Dielectric In-Line Monitoring for Composite Cure Feedback Control
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Solution Overview
Problem
Current industrial composite manufacturing processes lack real-time, in-line monitoring capabilities for curing behavior, leading to inefficiencies in controlling production costs, component quality, and material behavior, particularly due to the absence of effective feedback mechanisms for optimizing curing cycles and addressing defects.
Innovation Solution
A system and method for real-time in-line monitoring using dielectric sensors and cloud-based analytics, where sensor data from manufacturing machines is processed and analyzed to optimize curing processes, allowing for predictive insights and feedback to improve manufacturing efficiency and quality control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If real-time in-line monitoring is implemented, then manufacturing precision and quality control are improved, but device complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where dielectric sensors continuously monitor curing behavior and transmit data via cloud interface to a data processing system. The system compares measured curing characteristics with target values and automatically adjusts process parameters, creating a closed-loop control system that improves manufacturing precision through real-time feedback without requiring complex manual intervention
Solution Approach 2:
The patent replaces traditional mechanical or manual monitoring methods with dielectric analysis technology that uses electromagnetic fields to measure curing characteristics. This substitution of measurement principles allows for non-contact, real-time monitoring of curing processes, reducing the need for physical sensors embedded in the material and simplifying the overall monitoring system architecture
2Productivity
If cloud-based data analysis is employed, then productivity and optimization are improved, but loss of time for data transmission occurs
Solution Approach 1:
The patent implements preliminary action by pre-configuring target curing characteristics and process parameters in the data processing system before manufacturing begins. The system pre-establishes the cloud interface and data transmission protocols, so that when manufacturing starts, data can be immediately transmitted and analyzed without setup delays, maximizing productivity from the outset
Solution Approach 2:
The patent ensures continuity of useful action by implementing continuous real-time data transmission from dielectric sensors through the cloud interface to the data processing system. This uninterrupted data flow allows for continuous monitoring and immediate optimization decisions, eliminating gaps in the manufacturing process and maintaining continuous productive action throughout the curing cycle
3Reliability
If in-line monitoring is implemented, then reliability of quality control is improved, but device complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where dielectric sensors continuously monitor curing behavior and transmit data via cloud interface to a data processing system. The system compares measured curing characteristics with target values and automatically adjusts process parameters, creating a closed-loop control system that improves manufacturing precision through real-time feedback without requiring complex manual intervention
Solution Approach 2:
The patent applies self-service by enabling the manufacturing system to automatically monitor its own curing processes through integrated dielectric sensors and perform self-diagnosis by comparing real-time data against predetermined targets. The system autonomously identifies deviations and triggers corrective actions without external quality control personnel, enhancing reliability while keeping the system architecture manageable
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise optimization of curing cycles, reduces defects, and enhances quality control by providing real-time data analysis and feedback, improving production consistency and adaptability across batches and locations.
Implementation Method 1
measurement methods are disclosed in WO 2018/142977 A1... dielectric analysis (DEA)... multiple physical characteristics such as dielectric loss factor, ion conductivity... may be determined by way of measurement of dipole polarization and ion migration patterns
Data Source
Figure 1~2
Figure 3
AI summary
A data acquisition system (10), in particular for dielectric analysis measurements, includes a sensor interface (4) configured to connect to one or more sensors (22a; 22b) located within an active machining zone (23) of an industrial manufacturing machine (20), a module processor (1) coupled to the sensor interface (4) and configured to receive measurement values from one or more sensors (22a; 22b) connected to the sensor interface (4), a cloud interface (3) coupled to the module processor (1), and a machine interface (5) coupled to the module processor (1). The measurement values indicate physical properties of workpieces processed in the active machining zone (23) of an industrial manufacturing machine (20). The cloud interface (3) is configured to connect to cloud-based resources (40), and the machine interface (5) is configured to connect to a controller (21) of the industrial manufacturing machine (20). The module processor (1) is configured to transmit the received measurement values from the one or more dielectric sensors (22a; 22b) to cloud-based resources (40) via the cloud interface (3) and to transmit manufacturing control signals to the controller (21) of the industrial manufacturing machine (20) via the machine interface (5), the manufacturing control signals being based on parameters received from cloud-based resources (40) via the cloud interface (3).