TPI Composites Participates in Cure Optimization for Wind Blade Fabrication with University of Texas at Dallas
TPI Composites (Nasdaq: TPIC) has announced its collaboration with the University of Texas at Dallas to develop a smart 'digital twin' for optimizing the composite curing process in wind blade manufacturing. The project, funded by the Office of Energy Efficiency & Renewable Energy, aims to apply physics-informed machine learning algorithms to simulate and optimize the curing process through multi-zone temperature control.
The digital twin will use inputs such as spatial and temporal process characteristics and material properties to guide the manufacturing process by providing optimal temperature profiles. Real-time sensor data will be used to adjust thermal loads in multiple heating zones based on the optimal cure trajectory identified by the surrogate digital twin.
This collaboration is expected to accelerate the transition of ML-based modeling tools from academia to industrial applications, potentially leading to cost savings and performance improvements in composite manufacturing.
TPI Composites (Nasdaq: TPIC) ha annunciato la sua collaborazione con l'Università del Texas a Dallas per sviluppare un 'gemello digitale' intelligente, volto a ottimizzare il processo di indurimento dei compositi nella produzione di pale eoliche. Il progetto, finanziato dall'Ufficio per l'Efficienza Energetica e le Energie Rinnovabili, mira ad applicare algoritmi di machine learning informati dalla fisica per simulare e ottimizzare il processo di indurimento attraverso il controllo della temperatura in più zone.
Il gemello digitale utilizzerà input come caratteristiche spaziali e temporali del processo e proprietà dei materiali per guidare il processo produttivo fornendo profili termici ottimali. Dati sensoriali in tempo reale saranno utilizzati per regolare i carichi termici in più zone di riscaldamento basandosi sulla traiettoria di indurimento ottimale identificata dal gemello digitale surrogato.
Questa collaborazione dovrebbe accelerare la transizione degli strumenti di modellizzazione basati sul ML dall'ambito accademico a quello industriale, portando potenzialmente a risparmi sui costi e miglioramenti delle prestazioni nella produzione di compositi.
TPI Composites (Nasdaq: TPIC) ha anunciado su colaboración con la Universidad de Texas en Dallas para desarrollar un 'gemelo digital' inteligente para optimizar el proceso de curado de composites en la fabricación de palas de viento. El proyecto, financiado por la Oficina de Eficiencia Energética y Energías Renovables, tiene como objetivo aplicar algoritmos de aprendizaje automático informados por la física para simular y optimizar el proceso de curado a través del control de temperatura en múltiples zonas.
El gemelo digital utilizará entradas como características espaciales y temporales del proceso y propiedades del material para guiar el proceso de fabricación proporcionando perfiles de temperatura óptimos. Se utilizarán datos de sensores en tiempo real para ajustar las cargas térmicas en múltiples zonas de calentamiento en función de la trayectoria de curado óptima identificada por el gemelo digital sustituto.
Se espera que esta colaboración acelere la transición de las herramientas de modelado basadas en ML de la academia a aplicaciones industriales, lo que podría llevar a ahorros de costos y mejoras en el rendimiento en la fabricación de composites.
TPI Composites (Nasdaq: TPIC)는 텍사스 대학교 댈러스 캠퍼스와 협력하여 풍력 블레이드 제조에서 복합재 경화 프로세스를 최적화하기 위한 스마트 '디지털 트윈'을 개발한다고 발표했습니다. 이 프로젝트는 에너지 효율성 및 재생 에너지 사무소의 자금 지원을 받아 물리 기반 기계 학습 알고리즘을 적용하여 다중 존 온도 조절을 통해 경화 프로세스를 시뮬레이션하고 최적화하는 것을 목표로 합니다.
디지털 트윈은 공간적 및 시간적 프로세스 특성과 재료 특성과 같은 입력을 사용하여 최적의 온도 프로파일을 제공함으로써 제조 프로세스를 안내합니다. 실시간 센서 데이터는 대체 디지털 트윈이 식별한 최적 경화 궤적에 따라 여러 난방 구역의 열 부하를 조정하는 데 사용됩니다.
이 협력은 ML 기반 모델링 도구의 학계에서 산업 응용 프로그램으로의 전환을 가속화할 것으로 예상되며, 이는 복합재 제조에서 비용 절감 및 성능 향상으로 이어질 수 있습니다.
TPI Composites (Nasdaq: TPIC) a annoncé sa collaboration avec l'Université du Texas à Dallas pour développer un 'jumeau numérique' intelligent afin d'optimiser le processus de durcissement des composites dans la fabrication de pales d'éoliennes. Le projet, financé par le Bureau de l'Efficacité Énergétique et des Énergies Renouvelables, vise à appliquer des algorithmes de machine learning informés par la physique pour simuler et optimiser le processus de durcissement grâce au contrôle de la température dans plusieurs zones.
Le jumeau numérique utilisera des données telles que les caractéristiques spatiales et temporelles du processus ainsi que les propriétés des matériaux pour guider le processus de fabrication en fournissant des profils de température optimaux. Des données de capteurs en temps réel seront utilisées pour ajuster les charges thermiques dans plusieurs zones de chauffage en fonction de la trajectoire de durcissement optimale identifiée par le jumeau numérique approximatif.
Cette collaboration devrait accélérer la transition des outils de modélisation basés sur l'IA du milieu universitaire vers des applications industrielles, pouvant potentiellement entraîner des économies de coûts et des améliorations de performance dans la fabrication de composites.
TPI Composites (Nasdaq: TPIC) hat seine Zusammenarbeit mit der University of Texas at Dallas angekündigt, um einen intelligenten 'digitalen Zwilling' zur Optimierung des Aushärtungsprozesses von Windblatt-Herstellung zu entwickeln. Das Projekt, finanziert vom Amt für Energieeffizienz und erneuerbare Energien, zielt darauf ab, physikalisch informierte Machine Learning-Algorithmen anzuwenden, um den Aushärtungsprozess durch die Steuerung der Temperatur in mehreren Zonen zu simulieren und zu optimieren.
Der digitale Zwilling wird Eingaben wie räumliche und zeitliche Prozessmerkmale sowie Materialeigenschaften nutzen, um den Herstellungsprozess zu steuern, indem er optimale Temperaturprofile bereitstellt. Echtzeit-Sensordaten werden verwendet, um die thermischen Lasten in mehreren Heizungszonen basierend auf der von dem Ersatz-digitalen Zwilling identifizierten optimalen Aushärtungstrasse anzupassen.
Diese Zusammenarbeit wird voraussichtlich den Übergang von ML-basierten Modellierungstools von der Wissenschaft in industrielle Anwendungen beschleunigen, was potenziell zu Kosteneinsparungen und Leistungsverbesserungen in der Verbundwerkstoffproduktion führen könnte.
- Collaboration with University of Texas at Dallas for advanced manufacturing technology
- Potential for cost savings and performance improvements in wind blade manufacturing
- Development of a 'digital twin' for optimizing the composite curing process
- Funding support from the Office of Energy Efficiency & Renewable Energy
- None.
This collaboration between TPI Composites and the University of Texas at Dallas represents a significant advancement in wind blade manufacturing. The application of physics-informed machine learning to optimize the curing process could lead to substantial improvements in production efficiency and blade quality.
The development of a "digital twin" for the curing process is particularly noteworthy. This technology has the potential to reduce production time, minimize material waste and enhance the consistency of blade properties. By optimizing the spatiotemporal temperature profiles, manufacturers can achieve more precise control over the final mechanical properties of the blades.
From an industry perspective, this research could result in cost savings and performance improvements, giving early adopters a competitive edge in the wind energy sector. However, the real-world implementation and scalability of this technology remain to be seen and it may take time before we observe tangible impacts on TPI's bottom line.
This research collaboration highlights TPI Composites' commitment to innovation in wind turbine blade manufacturing. By leveraging advanced AI and machine learning techniques, TPI is positioning itself at the forefront of technological advancements in the renewable energy sector.
The potential benefits of this research are significant. Optimized curing processes could lead to stronger, more durable wind turbine blades, potentially increasing the lifespan and efficiency of wind farms. This could make wind energy more competitive with other power sources, driving further adoption.
However, investors should note that this is still a research project and commercial implementation may be several years away. While promising, it's too early to factor this development into short-term financial projections for TPI. Long-term, if successful, this technology could strengthen TPI's market position and potentially lead to increased market share and profitability.
The application of physics-informed machine learning in this project represents a cutting-edge approach to manufacturing optimization. By creating a "digital twin" of the curing process, TPI and UT Dallas are effectively bridging the gap between theoretical models and real-world manufacturing conditions.
This approach is particularly innovative because it combines statistical AI models with deterministic multiphysics simulations. This hybrid method could potentially overcome limitations of traditional modeling techniques, leading to more accurate and adaptable manufacturing processes.
The use of real-time sensor data for reinforced learning is another important aspect. This continuous feedback loop allows for ongoing refinement of the model, potentially resulting in a system that becomes increasingly accurate and efficient over time. However, the success of this project will depend on the quality of the data collected and the robustness of the AI algorithms developed.
SCOTTSDALE, Ariz., Aug. 14, 2024 (GLOBE NEWSWIRE) -- Today TPI Composites, Inc., (TPI) (Nasdaq: TPIC) announced its participation with the University of Texas at Dallas to apply physics-informed machine learning (ML) algorithms to simulate and optimize the composite curing process through multi-zone temperature control. This will solve a current technological gap by developing a smart “digital twin” that optimizes the curing process in blade manufacturing. Funded by the Office of Energy Efficiency & Renewable Energy, this project will accelerate the transition of research on ML-based modeling tools in academia to real industrial applications.
The digital twin will take inputs like spatial and temporal characteristics of the process and material properties of the composite components to guide the manufacturing process by providing the optimal spatiotemporal temperature profiles needed for the achieving the design target mechanical properties. Real-time data from sensors are collected as the blade manufacturing process advances. Thermal loads in multiple heating zones are then adjusted based off optimal cure trajectory identified by the surrogate digital twin in the background.
Dr. Shaghayegh Rezazadeh, TPI Lead Engineer, said “the application of statistical physics-informed AI models bridges the gap between deterministic Multiphysics simulations and kinetics of cure as happening on the shopfloor. This process leverages different heating zones integrated in TPI molds to achieve the desired mechanical properties while optimizing the cure cycle time to ensure consistent quality and enhanced productivity for the blades manufactured by TPI. The data collection from sensors enables reinforced learning and model refinement to improve the accuracy of the models and adaptation to environmental conditions and variabilities in labor-intensive manufacturing processes.”
Dr. Dong Qian, the principal investigator on the project, commented, “We are very excited to have this opportunity to work with TPI on the project. Our collaborative research will lay an important foundation for smart composite manufacturing and provide a significant competitive advantage for industries adopting these technologies, in terms of both cost savings and performance improvement.”
About TPI Composites, Inc.
TPI Composites, Inc. is a global company focused on innovative and sustainable solutions to decarbonize and electrify the world. TPI delivers high-quality, cost-effective composite solutions through long-term relationships with leading OEMs in the wind market. TPI is headquartered in Scottsdale, Arizona and operates factories in the U.S., Mexico, Türkiye and India. TPI operates additional engineering development centers in Denmark and Germany and global service training centers in the U.S. and Spain.
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