The session begins with a practical case study on the use of AI in a project context, covering the initial planning phase, the selection of suitable AI methods, and their technical and organisational implementation. In addition, typical challenges will be highlighted, including data quality, employee acceptance and the effort required to integrate the technology into existing systems.
The following presentation demonstrates in a practical way how AI-supported software can prevent scrap even before coating takes place. Quality is not merely checked, but anticipated: potentially critical situations are identified at an early stage, production capacity is utilised specifically for good parts, and double costs resulting from scrap and rework are avoided. Practical examples demonstrate how process data can be used as an early warning system and how AI can be integrated into existing plants as an assistance system.
Two presentations will address the topic of AI and knowledge retention and transfer:
Demographic change is hitting the electroplating and surface treatment industry particularly hard. Experienced specialists are retiring, and with them there is a risk that valuable process knowledge will be lost. At the same time, it is becoming increasingly difficult to find qualified young talent. One presentation outlines approaches to documenting, making accessible and integrating experiential knowledge into daily work with the help of AI.
Another presentation demonstrates the role ChatGPT can play in imparting fundamental knowledge – particularly in the ‘Gen Tablet’. Since the onset of the COVID-19 pandemic, at the very latest, the virtual world has become a reality, and communication has taken a quantum leap. We must face up to this. ChatGPT offers an opportunity here to combine the old with the new. For example, with the concept of
‘experience spaces’ presented here, in which participants work in teams to acquire basic knowledge using predefined ‘prompts’.