AI-supported quality assurance

For sustainable quality improvement!

There are different quality requirements within industrial production chains. To meet these requirements, companies usually rely on manual quality checks. However, these are generally error-prone, complex and expensive. With the help of AI solutions, quality control can be carried out automatically and reliably in real time and recorded in detail. This allows errors in production and logistics to be identified and avoided quickly and effectively.

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The current status

One of the biggest topics in the field of AI is image recognition. This, in turn, is one of the most important prerequisites for AI-supported quality assurance. Developments in this area have made great strides in recent years. Due to the high volume of investment, especially by large companies, both market growth and the range of applications have increased continuously.

The use of automated quality assurance systems is also becoming increasingly interesting and economical for small and medium-sized companies.

Technology and use

Technology description

Camera systems integrated into the production chain provide real-time images of the products being manufactured/processed. Image processing software for automatic image evaluation can use AI algorithms to detect product-specific defect patterns (e.g. cracks, incorrect spraying, protrusions, geometric deviations). For this purpose, complex artificial neural networks are trained using many sample images tailored to the respective quality control and their individual specifications in order to meet the respective production standards.

Thanks to AI algorithms, even the smallest errors that are often overlooked in manual quality control can be found with consistently reliable quality. With the help of the automatically generated quality reports, correlations can be established between, for example, the setting of machines in the production process, environmental parameters such as pressure, temperature or similar and the resulting quality, thus sustainably improving the production process.

Possible application scenarios

  • Component inspection
  • Surface inspection
  • Weld seam inspection
  • Packaging control
  • Food processing
  • Harvest control

Step-by-step introduction

Step 1: Analysis of the current situation

First, the initial situation is specified in more detail. This includes answering the following questions in order to assess whether AI-driven automated quality assurance can be used in the production process at hand:

– Which problem class is relevant in your quality assurance? (cracks, assembly errors, soiling, breakage, text inspection, etc.)

– What material are your manufactured components made of?

– Is it possible to integrate cameras into the current process?

– At what interval must objects on a production line be checked?

Step 2: Data collection

The basic prerequisite for the implementation of AI-driven automated quality assurance is the continuous collection of large amounts of data. It is therefore essential that the production plant is equipped with appropriate cameras.

If the systems are not capable of generating corresponding image data, an upgrade may have to be carried out upstream.

Step 3: Data exchange

The resulting data volumes can only be evaluated and processed if data can be exchanged between the cameras in the production plant, the cloud and the image processing software. Interfaces and analysis options must therefore be defined.

Furthermore, the appropriate image processing software must be selected or implemented.

Step 4: Utilization

By collecting and analyzing large volumes of data, automated quality assurance can be carried out. Defective products can thus be automatically detected and sorted out.

Based on the collected data, holistic reports on product quality can be created and frequent sources of error in the production process can be iteratively identified, analyzed and eliminated. A continuous improvement process can thus be initiated.

Opportunities for SMEs

Central data networking and monitoring of the entire production quality

Traceability and verifiability of product quality

Minimization of waste

Sustainable improvement process of the product

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