Maximizing Production Outcomes for Plastics Manufacturers

sensXPERT’s material characterization sensors wield dielectric analysis to measure real-time, in-mold material behavior. The sensors are compatible with a wide variety of materials, such as thermosets, thermoplastics, fiber-reinforced polymers, elastomers, and sands or stones bonded with resins.

By calculating the degree of cure, degree of crystallization or vulcanization during the production process, sensXPERT is capable of detecting material deviations in real time.

The sensors are robustly engineered to withstand molding pressures and temperatures.

The material characterization sensors can be integrated into processes such as (Reaction) Injection Molding, Thermoforming and Compression Molding, Transfer Molding, Vacuum Infusion, and Autoclave Curing.

Designed to be robust and durable in manufacturing environments, the sensXPERT Edge Device bridges the physical and digital worlds of polymer processing. The edge device is a powerful industrial personal computer (IPC) and dielectric analyzing system that is loaded with machine learning algorithms and offers cloud connectivity.

As the single source of truth, the sensXPERT Edge Device communicates directly with the machine, exchanging manufacturing parameters and material properties, whilst sending and receiving trigger signals.

Advanced onboard machine learning models power sensXPERT’s dynamic and adaptive process control capabilities in the face of unforeseen changes in production, for example material aging, humidity, temperature, etc.

The edge device is not only compatible with sensXPERT’s material characterization sensors, but it can also connect to and receive real-time data from third-party pressure and/or temperature sensors.

The sensXPERT WebApp enables machine operators to monitor and optimize the manufacturing process in real time.

This interface visualizes the full spectrum of process and material parameters for every product being manufactured.

The WebApp also visualizes the advanced machine learning models in action. Machine learning models accurately predict the degree of cure/crystallization, glass transition temperature, and other thermal/mechanical material properties.

By presenting these real-time predictions on the edge device interface, operators are enabled to confidently optimize their processes right away, ensuring quality control in the manufacturing of each part.

The sensXPERT Digital Cloud Service collects and stores all measurement data via REST API and is the central collection site for all process data I.e., through an OPC-UA connection.

In the Cloud, data associated with quality indicators are processed and calculated for every part produced.

Selected parameters are visualized in sensXPERT’s Digital Cloud Service, displaying trends and deviations.

Easy-to-access dashboards offer an overview of all edge devices installed across multiple sites and/or production lines in your plastics manufacturing. These dashboards also inform manufacturers of any opportunities for optimization.