Hannover Messe, one of the world’s largest trade shows for industrial technologies, is planning to make this year’s event a “showcase for artificial intelligence”.
From 17 to 21 April 2023, Hannover Messe, held in Germany, will be shining a spotlight on the topic of “AI in manufacturing”.
Along with the subject of process optimization, exhibitors will be focusing on the role of AI in simulation, testing and product development. Generative AI is also making major inroads into industry.
Which is why the upcoming tradeshow will be offering special guided tours, a dedicated discussion format and numerous company exhibits featuring AI tools and use cases – from robotics to production cells.
The only way for industrial enterprises to remain competitive in the coming years is by linking AI to their process automation, warns Professor Dr Sepp Hochreiter of the JKU Linz university in Austria.
His message to industry: “Don’t screw this one up”. But AI in industry or manufacturing does in fact differ from many other sectors. And this goes beyond the issue of mere data acquisition and processing.
Today, prototypes can often be developed quickly, but the challenge in industrial AI projects – over and above the acquisition and the processing of data – usually lies in integrating the application into a plant, cell, conveyor system or production line.
In other words, AI plug and play is rare. Hannover Messe 2023 presents the ideal networking hub. This is where AI developers, software engineers get together with users to jointly develop industrial-grade AI products or processes.
Whereas in the past, the focus was on use cases in which errors or anomalies were detected or prognostications were made, industry in 2023 is focusing on the optimization of processes and the use of AI methods for simulation, testing and product development.
On the second day of the show, the Monolith AI firm will present its solution for simulation in mechanical engineering as part of the Industrial AI event on the Industrial Transformation Stage in Hall 3.
Monolith AI’s approach goes even further than the booming simulation industry. Every simulation performed develops a model, because the creators rely on real-time data. This means mechanical engineering could save on numerous testing procedures.
In addition, AI makes suggestions to developers about their products, based on the real-time data. This England-based firm has some very ambitious goals: By 2026, they aim to reduce the product development time of 100,000 engineers by 50 percent.
At the same event, machine manufacturer Hawe Hydraulik will report on how it is using reinforcement learning and then implementing the technology in its processes.
Generative AI, for example in the form of the DALL-E tool, will also change the face of industrial product development, with the designer receiving support from an intelligent agent.
Festo, the exhibiting company, has been working in the area of reinforcement learning for manufacturing processes for several years. The next step involves the use of generative algorithms for product development.
OpenAI recently published 3D models for DALL-E. The challenge in the industry, apart from the 3D challenge, is that the products must also be moveable. In addition to Festo, which is also bringing its new Cobot, Autodesk is also addressing this issue.
The challenge of integrating machine learning into processes is also being addressed by process control suppliers – Siemens is moreover focusing on providing ML Ops, in which engineers provide reliable machine learning models for efficient production and continually maintain them.
Siemens will also be providing an insight into an AI project at a customer’s site at the Industrial AI event on the second day of the fair.
In addition, visitors will find AI tools and use cases to draw inspiration on the tradeshow floor. Omron will present a Cell-Line Control System, while Beckhoff will showcase vision solutions and Dürr will feature its DXQanalyze product family.
The promise: This enables the comprehensive logging of all available process data to detect potential product quality defects or emerging equipment wear in real time.
The system uses data that is condensed at a higher level to draw conclusions about the functionality of individual steps along the value chain, based on documented product quality.
