Integrated Engineering Data Transformation: An AutomationML-Based Approach for Efficient Data Exchange

In the contemporary manufacturing sector, efficiency and precision are paramount but often hindered by manual and error-prone transfers of data across disparate engineering disciplines. This paper addresses the critical challenges posed by the diversity of data formats and the high reliance on manual processes in engineering data logistics. We propose a novel strategy leveraging Automation Markup Language (AutomationML) to foster a more efficient and error reduced data exchange framework. AutomationML is utilized for its robust representation of plant engineering information, promoting seamless interaction among various engineering tools and systems. By transitioning from existing pipeline-based data logistics methodology to a centralized data storage approach, our method markedly diminishes the likelihood of information loss and reduces integration complexity. This research not only maps out the prevailing issues with current data logistics practices but also introduces a scalable, adaptable tool designed to optimize data integration and transform the engineering workflow. By addressing these potential advancements, the approach sets a path towards a more efficient and effective engineering data logistics paradigm.