Embedded NERVE system for intelligent manufacturing of multifunctional composites for vehicles (Final Report)
Statement of the problem: Acellent Technologies Inc. in collaboration with subcontractor University of Delaware (UDel) has proposed to develop an innovative cradle-to-grave Networked Elements for Resin Visualization and Evaluation (NERVE) system consisting of embedded actuating and sensing elements to monitor and enhance all phases of the RTM manufacturing process used in the automotive manufacturing industry. This proposed process advancement utilizes an embedded sensor network to: (1) Optimize the manufacturing process for conventional RTM processes and variations thereof, (2) Measure and monitor the resin flow during the filling and curing cycle, (3) Improve manufacturing quality through real-time monitoring and feedback control, (4) Increase throughput by optimizing production rate, (5) Reduce overall costs by eliminating time-consuming post-fabrication inspections and (7) Enable new capabilities in the produced structural components by facilitating self-monitoring throughout the life of the part. Phase I results: In Phase I to develop the NERVE system for in-situ process monitoring and active feedback control of resin flow, curing and flaw mitigation, the first priority was to have a sensor network with multi-modal sensing capabilities integrated within the composite. The sensor network will have the following functions: 1) monitor resin flow, 2) detect voids, 3) measure temperature, 4) monitor cure status and accelerate cure, 5) provide feedback to control RTM process, 6) assess the manufacturing quality, and 7) monitor usage damage. Acellent worked with University of Delaware to develop the sensor design requirements for the composite manufacturing processes. Optimized placement of sensors and actuators was designed to ensure that the manufacturing defects during manufacturing and post-manufacturing damage during usage can be accurately detected. Sensor layers were designed to be placed on the top and bottom surfaces of the composite during the manufacturing process itself. Acellent’s ScanGenie Mini hardware was utilized for data acquisition and functional verification in this application. The RTM composite manufacturing process was carried out on composite test specimens at UDel and resin flow data collected. 3D rendering of resin flow was developed along with a preliminary algorithm to monitor degree of cure and to develop a preliminary algorithm for quality assurance and damage detection. After the installation of the sensors on CFRP plies and the mold, data acquisition was initiated. The curing module incorporated in Acellent’s SHM Composite software assessed the data collected and provided information on the curing status and the flow rate across different regions of the CFRP. The sensor data was also be used to assess curing gradient across different parts of the RTM mold. Continuous monitoring of curing state was done using Acellent’s SHM Composite software using the Damage Index (DI) parameter. Preliminary results indicated that the developed algorithms can detect when the part has cured and can save at least 30% manufacturing time for the composite part leading to manufacturing cost savings. The integrated sensors were also able to detect 100% damage simulated in the structure post manufacturing. Commercial Applications and Other Benefits: The NERVE system will influence the current genre of RTM manufacturing by providing immense benefits to the composite manufacturing industry including (1) Conceptual leap in the manufacturing process for diverse composite structures, (2) Considerable time savings in manufacturing due to reduction of post fabrication inspections, (3) Rapid and economic production of large composite structures leading to increased usage of composites in automotive, defense, aerospace and energy industries, (4) New advanced material that can save billions of dollars in life-cycle costs due to inspection and maintenance and (5) Environmental benefits due to reduction in scrapped parts.
- Research Organization:
- Acellent Technologies Inc.
- Sponsoring Organization:
- USDOE Office of Science (SC)
- Contributing Organization:
- University of Delaware
- DOE Contract Number:
- SC0020015
- OSTI ID:
- 1764436
- Type / Phase:
- SBIR (Phase I)
- Report Number(s):
- 501-19066-001
- Country of Publication:
- United States
- Language:
- English
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