AutonomIQ

Initial Situation

CAM programming is one of the biggest bottlenecks in work preparation in today's single-part and small-batch production. It is time-consuming, labor-intensive, and heavily dependent on the experience and knowledge of individual specialists. At the same time, the trend toward individualization is leading to a steady increase in the number of small orders – while the demands on efficiency and precision remain high. The growing shortage of skilled workers further exacerbates this challenge.

Objective

autonomIQ aims to automate the previously manual CAM planning process and replace it with intelligent algorithms. At the heart of the solution is a self-learning CAM kernel that uses AI and realistic process simulation to automatically generate and optimize tool paths and use them directly for quotation preparation. This will enable companies to process their orders faster, more efficiently, and with fewer errors—regardless of the qualifications of individual employees.

Approach

As part of the project, a solution with several expansion stages is being developed – from quotation automation to complete machine integration. autonomIQ combines process knowledge, machine-specific simulations, and AI models for real-time optimization. The solution is being tested and further developed in pilot projects with various industry partners.

The goal is to qualify both a scalable plug-and-play solution for brownfield machines and a deeply integrated variant for new machines. In addition to technological innovations, autonomIQ also pursues clear sustainability goals, for example by reducing waste, energy consumption, and skilled labor retention in repetitive tasks.

Utilization and outlook

This project is laying the foundation for a marketable product for autonomous train planning. Thanks to its modular design, the solution can be used by both manufacturing companies and machine manufacturers. The establishment of a deep tech start-up and the development of a strategic partner network (including EIT Manufacturing and Hessian.AI) are intended to ensure the successful transfer of research into industrial practice and contribute to the long-term digitalization and competitiveness of Germany as an industrial location.

Funding source

Funding source