Digitizing Automotive Composite Manufacturing Using Real-Time Material Characterization скачать в хорошем качестве

Digitizing Automotive Composite Manufacturing Using Real-Time Material Characterization

Reducing scrap and maintaining high-quality standards are critical goals for plastics manufacturers, particularly in the automotive industry. With the help of advanced technology, including automation, sensors, and AI, companies are now able to better monitor supply chains and adjust swiftly to disruptions, while simultaneously enhancing cost-efficiency and sustainability. In composite manufacturing, factors like material inconsistencies, environmental fluctuations, and complex material behaviors pose challenges, leading to unpredictable part quality. This can result in longer cycle times, heavy reliance on post-production quality checks, and high scrap rates. In this webinar, discover how real-time dielectric sensors, machine learning, and material models are transforming production. Our experts will show how these tools deliver live monitoring, prediction, and optimization for every individual part. By capturing real-time data like flow front, viscosity, glass transition, and cure degree, machine learning models are trained to predict and adjust processes on the go—ensuring quality and efficient cycle times. What to Expect: How dielectric sensors and material models enhance process reliability The role of sensXPERT technology in reducing waste, improving sustainability, and saving energy while creating traceable data Case Studies: a) Electronics encapsulation for high-power EV components with improved cycle efficiency b)Scrap reduction and process automation in T-RTM for composite battery enclosures