Primetals

Success Case – Primetals

2D and 3D dashboards, bar charts, line charts, donut and circle charts with color codes.

SECTOR

AECO

DEVICE

PC App

TECHNOLOGY

Artificial Intelligence

Data visualization

CONTEXT

The objective of the project was to enable Primetals to understand the predictions and conclusions generated by their existing data simulator. By incorporating explainable AI, the aim was to provide a clearer and more detailed visualization of the results, offering transparency so that the results would inspire confidence and optimize plant efficiency.

The Primetals team faced contradictions regarding the predictions generated by their old simulation system, compared to their own deductions and expectations based on the current knowledge of the plant's status. The predictions did not match reality, and the team did not understand why the system generated those results. This problem hindered future decision-making, as, without a clear interpretation of the data, it was difficult to plan improvements.

SOLUTION

To address this challenge, the team from IMMERSIA configured TOKII to develop an interactive data simulator that provided a clear and detailed visualization of the data. Utilizing explainable AI through visual interfaces, the application allows users to interact with the data, follow each stage of the process, rewind time to identify bottlenecks, and generate reports on critical moments or alerts that have occurred in the manufacturing process. This integration helped clarify the process behind the predictions and improve the understanding of the results.

RESULT

The digital twin powered by explainable AI technology has significantly improved the efficiency of the Primetals plant. The advanced visualization system of TOKII has enabled better interaction and understanding of the predictions from the simulation engine. Thanks to explainable AI, users can now understand the process behind the predictions and the reasoning for the results obtained. This ability to break down and explain the reasoning behind decisions has made it easier to identify bottlenecks, closely track the evolution of processes, and generate accurate reports on critical moments with transparency and reliability.