Enabling Automated Quality Control in Composites
As industries advance new bio-based, faster reacting and increased recycled content materials and faster processes, how can manufacturers quickly establish and maintain quality control?
On-Demand Webinar
Manufacturers often struggle with production anomalies that can be traced back to material deviations. These can cause fluctuations in material flow, cooling, and cure according to environmental influences and/or batch-to-batch variations. Today’s competitive environment demands cost-efficient, error-free production using automated production and stable processes. As industries advance new bio-based, faster reacting and increased recycled content materials and faster processes, how can manufacturers quickly establish and maintain quality control?
In-mold dielectric sensors, combined with advanced data analytics technology, provide manufacturers with invaluable insights into their production processes. These sensors enable real-time determination of the glass transition temperature (Tg), offering precise control over critical material properties. They continuously monitor material deviations, such as variations in resin mix ratios, material aging, and batch-to-batch inconsistencies, ensuring a comprehensive understanding of the material’s behavior throughout the manufacturing process.
By leveraging these insights, manufacturers can predict the impact of deviations or potential material defects during production, allowing for proactive adjustments. The technology also tracks the progression of the curing process, ensuring parts are demolded at the optimal point—when the desired degree of cure, Tg, or crystallinity is achieved. Additionally, this system enables the documentation of resin mix ratios, particularly when using snap-cure resins, which is essential for the qualification and certification of RTM (Resin Transfer Molding) parts. Together, these capabilities support higher quality and more efficient production.
Successful case histories with real parts illustrate how sensXPERT sensors, machine learning, and material models monitor, predict, and optimize production to compensate for deviations. This Digital Mold technology has enabled manufacturers to reduce scrap by up to 50% and generated energy savings of up to 23%.
What you will learn in this webinar:
- Dealing with the challenge of material deviations and production anomalies
- How dielectric sensors work with different composite resins, fibers and processes
- What is required for installation
- Case histories of in-mold dielectric sensors and data analytics used to monitor resin mixing ratios and predict potential material deviations
- How this Digital Mold technology has enabled manufacturers to optimize production, and improve quality and reliability
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