Developing a constraint model for using artificial intelligence on existing, limited hardware in manufacturing machines

In the rapidly evolving field of Artificial Intelligence (AI), its application in industrial settings, particularly for anomaly detection in time series data, poses unique challenges. Current methods often lack a comprehensive understanding of the trade-offs involved in achieving optimal model performance, data preparation effort, and prediction quality. To bridge this gap, this paper presents an adaptive approach to address these challenges, focusing on making conscious decisions about mentioned trade-offs. Inspired by the principles of the Iron Triangle from product engineering, our methodology introduces a novel "AI triangle" with dimensions of Speed, Effort, and Quality. We applied this methodology to a real-world case study involving anomaly detection in a constrained hardware environment in the context of a forming production process. The results highlight the effectiveness of our approach in achieving a practical balance between speed, effort, and quality constraints for implementing AI in an industrial setting. This paper provides valuable insights into the considerations and trade-offs necessary for the successful deployment of AI in manufacturing and other similar industrial applications.