In-mold sensor elicits real-time mold conditions, final part data

SensXPERT Digital Mold, presented by Netzsch Process Intelligence GmbH (Bayern, Germany), is a process control solution developed out of the company’s 50+ years of experience in plastics manufacturing. The end-to-end, integrated equipment-as-a-service solution enables processors to digitize and optimize their production. With a combination of advanced measuring hardware and material science with artificial intelligence, the technology provides moment-by-moment in-mold material characterization and process data to forecast the impact of internal and external mold conditions on final part quality and performance. sensXPERT Digital Mold elicits process efficiency by evaluating and controlling all critical influencing factors.  

The sensXPERT solution is comprised of four main components: material characterization sensors, an accompanying edge device, an edge device interface and a digital Cloud service. The material characterization sensors are at the heart of the sensXPERT system. The sensors use dielectric analysis to measure the material behavior of various materials, including thermosets, thermoplastics, fiber-reinforced polymers (FRPs), elastomers and sands or stones bonded with resin.

Moreover, the sensors transfer all collected data to the sensXPERT edge device. The IPC is said to excel in its mathematical and physical/chemical models of material behavior and the machine learning algorithms that consistently scan incoming data to detect patterns and deviations in the process. The algorithms translate all raw sensor data into predictive quality criteria, present them to operators via the edge device interface and empower manufacturers to adjust their process parameters in real time. Furthermore, all data is displayed in the sensXPERT Cloud service, thus allowing manufacturers to visualize and compare historical process data across multiple production sites.  

This smart solution provides manufacturers with insight, process transparency and the ability to act decisively before producing scrap. Additionally, benefits such as a digital thread per part produced can aid manufacturers in complying with reporting requirements. Overall, sensXPERT achieves a decrease of up to 50% in existing scrap, an increase of up to 23% in energy savings and up to 30% cycle time reduction.  

In addition, company expert Alec Redmann will be a featured speaker, presenting a session on “Real-Time Process Optimization Using In-Mold Dielectric Analysis and Machine Learning.” The described study demonstrates the integration of specialized dielectric analysis sensors, machine learning algorithms and material models to directly measure critical material and process data in real time during manufacturing. In addition, sensXPERT managing director Cornelia Beyer will participate in a panel discussion focused on AI machine learning in composites, which will be followed by a group Q&A session.