Webinar: Enabling Automated Quality Control in Composites for Less Scrap, Higher Yield by Gardner Business Media, Inc. Webinars

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About This 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 paired with data analytics technology enable manufacturers to:

Determine glass transition temperature in real time

Monitor material deviations such as resin mix ratio, aging, and batch-to-batch variations throughout the process

Predict the influence of deviations or material defects during the process

See the progression of curing and demold the part when the desired degree of cure, Tg or crystallinity is achieved

Document resin mix ratios using snap-cure resins for qualification and certification of RTM parts

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%.

Presented by:

Agenda

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

Presenter

Nicholas Ecke

Application Engineer

Dr. Nicholas Ecke has been a part of the Netzsch Process Intelligence GmbH team alongside Dr. Alexander Chaloupka since October 2022. He studied process and medical engineering at the Technical University of Munich and was a chief engineer for the chair of composite materials at the Technical University of Kaiserslautern. In 2021, he completed his doctorate in engineering and his dissertation was awarded the Oechsler Prize. The thesis dealt with methods and approaches for developing and designing tribological components made of plastics. As a sensXPERT, Dr. Nicholas Ecke ensures the smooth integration of an intelligent sensor technology, namely the sensXPERT Digital Mold, in customers' production processes worldwide.

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