Deploying Machine Learning in High Pressure Resin Transfer Molding and Part Post Processing: A Case Study

Abstract

AbstractHigh pressure resin transfer molding (HP-RTM) is well suited to medium volume automated production of composites. The process complexities of HP-RTM however often make its application insular. Data is not carried forward along the production chain and process reliability is assessed as a unified indicator with minimal granular consideration of individual contributing factors. Cause and effect relationships spanning the process chain remain undetected. Predator (10/2020–09/2023) is an ongoing Eurostars project aiming to bridge this divide by developing an intelligent data processing system across the industrial process chain of composite production. The consortium has already developed an approach to acquire and transfer meaningful process related data from molding to post-processing of parts. The data collection merges RTM tooling, equipment sensors, structure-borne sound data and tool wear measurements during the milling process. Unique part identifiers allow traceability of production parameters for online quality assurance and data-based optimization across the process chain. The developed approach enables tool wear monitoring as well as tailored predictive maintenance and enhanced remote customer support in addition to a data-driven understanding of the production process.