Smart Industrial Internet of Things Framework for Composites Manufacturing

Area of Science:

• Materials Science and Engineering
• Manufacturing Technology
• Industrial Internet of Things (IIoT)

Background:

• Composite materials are crucial for high-performance products but face manufacturing challenges due to material variability and process inconsistencies.
• Existing methods struggle to address the inherent stochasticity and variabilities in composites manufacturing.

Purpose of the Study:

• To propose a novel smart Industrial Internet of Things (IIoT) framework, also known as an Artificial Intelligence of Things (AIoT) framework, for composites manufacturing.
• To enhance production performance through real-time process monitoring and AI-based forecasting.

Main Methods:

• Development of an AIoT framework integrating sensor data (temperature, heat flux, dielectric, flow) with an IoT platform.
• Implementation of an AI-based model for real-time production process monitoring, visualization, and forecasting.
• Proof-of-concept validation using a real-world composites manufacturing case study.

Main Results:

• The proposed AIoT framework enables real-time monitoring and visualization of the composites manufacturing process.
• AI-based forecasting accurately predicts future process states, identifying potential deviations.
• The case study demonstrated significant improvements in production performance and process control.

Conclusions:

• The novel AIoT framework effectively addresses challenges in composites manufacturing by integrating real-time data and AI-driven predictions.
• This approach offers a pathway to more robust, efficient, and predictable composite production.
• The framework's successful implementation highlights its potential for industrial adoption.