sensXPERT provides a data-driven manufacturing solution for the plastics industry that integrates measuring hardware and data science to optimize production processes. The system characterizes materials in real-time, enabling precise control of influencing factors to reduce scrap, energy consumption, and machine downtime while increasing cycle speed for thermoset materials.
Funding
Funding not disclosed
Founders
Product
Problem
In plastics manufacturing, real-time material behavior is often obscured, leading to inconsistencies in part quality, increased scrap rates, and inefficient energy consumption. Traditional process monitoring methods lack the granularity to detect subtle material deviations during production.
Solution
sensXPERT offers a data-driven manufacturing solution that provides real-time insights into material behavior, enabling precise control and optimization of plastics production processes. The system utilizes advanced dielectric sensors and AI-powered analytics to monitor material properties at the molecular level, detecting anomalies and deviations as they occur. This allows manufacturers to proactively adjust process parameters, reduce waste, improve part quality, and increase production efficiency across a range of materials, including thermosets, thermoplastics, elastomers, and composites. sensXPERT's technology adapts to various manufacturing processes, such as injection molding, resin transfer molding, and continuous manufacturing, ensuring consistent quality and optimized outcomes.
Target Audience
The primary target audience includes manufacturers in the plastics, composites, and rubber industries seeking to optimize their production processes, reduce waste, and improve part quality, specifically in automotive, aerospace, and electronics.
Features
- Real-time monitoring of material properties, including degree of cure, viscosity, and glass transition temperature, using dielectric sensors
- AI-powered data processing that identifies patterns and anomalies in production data
- Contactless cure monitoring technology for composite structures up to 40 mm thick
- High-frequency dielectric analysis (HF-DEA) to track permittivity and conductivity
- Web-based interface for visualizing material behavior and process parameters
- Integration with existing manufacturing equipment and processes
- Predictive machine learning models for accurate process control
- Anomaly detection capabilities to identify and address inconsistencies in real-time