AI-based Product Quality Controlling in an Anodizing Process


  • Joshua Prim Technische Hochschule Mittelhessen
  • Henrik Pöschl Seidel GmbH & Co. KG
  • Michael Guckert Cognitive Information Systems, KITE - Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen



Artificial Intelligence, Machine Learning, Quality Assurance, Automation Process Monitoring


Product quality is a crucial factor of customer satisfaction and thus directly influences the competitiveness of a company.
In manufacturing companies the quality of production processes obviously has significant impact on product quality.
Therefore, establishing automated quality control offers considerable leverage for improving processes without necessarily increasing work efforts and costs.
In this paper an artificial intelligence based pattern recognition method for increasing the output of an anodising process for aluminium parts is discussed.
In the use case presented here, customers have high aesthetic requirements regarding the products which are used in an expensive market segment with only limited fault tolerance.
Preparation of the product parts before going through the anodising process is a manual, tedious, and error-prone task that nevertheless requires highest precision. Small deviations can lead to quality problems causing rejections and enforcing repetitions in production.
We discuss the application of visual image processing with an artificial intelligence algorithm integrated into the information system of the company to monitor the process and prevent human errors.
Results show that our approach reaches high accuracy and can potentially improve delivery reliability with respect to time and quantity by reducing cost-intensive manufacturing errors.