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1. What concrete benefits can industrial companies expect from the use of artificial intelligence, for example in terms of visualization and simulation?

Simulation as a virtual test is established in many companies to save prototypes and resources - but their preparation and assessment are in the hands of a few specialists. With the help of machine learning methods, behavioral patterns can be identified, for example, in extensive variant studies in product development. By applying so-called clustering, parameter sets can be sorted into groups that show favorable behavior, for example in the optimization of safety-relevant components in vehicle crashes or in complex manufacturing processes.

AI aids with Expert Emulation to draw the right conclusions more quickly out of large test series and gives a wider group of people in development the opportunity to evaluate them. This is where the key to industrial AI lies: combining engineering expertise with data expertise and extracting additional value from the data.

Reference example: BMW – Emulating Engineering Expertise with AI

Simulation can also generate synthetic data to train learning models and even generate data from operating conditions in which no machine operator would want to operate his machine. It can also map effects that cannot be captured by sensors. In this way, applications for predictive maintenance and active control of product quality and scrap reduction can be realized through the symbiosis of AI and simulation.Several use cases on the topics Artificial Intelligence in Simulation, the combination of AI with digital twins and democratization of AI in manufacturing were presented recently at the conference AI for Engineers .

2. How can Artificial Intelligence help to increase efficiency and flexibility in the digital factory and what concrete solutions do you offer in this area?

AI can massively increase efficiency on the factory floor . But friction of an organizational, technological, or financial nature hinders the implementation of many AI projects: Departments operating in an isolated manner, a fast-growing volume of data, and a severe lack of data science professionals. Companies need to address these friction points to successfully execute fast and scalable data analytics projects.

The end-to-end data and AI solution, Altair® RapidMiner® Platform , enables faster implementation of machine learning and AI applications by connecting the three most important resources: Data, People, and Process.

3. How can the combination of artificial intelligence and the digital twin help to increase the quality of production processes in industry?

The example of Patrone and Mongiello , who were seeking technology to monitor and control their sheet metal forming process, and chose Altair's Digital Twin solution, is a perfect showcase of this.

An AI-enhanced Digital Twin made it possible to understand the effects of various varying factors on the forming process and thus improve the quality of the produced parts. This reduced scrap rates by 15% and increased profitability and sustainability.

For more information, please visit .

Applications and their underlying approaches are discussed in more detail in the AI for the Factory Floor topic channel .

The 2023 Frictionless AI Report provides a comprehensive overview of how companies are implementing data and AI strategies, the impact of friction, and how you can address the root causes.

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