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Technical Conferences [clear filter]
Wednesday, June 2

14:00 CEST

Intelligent adaptive composite production technologies
  • 4.0 industry processes
  • latest innovations in composites
  • adaptive production

The presentation will include 4.0 industry processes and the latest innovations in composites, such as the developed production system for the manufacture of hybrid FRP components “iComposite 4.0”. Multi-material with long glass fibers and continuous carbon fibers are used for load specific designs with cost reduction. Moreover, the multi-stage process is used to establish an control-algorithm to compensate fluctuations of the mechanical properties of the final part during production. Core aspects are the integration of inline measurement technology, modelling and decision algorithms into the production system.

Speakers, Jury Members & Final...
avatar for Philipp WIGGER

Philipp WIGGER

Research Assistant, AZL Aachen GmbH
Philipp Wigger works as a research assistant at the Aachen Centre for integrative Lightweight Production. In April 2018 he got his bachelor's degree in mechanical engineering from the RWTH. He followed this up with a master's degree in plastics engineering, that he finished in July... Read More →

Wednesday June 2, 2021 14:00 - 14:25 CEST
Conferences (Hall 6 - Room 611)

14:25 CEST

The Implementation, Use and ROI of IIoT and AI Technologies in Composites Manufacturing
  • Industry 4.0 Technologies Overview
  • How to Calculate your Smart Factory ROI
  • Case Studies & Implementation Best Practices

Facing increasing market demands, advanced manufacturers must examine methods to support high-rate programs at lower costs. Industrial IoT and AI (Artificial Intelligence) technologies open a new horizon of possibilities for these advanced manufacturers. Real-time, context-aware recommendations and actionable insights driven by AI and real-data collection, allow factories to become smart and to optimize their operations, increase production, reduce costs and stay ahead of the competition, by digitizing and optimizing their complex production environments.
Learn through real-life challenges, implementation examples and best practices:
How AI-based technologies and methods create new opportunities for advanced manufacturers, fitting a range of business models, needs and constraints;
How automation and end-to-end digitization of advanced manufacturing, including additive manufacturing, enable enhanced productivity and efficiency across industrial and business processes, creating a sustainable competitive advantage with a tangible, fast ROI.
In addition to aerospace, other industries are potential targets for the solution including automotive, motorsport, wind energy, mass transit, sporting goods and healthcare.

Speakers, Jury Members & Final...
avatar for Avner BEN-BASSAT


President & CEO, Plataine
Avner Ben-Bassat is the President & CEO of Plataine, a leading provider of Industrial IoT and AI-based optimization solutions for complex manufacturing environments.Avner leads Plataine’s product vision and global business strategy. Plataine’s solutions are used by leading manufacturers... Read More →

Wednesday June 2, 2021 14:25 - 14:50 CEST
Conferences (Hall 6 - Room 611)

14:50 CEST

AI driven quality assurance for composite parts using ultrasound
  • Business case behind AI
  • Deep Learning assisted defect detection
  • Wider applicability

Premium AEROTEC has developed a deep learning model for defect detection in composite aircraft components based on ultrasound scans. Non-destructive testing is indispensable for the production of CFRP parts in the aerospace industry. For large components, such as the A350XWB fuselage shells, ultrasonic testing is the standard method of detection. Acquisition of data for these parts is already highly automated. Data evaluation is nevertheless fully manual and may require more than one working day per part. Continuous tasks demanding human expertise, to be applied repeatedly across the complete scanned area of each part, requires AI assistance to increase output whilst maintaining quality check standards. The business case for reducing manual overheads, through the automation of data evaluation, mitigates fatigue and other phenomena that decrease quality.
Classical computer vision methods are insufficient to accomplish this goal. With the recent success of artificial intelligence in the field of image recognition, it is now possible to automate defect detection. A general approach to automation using Deep Learning will be presented. A wider applicability of the method to other signal types such as X-ray, CT and active thermography is possible.

Speakers, Jury Members & Final...
avatar for Olaf BEESDO


Data Scientist, Premium AEROTEC GmbH
Olaf Beesdo has worked for many years within the aerospace industry with particular interest in industrializing the latest technological trends. Currently, he works at Premium AEROTEC GmbH as a Data Scientist. His remit is the analysis and optimization of manufacturing processes with... Read More →

Wednesday June 2, 2021 14:50 - 15:15 CEST
Conferences (Hall 6 - Room 611)

15:15 CEST

15:35 CEST

Composites and their forming processes in the era of Data and Artificial Intelligence: Composite Twins
  • Hybrid-Twins
  • Proper Generalized Decomposition (PGD)
  • Big data

Model order reduction -MOR- allows speeding-up complex calculations, by using reduced bases extracted in the “offline learning stage”, or constructed on-the-fly during the problem solution. The former procedure is at the origin of Proper Orthogonal Decomposition (POD) and Reduced Basis (RB) MOR methodologies. The latter, at the origin of the so-called Proper Generalized Decomposition (PGD) could seem, at first view, unattractive because of the fact that the reduced model is computed during the solution procedure itself. In fact, the PGD efficiency comes from the fact of solving the model by considering the model parameters as problem extra-coordinates, leading to a parametric solution. That solution, in order to circumvent the so-called curse of dimensionality, is expressed in a separated form.
Our recent researches leaded to a non-intrusive solver of parameterized partial differential equations that was successfully employed in a diversity of engineering problems of industrial relevance. Today the so-called sparse variant of the PGD solver allows the efficient computation of such non-intrusive parametric solutions at the very low-data limit. Problems involving moving fronts, localization or geometrical and topological parameters, usually encountered in RTM and SMC composite forming processes, require advanced interpolation schemes.
With such a robust parametric solution available, control, inverse identification, optimization and uncertainty quantification and propagation, all them operating under the stringent real-time conditions are attainable and were properly coupled with data-assimilation to produce efficient DDDAS (dynamic data-driven applications systems), that is, the so-called material and process digital and hybrid-twins.
Within the context of composites modeling, their associated forming processes and their in-service performances, we will prove that hybrid twins allow (i) a natural alliance between physics and data, mathematics and artificial intelligence, (ii) proceeding efficiently at the scarce-data limit and (iii) certifying designs.

Speakers, Jury Members & Final...
avatar for Francisco CHINESTA

Francisco CHINESTA

President of the Scientific Committee and Director of the Scientific Department, ESI Group
Francisco Chinesta is currently full Professor of computational physics at ENSAM Institute of Technology (Paris, France). He was (2008-2012) AIRBUS Group chair professor and since 2013 he is ESI Group chair professor on advanced modeling and simulation of materials, structures, processes... Read More →

Wednesday June 2, 2021 15:35 - 16:00 CEST
Conferences (Hall 6 - Room 611)

16:00 CEST

Future additive and composite manufacturing by using industrial IoT and AI-based optimization solutions
  • digitization
  • data analytics
  • material & asset tracking

Digitization and industrial IoT becoming more and more relevant for the future production of lightweight components in aviation industry. Hence, the Composite Technology Center (CTC) of AIRBUS in Stade, Germany, and Plataine from Tel-Aviv, Israel, are demonstrating advanced IoT solutions for industrial 3D printing in CTC’s “3D Hub”. Operating industrial 3D printer, bringing enhanced digitization, real-time alerts and recommendations to the production process.
Additive Manufacturing offers shorter time to market, greater production flexibility, increased quality and cost reduction for certain low-mid volume production series. In order to deliver parts on time at the highest quality, the CTC deployed Plataine’s software to digitize, automate and optimize the manufacturing process while collecting sensor and machine data for analytics and smart predictions. The solution optimizes spool management & consumption, enhances part traceability, and offers complete visibility and process control. IIoT and digital assistants offer predictive alerts, actionable insights and real-time recommendations to staff, allowing them to further optimize their operations and proactively deal with production challenges.
In general, that brings an automated, intelligent, end-to-end solution for additive manufacturing operations using applications such as tool tracking, material management and shelf-life management. Meanwhile, all production data is stored forming digital threads, recording of the entire production process, from raw material to end-product, creating the basis for the subsequent project phase of applying IoT-based capabilities to further improve the process and to additionally transfer the knowledge to future composite production.

Speakers, Jury Members & Final...
avatar for Walter Ofer ABRAMSOHN


Head of Product Development and Product Marketing, Plataine
Walter Abramsohn is the head of Product Development and Product Marketing at Plataine. Mr. Abramsohn has a vast background in technology encompassing both software and hardware solutions. He has held diverse leadership roles spanning from R&D to Product Management and Product Marketing... Read More →
avatar for Jan-Patrick KALCKHOFF


R&D Project Manager, Composite Technology Center (An AIRBUS Company)
Jan-Patrick Kalckhoff is working as a R&D project manager in the field of IoT and digitization at the Composite Technology Center of AIRBUS in Stade, Germany.

Wednesday June 2, 2021 16:00 - 16:25 CEST
Conferences (Hall 6 - Room 611)

16:25 CEST

Identifying Costly Design Features and Manufacturability Issues Early in the A&D Development Process
  • Simulation of aerospace manufacturing processes during design to identify expensive features and manufacturability issues directly from 3D CAD model
  • Reduction of late stage design churn and Engineering Change Orders (ECOs) by getting it right the first time.
  • Improvement of your negotiating position with suppliers through increased visibility to costs.

With arguably some of the world’s most complex product development programs, aerospace and defense manufacturers are experiencing considerable pressure to contain escalating product costs as they innovate using the latest materials and manufacturing processes. However, aggressive program schedules and an emphasis on optimizing designs for performance, weight, and safety leaves little time to focus on cost. As a result, extensive cost down initiatives and value engineering activities often commence after initial product release.
The most progressive manufacturers are transforming their processes by leveraging advanced software technology to Design for Manufacturability and Cost (DFMC) early in the design phase.
In this presentation, Steven Peck, a product development technology executive with 20+ years of experience, will describe how industry giants such as Honeywell Aerospace, Boeing, Spirit AeroSystems and others are responding to these market pressures.
He will discuss the application of a solution that enables:
  • Simulation of aerospace manufacturing processes during design to identify expensive features and manufacturability issues directly from 3D CAD models.
  • Evaluation of design trade-off decisions to reduce cost in real time.
  • Reduction of late stage design churn and Engineering Change Orders (ECOs) by getting it right the first time.
  • Improvement of your negotiating position with suppliers through increased visibility to costs.
Following the session, the presenter will be available to provide one-on-one sessions for analysis of your own designs that you can uploaded to a secure portal.

Speakers, Jury Members & Final...
avatar for Steven PECK

Steven PECK

VP, Applications Engineering & Solutions, aPriori
Steven PeckDesign and Manufacturing Technology ExecutiveSteve has extensive experience working within the aerospace & defense industries, consulting with Global Fortune 100 A&D manufacturers on Business & Technology Solutions that improve innovation, product profitability and quality... Read More →

Wednesday June 2, 2021 16:25 - 16:50 CEST
Conferences (Hall 6 - Room 611)

16:50 CEST

Applying machine learning to process and characterisation data of nanoenhanced composites: a means for prediction
  • Machine Learning
  • Characterisation
  • Nanoindentation

This work describes a novel methodology of data documentation in materials characterisation, which has as starting point the creation and usage of any Data Management Plan (DMP) for scientific data in the field of materials science and engineering, followed by the development and exploitation of ontologies for the harnessing of data created through experimental techniques. The case study that is discussed here is nanoindentation, a widely used method for the experimental assessment of mechanical properties on a small scale. Except for technology development and synthesis of new materials and hybrid composite structures, the need of developing new evaluation methodologies is highlighted to assist and accelerate developments. Artificial Intelligence (AI) is a promising candidate to bridge the gap between Research and Development (R&D) and industry by establishing unbiased relations between microstructure and properties. This is majorly appreciated in case of Safe-by-Design requirements regarding mechanical performance, and real-time characterisation. Being representative, k-means, Random Forrest (RF), Support Vector Machines (SVM), k-Nearest Neighbors (KNN) are common Machine Learning (ML) algorithms used in multiclass classification problems for automated classification of microstructures.
This work contributes to nanocomposites design and quality control associated with identifying the optimum inclusion in nanomaterials reinforcement by microstructure assessment. In this direction, Artificial Intelligence can provide a module for enabling fast, in-line, and real-time metrological characterisation of nanoindentation data.
This work has been partially supported by the EU Horizon 2020 Programmes: MODCOMP (GA No 685844), SMARTFAN (GA No 760779), OYSTER (GA No 760827) and REPAIR3D (GA No 814588).

Speakers, Jury Members & Final...
avatar for Elias KOUMOULOS


Dr. Elias P. Koumoulos holds a BSc in Chemical Engineering, followed by MSc in Materials Science and Technology and PhD in nanomechanics. To date, he has the authorship of over 70 published papers in ISI journals, 6 book chapters, 90 participations in national/international conferences... Read More →

Wednesday June 2, 2021 16:50 - 17:15 CEST
Conferences (Hall 6 - Room 611)