Induction hardening: Digitalized production planning and Quality control (DigPIn2)

Purpose and goal

The project aims to increase market shares for induction hardening by introducing a more digitized production planning and quality control, enabling rapid implementation of robust processes for new products and new materials. Today, industrial production planning of this process is done exclusively through experiential trial-and-error and much of the quality control via destructive testing.

Expected effects and result

The rapidly growing green technology areas of electrified vehicles and renewable electricity generation will continue to rely on surface hardened low-alloy steel components for mechanical power transmission. From a sustainability perspective, no other hardening process can match induction hardening, where only the volume of material to be hardened is heated inductively and then quickly cooled with a shower quench to harden.

Planned approach and implementation

Our solution for increased efficiency and flexibility is based on digitizing material and process data and implementing it into existing finite element calculation (FEA) tools. Furthermore, via machine learning, to connect the data sets created from material, process and product during manufacturing with induction hardening.The vision, to reach a manufacturing with induction hardening with End-to-End AI, will require that a whole range of digital tools can be realized and implemented within the industry.