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Automated Inline Quality Control System for Real-Time Analysis of Complex Automotive Parts under use of Perception Based Artificial Intelligence

Acquisition of real-time information of technical systems is becoming more and more important. One of the biggest beneficiaries of transparent processes can be found amongst companies applying industrial production methodology. According to this demand, a 3-scanning technology in combination with deep learning algorithms helps to overcome current problems at real-time quality control. The projects aim is to support the current quality control process of a German Tier 1 automotive supplier to become more efficient. The goal is to deliver real-time quality information of the produced parts to employees, management and to further business intelligence systems.

Instead of the conventional procedure by manual inspection, the detection process automatically reviews the parts at the assembly line without the need of further support by workers. As a key element of this strategy, an image-based, predictive quality control system can be established at the entire assembly lines. The collected data supports the internal BI (Business Intelligence) with necessary information for further optimization steps. An additional benefit will be the reduction of labor and savings of raw material. Additionally, workers will be relieved by less repetitive tasks.

One of the main targets is to use this sensing technology to support the digital transformation of the company. Goal is to observe current quality aspects in real-time and verify quality aspects of the automotive customers automatically. Additionally, a deep-learning technology provides a prediction about when recalibration/maintenance of the production equipment is necessary. The data will also be used for the business intelligence in order to track quality and ensure the traceability from raw material to the final products. 




Project type



Project budget

(IoT4industry funding): 60 000 €


Project end date

(estimated):  March 2020


Partners involved

Logo / Website Name Type Country Region
STURM GmbH SME Germany North Rhine Westphalia
Hochschule Aalen Research Organization Germany Baden Württemberg
Fachhochschule Vorarlberg Research Organization Austria Vorarlberg
Mühlhoff Umformtechnik GmbH Large Enterprise Germany North Rhine Westphalia


Vertical sector addressed

  • Automotive

Industrial application addressed

  • Monitoring & control

Technologies involved

  • IoT & wearables
  • Big Data & AI


Contact Project Coordinator

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