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The use of nCode software for test and CAE in the product development processes of AL-KO

Since 1931, AL-KO have been manufacturing premium, high quality components used in the automotive, commercial and leisure industry. The extensive range of AL-KO’s chassis components premium accessories and spare parts are engineered to last. 


  • less prototypes
  • extended durability
  • easy identification of critical locations
  • reduced costs for tooling
  • reduced development time
  • more precise tests
  • easy generation of test rig signals


Capture and secure the fatigue life of all components and products developed by AL-KO through an accurate virtual and physical testing process. AL-KO needs to be able to make precise predictions regarding product life, performance, durability, and warranty while reducing the overall time and effort going into the development process.


AL-KO established a development and testing process that includes the use of nCode GlyphWorks for data analysis and nCode DesignLife for fatigue simulation, enabling a cost efficient, fast, and reliable development and production of new components.
Injection molding analysis performed by Moldex3D on a seat adjustment.

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Test data processing and durability analysis

nCode GlyphWorks is a data processing system that contains a powerful set of pre-defined tools for performing durability analysis and other insightful tasks such as digital signal processing. Designed to handle huge amounts of data, GlyphWorks provides a graphical, process-oriented environment that contains leading analysis capabilities for saving both time and money in environmental qualification and product validation.

CAE-based fatigue analysis

nCode DesignLife is an up-front design tool that identifies critical locations and calculates realistic fatigue lives from leading finite element (FE) results for both metals and composites. Design engineers can go beyond performing simplified stress analysis and avoid under- or over-designing products by simulating actual loading conditions to avoid costly design changes.

Ready to achieve success through failure prediction?