1. CRESCENDO Full virtuality in design and product development within the extended enterprise Naples, 28 Nov. 2007

2. INASCO A high-technology privately held industrial SME founded in 1989 Areas of expertise: Company overview: 20 Top rate researchers/developers Multidisciplinary expertise: Process Monitoring Sensors, Composites Manufacturing, Materials Science, CAD/CAM, Engine Noise Control 1,5 m€ per annum in the last 2 years invested in New Research Studies and Technologies development 2 m€ investment on new manufacturing plant for high-end aerospace components (commencing manufacturing activities in 3 rd Q 2009)

3. INASCO expertise related to CRESCENDO CRESCENDO GOALSINASCO EXPERTISE Virtual Overall Aircraft Model. Virtual Stochastic Life – Cycle Design (VSLCD) platform (1) Methodology and tools: Uncertainty Management and Decision Support, Unified Analysis Joint Probabilistic Decision Making (JPDM) technique (2) Probabilistic Analysis including mechanical, thermal, aerodynamic, noise, weight and cost. Prometheus software (3) Engineering Capabilities: Multidisciplinary Investigation of solution field, Early Multidisciplinary Design Multidisciplinary Design Optimization tools (4)

4. INASCO expertise related to CRESCENDO VSLCD: A novel multidisciplinary environment for design, in which new techniques in such areas as physics-based analysis, uncertainty modeling, prediction, system synthesis, and decision-making are integrated. Challenge of next – generation systems design:  Traditional methodologies are becoming ineffective for designing complex systems that meet multiple goals and disciplines.  Manufacturing and Inspection related issues must be considered in concert with product Performance in the presence of Uncertainty. 1. Development of Virtual Stochastic Life-Cycle Design (VSLCD) platform

5. INASCO expertise related to CRESCENDO NDI Manufacturing & real – time monitoring Life Prediction JPDM NDI Simulation Platform Probabilistic Life – Cycle Prediction Framework Virtual Stochastic Life – Cycle Design Probabilistic association of Manufacturing process parameters to Defects generation. Manufacturing Process Monitoring Data for Structural Analysis. Life Prediction incorporating Manufacturing and NDI Data / Parameters. Life – Cycle Design Optimization using Joint Probabilistic Decision Making. 1. Development of Virtual Stochastic Life-Cycle Design (VSLCD) platform

6. INASCO expertise related to CRESCENDO Manufacturing Process Monitoring Inspections Solid Mechanics Collaborative Methods Subsystems Virtual Manufacturing Monitoring System NDI Techniques (Stochastic) FEM Life Prediction Prediction Models Integration MDO Quality PLCPF Uncertainty Propagation Probabilistic Methods 1. Problem Formulation: Determination of the Design Space Topology Configurable quantities (variables), bounds, distributions. Non – configurable quantities (parameters), distributions. Constraints between variables / parameters. User – defined Criteria as an implicit or explicit function of variables / parameters 1. Problem Formulation: Determination of the Design Space Topology Configurable quantities (variables), bounds, distributions. Non – configurable quantities (parameters), distributions. Constraints between variables / parameters. User – defined Criteria as an implicit or explicit function of variables / parameters 1. Development of Virtual Stochastic Life-Cycle Design (VSLCD) methodology

7. INASCO expertise related to CRESCENDO Manufacturing Process Monitoring Inspections Solid Mechanics Collaborative Methods Subsystems Virtual Manufacturing Monitoring System NDI Techniques (Stochastic) FEM Life Prediction Prediction Models Integration MDO Probabilistic LP PLCPF Uncertainties Probabilistic Methods 2. Life-Cycle Modeling Analysis tools (statistical, or physics-based) that are used to assess the contribution of each phase to the integrated framework. More detailed: Process monitoring provides useful output (uncertainties, possible defects) to be used for the optimal configuration of the manufacturing parameters (temperature, pressure, sensor topology, etc). NDI techniques provide probabilistic information on the defect parameters (type, amount, size, etc) to be used for Life Prediction and correlation with Manufacturing parameters. Structural Analysis packages and methods that are able to handle probabilistic input. Probabilistic Life Assessment by incorporating Manufacturing, NDI and Life Prediction Models. Probabilistic Methods that are employed to evaluate the propagation of uncertainties over time as well as the statistical correlation of different quantities. MDO methods make use of the interaction between different disciplines that are created by the breakdown of the structural system into subsystems. 2. Life-Cycle Modeling Analysis tools (statistical, or physics-based) that are used to assess the contribution of each phase to the integrated framework. More detailed: Process monitoring provides useful output (uncertainties, possible defects) to be used for the optimal configuration of the manufacturing parameters (temperature, pressure, sensor topology, etc). NDI techniques provide probabilistic information on the defect parameters (type, amount, size, etc) to be used for Life Prediction and correlation with Manufacturing parameters. Structural Analysis packages and methods that are able to handle probabilistic input. Probabilistic Life Assessment by incorporating Manufacturing, NDI and Life Prediction Models. Probabilistic Methods that are employed to evaluate the propagation of uncertainties over time as well as the statistical correlation of different quantities. MDO methods make use of the interaction between different disciplines that are created by the breakdown of the structural system into subsystems. 1. Development of Virtual Stochastic Life-Cycle Design (VSLCD) methodology

8. INASCO expertise related to CRESCENDO Manufacturing Process Monitoring Inspections Solid Mechanics Collaborative Methods Subsystems Virtual Manufacturing Monitoring System NDI Techniques (Stochastic) FEM Life Prediction Prediction Models Integration MDO Quality PLCPF Uncertainty Propagation Probabilistic Methods 3. Integration: Operates on the knowledge produced by Analysis Models and Problem Formulation to generate design options that will be evaluated at “Decision Making” stage. 3. Integration: Operates on the knowledge produced by Analysis Models and Problem Formulation to generate design options that will be evaluated at “Decision Making” stage. 1. Development of Virtual Stochastic Life-Cycle Design (VSLCD) methodology

9. INASCO expertise related to CRESCENDO Manufacturing Process Monitoring Inspections Solid Mechanics Collaborative Methods Subsystems Virtual Manufacturing Monitoring System NDI Techniques (Stochastic) FEM Life Prediction Prediction Models Integration MDO Quality PLCPF Uncertainty Propagation Probabilistic Methods 4. Decision Making: The “core module” JPDM: Joint Probabilistic Decision Making technique. A design optimization method that maximizes the Probability of satisfying all design Criteria (manufacturing cost, structural strength, weight, Probability of Defect, etc.) simultaneously. 4. Decision Making: The “core module” JPDM: Joint Probabilistic Decision Making technique. A design optimization method that maximizes the Probability of satisfying all design Criteria (manufacturing cost, structural strength, weight, Probability of Defect, etc.) simultaneously. 1. Development of Virtual Stochastic Life-Cycle Design (VSLCD) methodology

10. INASCO expertise related to CRESCENDO JPDM is an in-house Probabilistic Multi – Criteria Decision Making and Optimisation tool. It maximizes the Probability of Success, POS of a set of Criteria simultaneously by taking account Uncertainties arising from the environment or structure. JPDM can be applied on every Design phase (Conceptual, Preliminary, Detailed) as long as system models and uncertainty information are available in any format. Methodology Steps:  Criteria Definition – Weighting  Variables/Parameters & Distributions definition  Simulation  Joint Probability Distribution Evaluation  Decision Making  Change of target values (trade – off procedure) Methodology Steps:  Criteria Definition – Weighting  Variables/Parameters & Distributions definition  Simulation  Joint Probability Distribution Evaluation  Decision Making  Change of target values (trade – off procedure) 2. Implementation of Joint Probabilistic Decision Making technique

11. INASCO expertise related to CRESCENDO Benefits:  Uncertainties are taken into account due to method’s Probabilistic nature.  Multi-criteria information into a single objective (Probability of Success).  Enables requirements trade – off studies. Benefits:  Uncertainties are taken into account due to method’s Probabilistic nature.  Multi-criteria information into a single objective (Probability of Success).  Enables requirements trade – off studies. Case study: NACRE Project (New Aircraft Concepts Research)  Evaluation of Cabin – Structural – Aerodynamic concepts.  Engine positioning optimization.  Manufacture-driven wing optimization. Assessment of the potential for economics and time reduction in the manufacture and maintenance of wing. In – side - out approach (cabin – skin – skeleton) is adopted and different cabin concepts are under investigation and comparison. 2. Implementation of Joint Probabilistic Decision Making technique

12. INASCO expertise related to CRESCENDO ADMIRE Prometheus Software is an in-house Probabilistic Design software tool. Its modules have been successfully applied on various probabilistic structural analysis problems such as: i) fatigue crack growth Reliability and Sensitivity Analysis and ii) ageing prediction of various aircraft components (Ref: ADMIRE, RAMGT, TATEM). 3. PROMETHEUS Software: Reliability Analysis - Sensitivity

13. INASCO expertise related to CRESCENDO Optimal Latin Hypercube and Kriging Surrogate Model comprise “state of the art” tools for MDO (HISAC project) Implementation of advanced modules applied on Multidisciplinary Design Optimisation (projects: HISAC, MUSCA). “State of the art” Sampling techniques (Quasi – Random Monte Carlo, Optimal Latin Hypercube), Surrogate Models (Voronoi Tessellation, Kriging), and robust Evolutionary Optimization algorithms are mainly used for Optimizing complex systems with a large amount of variables using a relatively low amount of high – fidelity information. Implementation of advanced modules applied on Multidisciplinary Design Optimisation (projects: HISAC, MUSCA). “State of the art” Sampling techniques (Quasi – Random Monte Carlo, Optimal Latin Hypercube), Surrogate Models (Voronoi Tessellation, Kriging), and robust Evolutionary Optimization algorithms are mainly used for Optimizing complex systems with a large amount of variables using a relatively low amount of high – fidelity information. MUSCA 4. Multidisciplinary Design Optimization tools