Digital Engineering Methods in Practical Use during Mechatronic Design Processes

This project gives step‑by‑step guidance for evaluating and adding digital engineering methods to modern mechatronic design. Today’s product development needs fast iteration and the ability to handle very complex systems. Tools such as artificial intelligence, deep learning, digitalization, and data mining can make these workflows much more efficient. Despite their promise, these tools are not yet widely used in everyday mechanical and mechatronic engineering. To address this, the work surveys the latest research and builds a single framework that links specific digital‑engineering tools to each stage of the product‑development cycle. By sorting the application areas and reviewing the literature, the project shows practical ways to move from traditional design methods

to data‑driven engineering processes. The framework is meant to help engineering students and professionals adopt digital twins, predictive models, and automated data analysis when designing mechatronic systems. It also points out the biggest gaps in current research and highlights where future work can have the most impact. In short, it provides a clear roadmap for putting digital product‑development strategies into practice in industrial settings.

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