A Hybrid Design Optimization Method using Enriched Craig-Bampton Approach

A hybrid design optimization method is presented which combines a number of techniques such as Component Mode Synthesis (CMS), Design of Computer Experiments and Neural Networks for surrogate modeling with Genetic Algorithms and Sequential Quadratic Programming for optimization. In the method, the FE analysis is decomposed and reduced by a well-known CMS technique called the Craig-Bampton method. Since the optimization method requires CMS calculations of the updated model at each of its iterations due to the changes in the design variables, one can either reuse the reduction basis of the initial components or compute new reduction basis for the condensation of the system matrices. The first option usually leads to inaccurate results and the last one increases the omputation time. In the method, instead of using one of these options, the Enriched Craig-Bampton method, proposed by Masson et al., is employed for efficient optimization. New basis for the modified components are generated by extending the corresponding initial reduction basis with a set of static residual vectors which are calculated using prior knowledge of the initial component designs. Thus, time consuming complete component analyzes are prevented. A theoretical test problem is used for the demonstration of the method

On a novel approach for optimizing composite materials panel using surrogate models

This paper describes an optimization procedure to design thermoplastic composite panels under axial compressive load conditions. Minimum weight is the goal. The panel design is subject to buckling constraints. The presence of the bending-twisting coupling and of particular boundary conditions does not allow an analytical solution for the critical buckling load. Surrogate models are used to approximate the buckling response of the plate in a fast and reliable way. Therefore, two surrogate models are compared to study their effectiveness in composite optimization. The first one is a linear approximation based on the buckling constitutive equation. The second consists in the application of the Kriging surrogate. Constraints given from practical blending rules are also introduced in the optimization. Discrete values of ply thicknesses is a requirement. An ad-hoc discrete optimization strategy is developed, which enables to handle discrete variables

An optimization method for dynamics of structures with repetitive component patterns

The occurrence of dynamic problems during the operation of machinery may have devastating effects on a product. Therefore, design optimization of these products becomes essential in order to meet safety criteria. In this research, a hybrid design optimization method is proposed where attention is focused on structures having repeating patterns in their geometries. In the proposed method, the analysis is decomposed but the optimization problem itself is treated as a whole. The model of an entire structure is obtained without modeling all the repetitive components using the merits of the Component Mode Synthesis method. Backpropagation Neural Networks are used for surrogate modeling. The optimization is performed using two techniques: Genetic Algorithms (GAs) and Sequential Quadratic Programming (SQP). GAs are utilized to increase the chance of finding the location of the global optimum and since this optimum may not be exact, SQP is employed afterwards to improve the solution. A theoretical test problem is used to demonstrate the method

Design Optimization Utilizing Dynamic Substructuring and Artificial Intelligence Techniques

In mechanical and structural systems, resonance may cause large strains and stresses which can lead to the failure of the system. Since it is often not possible to change the frequency content of the external load excitation, the phenomenon can only be avoided by updating the design of the structure. In this paper, a design optimization strategy based on the integration of the Component Mode Synthesis (CMS) method with numerical optimization techniques is presented. For reasons of numerical efficiency, a Finite Element (FE) model is represented by a surrogate model which is a function of the design parameters. The surrogate model is obtained in four steps: First, the reduced FE models of the components are derived using the CMS method. Then the components are aassembled to obtain the entire structural response. Afterwards the dynamic behavior is determined for a number of design parameter settings. Finally, the surrogate model representing the dynamic behavior is obtained. In this research, the surrogate model is determined using the Backpropagation Neural Networks which is then optimized using the Genetic Algorithms and Sequential Quadratic Programming method. The application of the introduced techniques is demonstrated on a simple test problem

On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening

Here we explore the use of various feature point descriptors as visual word variants within a Bag-of-Visual-Words (BoVW) representation scheme for image classification based threat detection within baggage security X-ray imagery. Using a classical BoVW model with a range of feature point detectors and descriptors, supported by both Support Vector Machine (SVM) and Random Forest classification, we illustrate the current performance capability of approaches following this image classification paradigm over a large X-ray baggage imagery data set. An optimal statistical accuracy of 0.94 (true positive: 83%; false positive: 3.3%) is achieved using a FAST-SURF feature detector and descriptor combination for a firearms detection task. Our results indicate comparative levels of performance for BoVW based approaches for this task over extensive variations in feature detector, feature descriptor, vocabulary size and final classification approach. We further demonstrate a by-product of such approaches in using feature point density as a simple measure of image complexity available as an integral part of the overall classification pipeline. The performance achieved characterises the potential for BoVW based approaches for threat object detection within the future automation of X-ray security screening against other contemporary approaches in the field