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Sensor-based, Data-driven Optimization of Composites Manufacturing.pptx
- 1. Sensor-based, Data-driven Optimization of Composites Manufacturing SAMPE Europe Conference 23 Madrid
- 2. Who is behind sensXPERT? Parent Company: NETZSCH Family-owned global technology leader with 4100+ employees present in 36 countries Corporate Venture: NETZSCH Process Intelligence GmbH Enhancing productivity through advanced process analysis technology for the industry 4.0 As a recognised industry expert with 50+ years of experience in material science and sensor technology, it is the NETZSCH Group that transforms industries with next-level intelligence for increased efficiency, quality assurance and process reliability for the plastics industry. &
- 3. Technology Overview: Gathering data with sensXPERT in-mold setup
- 13. Dielectric analysis • Observes the behavior of material under the application of an alternating electric field • Molecules in the material sample have a net electric charge and move within the electric field • Ion viscosity – An analog to mechanical viscosity
- 14. Dielectric analysis Traditional parallel plate electrodes Interdigitated “comb“ electrodes sensXPERT in-mold sensor
- 15. Dielectric Sensor Information Introduction of the material Minimum resin viscosity Progression of cure / gelation / crystallization Completion of cure / crystallization
- 16. Influence of batch-to-batch variations on the curing behavior
- 17. Influence of aging variations on the curing behavior
- 19. Material: Epoxy / carbon fiber Process: VARTM followed by out-of- mold autoclave post-curing Quality criterion: Degree of cure > 80% after VARTM Degree of cure >93% after autoclave Composite aerospace structures
- 20. • Creation of kinetic models based on DSC. Material chemistry
- 21. On-site fuselage support monitoring Process Step 1 Process Step 2 Process Step 3 Degree of Cure [%]
- 22. What is machine learning? Expert Data Algorithm Output Classic Machine learning Training Data Learn Algorithm (Split)
- 23. Data science lifecycle (CRISP-DM) – Mold V1 Data Process Understanding Data Understanding Data Preparation Modeling Evaluation Deployment • Material • Goal • Process dependencies • Visualization • Robust outlier detection • Feature selection • Feature engineering Customer Feedback • Local installation (IPC) • Customer-based model • Model monitoring • Metrics • Plots • Inputs • Architecture
- 24. • Prediction with RFF-Autoregression • Prediction inputs are observed temperature and DEA data Process prediction
- 25. scrap reduction 50% Up to energy savings 23% Up to cycle time reduction 30% Up to (re)- commissioning time Reduce Documented quality: „digital birth certificate“ for every part RESULTS OF sensXPERT®
- 26. We look forward to welcoming you into the sensXPERT community! Contact us NETZSCH Process Intelligence GmbH Dr. Nicholas Ecke Gebrüder-Netzsch-Str. 19 nicholas.ecke@sensxpert.com 95100 Selb, Germany
Editor's Notes
- As a recognized industry expert with almost 50 years of experience in material science and sensor technology, it is the NETZSCH Group that transforms industries with next-level intelligence for increased efficiency, quality assurance and process reliability.
- The following animation covers two fronts: Hardware: Dielectric sensors Edge Device 2. Software Kinetics model input or ML ML in the Edge Device Cloud, WebApp
- John Presents this
- Dielctric analysis works by measuring the material behavior under the influence of an applied electric field. An electric field across the dilectric sensor causes ion motion (and dipole rotation) in the material. Positively charged ions move towards the negatively charged electrode, and negatively charged ions move towards the positively charged electrode. As the material cures or solidifies the flow of the ions is restricted, resulting in a decreased conductivity. This translates to an increase in the mechanical viscosity of the material. [Think water -> honey -> solid]
- Traditional dielectric analysis is conducted using parallel plate electrodes with the material sample (green) between the electrodes. The sensXPERT in-mold sensor uses a different, interdigitated or “comb”, configuration to enable a single flat measurement surface that can be installed directly into any mold.
- With dielectric analysis, sensXPERT can measure and record valuable process data. The ion viscosity data (blue) is an analog to the mechanical viscosity of the material (the ability for the material to flow). Point 1 shows when the material was introduced to the mold and reaches the dielectric sensor Point 2 shows where the minimum viscosity of the material occurs. The viscosity is important because it affects the ability of the resin to fill the mold. Lower viscosity allows for quicker mold filling. Point 3 shows the increase of viscosity as the material begins to cure at the elevated temperature. The inflection point of the ion viscosity curve indicates the gel point, which is where the resin can no longer flow. Point 4 shows the ion viscosity plateau marking the end of the curing reaction. It is clear the reaction completes around 120 minutes, and there is opportunity to optimize this curing cycle to reduce the cycle time.
- Animierte Folie. Obere Abschnitt zuerst sichtbar. 1. Verarbeitung von Daten bei einem klassischen (informatik) Ansatz. Ein Algorithmus wird von einem Programmierer geschrieben. Algorithmen sind wie Kochrezepte die gelieferte Daten in einem festen Schema nach festen vorgaben verarbeiten um einen gewünschten Output zu produzieren. Wichtig: Bei veränderten Eingansdaten Daten muss der Algorithmus selbst angepasst werden, keine eigen Adaption möglich. 2. Machinelles Lernen (auch Metaprogrammierung genannt) Anstelle eines Algorithmus wird ein Learnalgorithmus (Metaalgortihmus) vom Programmierer geschrieben. Dieser Lernalgorithmus ist in der Lage von Trainingsdaten eine adaptieren Algorithmus selbständig zu entwickeln. Im Prinzip kann der Learnalgorithmus die Arbeit eines Programmieres übernehmen. Zusätzlich wird es immer schwere für viele Daten einen klassischen Algorithmus zu entwickeln, stelle dir vor ein Programm zu entwickeln, dass Fell erkennt. Farbe ist nicht ausreichend, und Fellstrukturen können sich stark unterscheiden, eine einfache Gradientenanalyse reicht nicht aus. Machinelles lernen ermöglicht das entwickeln von Algorithmen, die komplexe Konzepte und Zusammenhänge verarbeiten können ohne diese in einer Computersprache zu verfassen.
- [animated version of previous figure] Example: The curing (solidification) of an epoxy resin. The in-mold sensor is collecting data (light blue dashed line). This collected data is used in combination with the machine learning model to create a prediction (green dashed line). If the prediction does not match the target value (blue solid line), the process will be dynamically adjusted. In this case, the temperature (pink line) is increased to make sure the target value of degree of cure is reached.
- John presents this
- With dielectric analysis, sensXPERT can measure and record valuable process data. The ion viscosity data (blue) is an analog to the mechanical viscosity of the material (the ability for the material to flow). Point 1 shows when the material was introduced to the mold and reaches the dielectric sensor Point 2 shows where the minimum viscosity of the material occurs. The viscosity is important because it affects the ability of the resin to fill the mold. Lower viscosity allows for quicker mold filling. Point 3 shows the increase of viscosity as the material begins to cure at the elevated temperature. The inflection point of the ion viscosity curve indicates the gel point, which is where the resin can no longer flow. Point 4 shows the ion viscosity plateau marking the end of the curing reaction. It is clear the reaction completes around 120 minutes, and there is opportunity to optimize this curing cycle to reduce the cycle time.
- Critical values like degree of cure (the progression of the curing reaction) can be calculated from the dielectric data. The green curve shows the calculated degree of cure during the process cycle.
- Dielctric analysis is the heart of the sensXPERT solution. It can measure and monitor important material behavior including viscosity, cure, glass transition temperature, and more. It is capable of measuring numerous types of materials used in a variety of plastic processing technologies.