Success Story Process Optimization

Optimization in Automotive Manufacturing

The act of manufacturing cars is characterized by a high level of complexity paired with thrive to maximize output while working under strong cost pressure. The entire production is optimized throughout and to a high degree: From streamlined production buffers with just-in-time delivery to the best possible production sequence.

Advanced AI systems offer further potential to reduce cost or improve output. Using continuous measurement data from individual production stations helps improve various key parameters.


lower scrap rate


  • The scrap rate due to tearing and wrinkling during the forming of sheet metal into components is to be reduced.
  • For this purpose, various parameters such as lubrication and blank holder pressure are to be dynamically adjusted.


  • The processes during forming are complex and existing prediction methods are too slow for optimization per individual sheet.
  • A model of the crucial parameters should provide near real-time settings for the stamping process so that it reliably leads to a usable result.


  • In a preparatory phase, a series of simulations were carried out using existing methods, covering a wide range of setting sizes and variants.
  • Prexello‘s models were calibrated and validated on the basis of this data.
  • The resulting model reliably reflects all relevant interrelationships, so that the most promising settings can be individually determined for each sheet of metal.


  • Prexello‘s modeling techniques have identified causations between the critical setting parameters and the properties of the formed sheet.
  • For each individual sheet, it is possible to determine which setting offers the best possible chance of success.
  • This reduces the number of parts that are rejected during quality control, largely avoiding tears and wrinkles.
  • This approach has reduced the scrap rate by 35%.

Your Contact

Carlos Ayala Jiménez

Carlos Ayala Jiménez

Phone:  +49 1516 2 60 74 21