EMO Hannover 2023, the world’s leading trade fair for production technology, will showcase the transformative power of artificial intelligence (AI) in industrial manufacturing. From 18 to 23 September, the event will present a wide range of innovative ideas under the banner of “Innovate Manufacturing,” with AI taking center stage.
One of the key areas where AI is revolutionizing industrial production is machine learning, which significantly enhances efficiency levels. By leveraging AI, production machines can self-optimize, learn from their mistakes, and acquire knowledge from other machines. This intelligent functionality leads to improved productivity, reduced costs, enhanced quality, and minimized downtimes.
Predictive maintenance, enabled by AI models, will be one standout application which will be showcased at EMO Hannover. Machine tool manufacturer Weisser Söhne GmbH & Co. KG employs AI-powered predictive maintenance to anticipate machine servicing needs, preventing breakdowns and enhancing reliability. Start-up Prenode GmbH assists machine builders in incorporating customized AI features into their plants.
Machine learning methods, such as pattern recognition and correlation analysis, enable modern production machines to self-optimize. These methods allow machines to identify patterns and correlations in production data, leading to automatic improvements. Machines can also learn from their mistakes and leverage the know-how of other machines.
To develop accurate AI models, federated learning techniques are often utilized. This approach involves decentralized data storage, with no direct sharing of sensitive information. Individual machines contribute their data, enabling the training of a common AI model without compromising data security. Deep learning neural networks are used to estimate the present status of the production plant, facilitating real-time decision-making.
Another prominent example of AI application at EMO Hannover will be the Sorting Guide developed by laser specialist Trumpf. This decentralized machine learning system assists in sorting produced parts and optimizing machine utilization. By linking multiple machines, the Sorting Guide utilizes shared knowledge without compromising raw data privacy, leading to improved efficiency and productivity.
AI also enhances machining processes by accurately predicting tool wear. Researchers at the Technical University of Kaiserslautern have developed a method that uses AI to train systems in predicting tool wear based on real process and measurement data. By analyzing various parameters, such as machining forces and vibrations, AI models can optimize cutting processes and avoid costly tool breakages and machine damages.
Recognizing that smaller companies may have concerns about AI and data analysis, the IIP-Ecosphere project, in collaboration with the Fraunhofer Institute for Software and Systems Engineering, aims to provide low-threshold access to vendor-independent AI solutions. The project establishes an ecosystem where industrial companies, research institutions, and AI solution providers can promote the use of AI in manufacturing, fostering knowledge sharing and collaboration.
EMO Hannover 2023 will demonstrate that AI is reshaping industrial production by increasing efficiency, optimizing processes, and improving decision-making. While Germany is still striving to catch up with international providers in AI development, the country’s expertise in optimizing domain-specific processes gives it a competitive edge. The event emphasized the importance of leveraging AI to stay ahead in the global industrial production landscape.
