Machine Learning based prediction of the effect of lay-up defects in the automated fiber placement

The use of Automated Fiber Placement is being widespread in the aerospace industry. The need of manufacturing large and complex structural composite components, it makes the use of this technology much more efficient than the conventional hand lay-up manufacturing. However, these components are still being manually inspected and the effect of the defects found is calculated with a simulation software. The scope of this thesis is to create a Machine Learning model that is able to calculate the effect on the effective stiffness for different defect configuration. This Machine Learning model should be provided with the geometrical defect characteristics in the laminate and it has to be able to predict, with a high level of accuracy, the effective stiffness of the laminate. Training this model with a big amount of different configuration defects generates the need to create a parametrized FE model of a composite laminate on the coupon level. The results show that a Multi Layer Perceptron architecture with two hidden layers. The first one with 281 nodes and the second one with 76 nodes which is able to predict the effective stiffness of a defective laminate coupon with an accuracy of 0,1 GPaL'ús del Automated Fiber Placement està estenent-se en la indústria aeroespacial. La necessitat de fabricar components estructurals compostos grans i complexes, fa que l'ús d'aquesta tecnologia sigui molt més eficient que la fabricació convencional amb col·locació manual. No obstant això, aquests components encara s'estan inspeccionant manualment i es calcula l'efecte dels defectes trobats amb software de simulació. L'abast d'aquesta tesi és crear un model de Machine Lerning que sigui capaç de calcular l'efecte en la rigidesa efectiva per diferents configuracions de defectes. Aquest model d'aprenentatge automàtic hauria de rebre les característiques geomètriques dels defectes en el laminat i de ser capaç de predir, amb un alt nivell de precisió, la rigidesa efectiva del laminat. Entrenar aquest model amb una gran quantitat de configuracions de defectes diferents genera la necessitat de crear un model FE parametritzat d'una laminació composta en el nivell de cupó. Els resultats mostren que una arquitectura de Multilayer Perceptron amb dues hidden layers. La primera amb 281 nodes i la segona amb 76 nodes, és capaç de predir la rigidesa efectiva d'un laminat defectuós amb una precisió de 0,1 GPaEl uso del Automated Fiber Placement se está expandiendo en la industria aeroespacial. La necesidad de fabricar grandes y complejos componentes estructurales de materiales compuestos, hace que el uso de esta tecnología sea mucho más eficiente que la fabricación manual convencional. Sin embargo, estos componentes siguen siendo inspeccionados manualmente y se calcula el efecto de los defectos encontrados con un software de simulación. El objetivo de esta tesis es crear un modelo de Machine Learning que sea capaz de calcular el efecto sobre la rigidez efectiva para diferentes configuraciones de defectos. A este modelo de aprendizaje automático se le deben proporcionar las características geométricas del defecto en el laminado y tiene que ser capaz de predecir, con un alto nivel de precisión, la rigidez efectiva del laminado. El entrenamiento de este modelo se debe de realizar con una gran cantidad de configuraciones de defectos diferentes. Este hecho genera la necesidad de crear un modelo de elementos finitos parametrizado de un laminado a nivel de cupón. Los resultados muestran que una arquitectura Multilayer Perceptron con dos hidden layers. La primera con 281 nodos y la segunda con 76 nodos que es capaz de predecir la rigidez efectiva de un coupon laminado defectuoso con una precisión de 0,1 GP

Planejamento para missões autônomas persistentes cooperativas de longo prazo

Orientador: Andre Ricardo FioravantiDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Uma metodologia para abordar missões autônomas persistentes a longo prazo é apresentada juntamente com uma formalização geral do problema em hipóteses simples. É derivada uma realização dessa metodologia que reduz o problema geral para subproblemas de construção de caminho e de otimização combinatória, que são tratados com heurísticas para a computação de solução viável. Quatro estudos de caso são propostos e resolvidos com esta metodologia, mostrando que é possível obter caminhos contínuos ótimos ou subótimos aceitáveis a partir de ma representação discreta e elucidando algumas propriedades de solução nesses diferentes cenários, construindo bases para futuras escolhas educadas entre o uso de métodos exatos ou heurísticosAbstract: A Methodology for tackling Persistent Long Term Autonomous Missions is presented along with a general formalization of the problem upon simple assumptions. A realization of this methodology is derived which reduces the overall problem to a path construction and a combinatorial optimization subproblems, which are treated themselves with heuristics for feasible solution computation. Four case studies are proposed and solved with this methodology, showing that it is possible to obtain optimal or acceptable suboptimal continuous paths from a discrete representation, and elucidating some solution properties in these different scenarios, building bases for future educated choices between use of exact methods over heuristicsMestradoMecanica dos Sólidos e Projeto MecanicoMestre em Engenharia Mecânica1687532CAPE

Designing 3D scenarios and interaction tasks for immersive environments

In the world of today, immersive reality such as virtual and mixed reality, is one of the most attractive research fields. Virtual Reality, also called VR, has a huge potential to be used in in scientific and educational domains by providing users with real-time interaction or manipulation. The key concept in immersive technologies to provide a high level of immersive sensation to the user, which is one of the main challenges in this field. Wearable technologies play a key role to enhance the immersive sensation and the degree of embodiment in virtual and mixed reality interaction tasks. This project report presents an application study where the user interacts with virtual objects, such as grabbing objects, open or close doors and drawers while wearing a sensory cyberglove developed in our lab (Cyberglove-HT). Furthermore, it presents the development of a methodology that provides inertial measurement unit(IMU)-based gesture recognition. The interaction tasks and 3D immersive scenarios were designed in Unity 3D. Additionally, we developed an inertial sensor-based gesture recognition by employing an Long short-term memory (LSTM) network. In order to distinguish the effect of wearable technologies in the user experience in immersive environments, we made an experimental study comparing the Cyberglove-HT to standard VR controllers (HTC Vive Controller). The quantitive and subjective results indicate that we were able to enhance the immersive sensation and self embodiment with the Cyberglove-HT. A publication resulted from this work [1] which has been developed in the framework of the R&D project Human Tracking and Perception in Dynamic Immersive Rooms (HTPDI

3D shape matching and registration : a probabilistic perspective

Dense correspondence is a key area in computer vision and medical image analysis. It has applications in registration and shape analysis. In this thesis, we develop a technique to recover dense correspondences between the surfaces of neuroanatomical objects over heterogeneous populations of individuals. We recover dense correspondences based on 3D shape matching. In this thesis, the 3D shape matching problem is formulated under the framework of Markov Random Fields (MRFs). We represent the surfaces of neuroanatomical objects as genus zero voxel-based meshes. The surface meshes are projected into a Markov random field space. The projection carries both geometric and topological information in terms of Gaussian curvature and mesh neighbourhood from the original space to the random field space. Gaussian curvature is projected to the nodes of the MRF, and the mesh neighbourhood structure is projected to the edges. 3D shape matching between two surface meshes is then performed by solving an energy function minimisation problem formulated with MRFs. The outcome of the 3D shape matching is dense point-to-point correspondences. However, the minimisation of the energy function is NP hard. In this thesis, we use belief propagation to perform the probabilistic inference for 3D shape matching. A sparse update loopy belief propagation algorithm adapted to the 3D shape matching is proposed to obtain an approximate global solution for the 3D shape matching problem. The sparse update loopy belief propagation algorithm demonstrates significant efficiency gain compared to standard belief propagation. The computational complexity and convergence property analysis for the sparse update loopy belief propagation algorithm are also conducted in the thesis. We also investigate randomised algorithms to minimise the energy function. In order to enhance the shape matching rate and increase the inlier support set, we propose a novel clamping technique. The clamping technique is realized by combining the loopy belief propagation message updating rule with the feedback from 3D rigid body registration. By using this clamping technique, the correct shape matching rate is increased significantly. Finally, we investigate 3D shape registration techniques based on the 3D shape matching result. Based on the point-to-point dense correspondences obtained from the 3D shape matching, a three-point based transformation estimation technique is combined with the RANdom SAmple Consensus (RANSAC) algorithm to obtain the inlier support set. The global registration approach is purely dependent on point-wise correspondences between two meshed surfaces. It has the advantage that the need for orientation initialisation is eliminated and that all shapes of spherical topology. The comparison of our MRF based 3D registration approach with a state-of-the-art registration algorithm, the first order ellipsoid template, is conducted in the experiments. These show dense correspondence for pairs of hippocampi from two different data sets, each of around 20 60+ year old healthy individuals

Electro-Optical/Infrared sensor turret integration on an aircraft - structural im-pact on LOCKHEED MARTIN C-130H and design methodology

The growing need to patrol and survey large maritime and terrestrial areas increased the need to integrate external sensors on aircraft in order to accomplish those patrols at increasingly higher altitudes, longer range and not depending upon vehicle type. The main focus of this work is to elaborate a practical, simple, effective and efficient methodology for the aircraft modification procedure resulting from the integration of an Elec-tro-Optical/Infra-Red (EO/IR) turret through a support structure. The importance of the devel-opment of a good methodology relies on the correct management of project variables as time, available resources and project complexity. The key is to deliver a proper tool for a project de-sign team that will be used to create a solution that fulfils all technical, non-technical and certi-fication requirements present in this field of transportation. The created methodology is inde-pendent of two main inputs: sensor model and aircraft model definition, and therefore it is in-tended to deliver the results for different projects besides the one that was presented in this work as a case study. This particular case study presents the development of a structure support for FLIR STAR SAPHIRE III turret integration on the front lower fuselage bulkhead (radome) of the LOCKHEED MARTIN C-130 H. Development of the case study focuses on the study of local structural analysis through the use of Finite Element Method (FEM). Development of this Dissertation resulted in a cooperation between Faculty of Science and Technology - Universidade Nova de Lisboa and the company OGMA - Indústria Aeronáutica de Portuga

Computer-aided design techniques in data processing for finite element analysis

Imperial Users onl

Robust Multiscale Identification of Apparent Elastic Properties at Mesoscale for Random Heterogeneous Materials with Multiscale Field Measurements

The aim of this work is to efficiently and robustly solve the statistical inverse problem related to the identification of the elastic properties at both macroscopic and mesoscopic scales of heterogeneous anisotropic materials with a complex microstructure that usually cannot be properly described in terms of their mechanical constituents at microscale. Within the context of linear elasticity theory, the apparent elasticity tensor field at a given mesoscale is modeled by a prior non-Gaussian tensor-valued random field. A general methodology using multiscale displacement field measurements simultaneously made at both macroscale and mesoscale has been recently proposed for the identification the hyperparameters of such a prior stochastic model by solving a multiscale statistical inverse problem using a stochastic computational model and some information from displacement fields at both macroscale and mesoscale. This paper contributes to the improvement of the computational efficiency, accuracy and robustness of such a method by introducing (i) a mesoscopic numerical indicator related to the spatial correlation length(s) of kinematic fields, allowing the time-consuming global optimization algorithm (genetic algorithm) used in a previous work to be replaced with a more efficient algorithm and (ii) an ad hoc stochastic representation of the hyperparameters involved in the prior stochastic model in order to enhance both the robustness and the precision of the statistical inverse identification method. Finally, the proposed improved method is first validated on in silico materials within the framework of 2D plane stress and 3D linear elasticity (using multiscale simulated data obtained through numerical computations) and then exemplified on a real heterogeneous biological material (beef cortical bone) within the framework of 2D plane stress linear elasticity (using multiscale experimental data obtained through mechanical testing monitored by digital image correlation)

Multiscale Machine Learning and Numerical Investigation of Ageing in Infrastructures

Infrastructure is a critical component of a country’s economic growth. Interaction with extreme service environments can adversely affect the long-term performance of infrastructure and accelerate ageing. This research focuses on using machine learning to improve the efficiency of analysing the multiscale ageing impact on infrastructure. First, a data-driven campaign is developed to analyse the condition of an ageing infrastructure. A machine learning-based framework is proposed to predict the state of various assets across a railway system. The ageing of the bond in fibre-reinforced polymer (FRP)-strengthened concrete elements is investigated using machine learning. Different machine learning models are developed to characterise the long-term performance of the bond. The environmental ageing of composite materials is investigated by a micromechanics-based machine learning model. A mathematical framework is developed to automatically generate microstructures. The microstructures are analysed by the finite element (FE) method. The generated data is used to develop a machine learning model to study the degradation of the transverse performance of composites under humid conditions. Finally, a multiscale FE and machine learning framework is developed to expand the understanding of composite material ageing. A moisture diffusion analysis is performed to simulate the water uptake of composites under water immersion conditions. The results are downscaled to obtain micromodel stress fields. Numerical homogenisation is used to obtain the composite transverse behaviour. A machine learning model is developed based on the multiscale simulation results to model the ageing process of composites under water immersion. The frameworks developed in this thesis demonstrate how machine learning improves the analysis of ageing across multiple scales of infrastructure. The resulting understanding can help develop more efficient strategies for the rehabilitation of ageing infrastructure