AI
advisor can be a welcome aid. Its functionality is constantly
being expanded with the aim of a fully autonomous
classification of all abnormalities found.
Trust is good – control is better!
“Often there is still a sense of unease about the
use of artificial intelligence in the field of inspection
systems” notes Tuerk. “Why did the AI decide one
way and not another? Will the AI find previously discovered
defects even after it has been trained with
new image samples? These are all legitimate questions.
Luckily we already have some answers. The AI
must be trustworthy – decisions must be comprehensible.
With this in mind, Goepel electronic is
working on ‘Comprehensible AI’ to increase trust and
acceptance. We shouldn’t forget AI is only as good as
the human who trained it, and this depends entirely
on data selection, its completeness and consistently
correct labelling and categorisation,” says Tuerk.
There may still be some way to go before we
achieve fully-autonomous inspection but, thanks to
AI, the next paradigm shift is already well underway.
Productronica, Booth A2.239
www.goepel.com
54 EPP Europe » 11 | 2023