CAPP_AI4.0 | Learning about AI and digitalization for a more efficient computer-aided process planning in Machining 4.0

The CAPP_AI4.0 project focuses on the use of AI and digitalization solutions for more efficient computer-aided process planning in manufacturing. While decisions in the planning phase have so far been based mainly on the experience of qualified experts, the aim of the project is to professionally prepare and support employees in production for the use of upcoming AI systems for process planning. The project aims to develop courses, workshops and learning materials that can be used to train how to effectively use AI systems in production.

Coordination: Gilbert Engert M.Sc. (TEC)

Duration: 01.10.2023 – 31.12.2024
Funded by: EIT Manufacturing


The planning and preparation of machining processes are crucial for efficient industrial production. Decisions made during process planning directly influence the precision and quality of the manufacturing result. To date, these decisions during the planning phase have been made primarily based on the experience and expertise of qualified professionals. However, process planning is based on purely personal experiences and decisions, which means that there is no clear process planning strategy. After an employee leaves the company, process knowledge is also lost. In addition, employees also lack explicit methods for solving particularly unfamiliar production tasks. As the complexity of manufacturing tasks increases, the complexity of process planning also increases, which creates new challenges, and the planning of machining processes more quickly reaches the limits of the employees' personal abilities.

However, with the advent of new AI systems, opportunities are opening up to improve and support process planning. AI systems can help shorten planning times and make processes more efficient and, above all, strategic. By using AI in process planning, it is possible to gain empirical experience and thus optimize technology parameters and settings in the CAD-CAM process chain. The new challenge here lies in working professionally to effectively handle such AI systems.


The aim of the project is to provide production employees with support for the use and handling of AI systems for process planning. For this purpose, the project develops courses, workshops and learning materials that can be used for professional training of employees. The learning and further training opportunities developed are intended to increase awareness of the potential of AI systems, promote the skills of employees and improve their application in machining process planning and preparation. Employees should be able to introduce new AI systems in the company and use them effectively, thereby sustainably increasing the performance, productivity and overall effectiveness of small and medium-sized companies (SMEs). To this end, the learning and further training opportunities should primarily consist of the creation, setup and use of AI in process planning and, through collaboration with European project partners, should be available internationally in several languages, both in person and online and, above all, free of charge. The potential of the learning and further training opportunities created lies in the fact that decision-makers and employees can bring many years of experience into AI-supported processes, but planning processes will no longer only depend on the experiences and decisions of individual employees. Even new employees without many years of experience can make decisions on the topics of machine set up, tools, machine programs, etc. in preparation for turning and milling processes. In addition, standardization in every company is made possible with the help of AI systems, for which the project provides in-depth knowledge.


The project is based on the previous projects Mach4.0 and LIVE4.0 and builds on the knowledge and project results achieved there. The projects have already paved the way for fully digital production planning. The CAPP_AI4.0 project expands this Learning content about the use of AI.

To this end, an extensive study of manufacturing companies in Europe, especially small and medium-sized enterprises (SMEs), will first be carried out in order to identify existing AI use cases in production. A survey will then be carried out in the partners' respective countries (Czech Republic, Germany, Italy and Spain) to determine the need for the use of AI in process planning. For this purpose, the existing knowledge of the respective project partners about AI and direct contact with SMEs in the respective countries should also be used.

Based on the identified needs and results from the survey and analysis of the companies, the learning content should be defined in order to design the most suitable topics for learning and further training opportunities. The EIT Skills.move online learning platform should be used to develop the learning content, with which every company can then gain free access to the online learning content created. Reference companies such as WatAJet should be selected by the project partners in order to test the learning materials created in advance. Thematic workshops will also be held on site on topics of interest to companies. To practically design and improve the learning experiences, already developed tools for CAD, CAM, simulation, cutting tools and working conditions/parameters should also be used. In addition, other interactive learning experiences, such as tours, demonstrations of machines and laboratory activities, will also be offered, with which the use of AI will be presented in a practical way using exemplary machining processes.

In order to assess the companies being trained, clear learning objectives and assessment methods should also be defined for each learning content. For this purpose, tests with technical questions should be developed for the learning content, which companies can use to test their knowledge. After successfully completing the learning content and passing the tests, the companies should receive a certificate with which they also have proof of the knowledge they have developed.


The research project is funded by the European Institute for Innovation and Technology (EIT). We thank for the opportunity to work on this project.

Funding source