Motivation

Due to the increasing individuality of production tasks and the ever more diverse technical and functional possibilities of machine tools, the cognitive load in the use of machine tools is increasingly increased. Since the first machine tool operating systems were introduced to the market in the 1960s, the number of physical operating elements has increased by about 400 percent to date, which has also increased complexity. Although current suppliers of machine operating systems talk about being “user-friendly” and “innovative,” actual user surveys show complaints regarding the confusion of the menu structure, incomprehensibility of error messages, lack of necessary information and limitations in customizability. While machine operating systems are undergoing almost no innovative development, the increasing development of AI is creating new opportunities for human-machine interaction. AI is enabling technologies and functions such as voice and gesture recognition, automations, predictions, recommendations, personalizations, proactive error detection, and generative chatbots. Especially in the field of consumer electronics, such AI-based technologies are already widely used for human-machine interaction. However, such an application within production for the human-machine interface (HMI) of machine tools is missing so far, which shows the necessary need for research in this area.

Objectives

The aim of the project is to show which application possibilities, potentials and benefits the use of AI for HMIs of machine tools in production brings forth. Within the project, a new AI-based HMI for operating machine tools will be developed and tested. The newly developed HMI is intended to improve the operation of machine tools through the use of AI, particularly in terms of effectiveness, efficiency, customizability and intelligent functions. The developed HMI is also intended to reduce the cognitive load for users and at the same time increase the work safety, usability and user experience of machine tools in production, which in turn contributes to improving the efficiency and effectiveness of the entire production.

Acknowledgement

The research project is funded by the Federal Ministry of Education and Research (BMBF). We thank for the opportunity to work on this project.

Funding source

Project sponsors