Consultings

Scope:

There is a need to address several challenges in optimization of manufacturing processes, such as the seamless integration of heterogeneous data to gain insights or establish correlations and the dynamic process optimization, ensuring real-time adaptability and responsiveness to variations in the production process. Also, companies need to uncover dependencies, providing insights for process improvements, as well as enhances decision-making and quality assurance and maintenance.

Cross-correlation algorithms and intelligent data-driven models analyse heterogenous data such as process parameters, material characterisation, sensor data and other data sources to identify intricate relationships between the available data sources that affect significantly the manufacturing process. Additionally, these algorithms can assist in developing feedforward optimization strategies.

Approach:

The data-driven models developed in the PIONEER project represent a transformative solution from conventional, manual approaches to manufacturing optimization. Unlike traditional empirical methods that rely on static data handling, these data-driven models will utilize advanced AI algorithms and cross-correlation techniques to seamlessly integrate data from several data sources, including sensors, processes and material metrics, enabling the identification of complex relationships, which directly impact process performance and product quality.

These models provide real-time analysis, allowing for dynamic process optimization, predictive maintenance, and improved quality assurance. Hence, the solution significantly improves decision-making for production managers, engineers and R&D teams, contributing not only to streamlining operations and reducing costs, but also to driving the creation of more sustainable and higher-quality products, adhering to global environmental and social responsibility goals.

The users seek real-time insights, improved quality control, reduced production costs, as well as predictive maintenance and these models can support faster, data-driven decision making to optimize manufacturing efficiency and product reliability. 

Outcome:

Models need to be adapted to customer requirements, by means of consultancy services based on CORE's expertise. The models can be offered as services and integrated into the PIONEER OIP Platform.