Fraunhofer Institute for Microelectronic Circuits and Systems
Fraunhofer Institute for Microelectronic Circuits and Systems
Cnc machine working, drill, manufacture background --ar 16:9 --stylize 1000 --v 6 Job ID: 517f1679-922f-4b9c-8481-d4b4640aa9f1

Wise-Cut: Wisdom in Machining

Real-world demonstration of a machining process with integrated vibration sensors and AI evaluation to predict tool life and workpiece quality

Wise-Cut: Wisdom in Machining

In the Wise-Cut project, a demonstrator was developed that combines innovative technologies for condition monitoring in the machining process. By integrating vibration sensors and AI algorithms, it becomes possible to precisely predict the remaining tool life and the quality of the workpieces, especially with regard to chatter marks.

Wise-Cut - Wisdom in Machining:

  • Predicts tool life and workpiece quality
  • Maximizes tool utilization and minimizes downtime
  • Supports long, unattended machining with real-time quality control

About the project

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The monitoring of complex machining processes (e.g., single-piece production, tool manufacturing, aerospace engineering) is of great importance for quality control and efficient production. Currently, tools are replaced preventively and based on experience to ensure the quality of the final product. However, the utilization of the tools is not optimal. Additionally, tool breakages or quality issues (e.g., chatter) lead to scrap and high costs, especially in automated manufacturing.

© Fraunhofer IMS

The solution aims to enable innovative process monitoring in machining, increased efficiency and process reliability, as well as a better understanding of the process. Users can predict or immediately detect the remaining tool life or the formation of chatter marks, saving additional costs such as scrap, suboptimally utilized tools, reduced productivity due to conservative process settings, high rework or quality costs, or operator monitoring.

Overall, the technologies integrated into the demonstrator promote a sustainable production.

© Fraunhofer IMS

In the demonstrator, various AI and ML algorithms for condition monitoring were implemented to optimize the efficiency and reliability of the machining process. Initially, condition detection and prediction of the remaining tool life of a portal milling machine on-site are performed using ML models. Additionally, other system states are detected, such as active machining, the condition of certain components, and surface quality, due to detection of the occurrence of chatter.

A key aspect was the faster adaptation of training to new machine configurations, in this case, the portal milling machine, through the use of transfer learning. Additionally, Auto-ML methods were tested and applied to automate and accelerate model development.

 

The optimization of AI models was particularly emphasized for small, mobile, and energy-efficient systems.

Another goal was the development of a physics-based simulation model as a digital twin on the Fraunhofer Edge Cloud for the detection of chatter, which serves as real-time detection of surface quality during the machining process.

 

Project partner