Karim Hamza

Optimal synthesis of 2D phononic crystals for broadband frequency isolation

The spatial distribution of material phases within a periodic composite can be engineered to produce band gaps in its frequency spectrum. Applications for such composite materials include vibration and sound isolation. Previous research focused on utilizing topology optimization techniques to design two-dimensional (2D) periodic materials with a maximized band gap around a particular frequency or between two particular dispersion branches. While sizable band gaps can be realized, the possibility remains that the frequency bandwidth of the load that is to be isolated might exceed the size of the band gap. In this paper, genetic algorithms are used to design squared bi-material unit cells with a maximized sum of band-gap widths, with or without normalization relative to the central frequency of each band gap, over a prescribed total frequency range of interest. The optimized unit cells therefore exhibit broadband frequency isolation characteristics. The effects of the ratios of contrasting material properties are also studied. The designed cells are subsequently used, with varying levels of material damping, to form a finite vibration isolation structure, which is subjected to broadband loading conditions. Excellent isolation properties of the synthesized material are demonstrated for this structure.

Design optimization of N-shaped roof trusses using reactive taboo search

Abstract Design optimization of a class of plane trusses called the N-shaped truss (NST) is addressed. The parametric model of NST presented is intended for real-world application, avoiding simplifications of the design details that compromise the applicability. The model, which includes 27 discrete variables concerning topology, configuration and sizing of the truss, presents a challenging optimization problem. Aspects of such challenge include large search space dimensionality, absence of a closed-form objective function (OF) and constraints, multimodal objective function and costly CPU time per objective function evaluation. Three implementations of general-purpose genetic algorithms (GAs) are tested for this problem, along with a version of taboo search called reactive taboo search (RTS). In this study, the raw version of RTS exhibited better performance than the tested versions of GA but lacks some of the GA capabilities to span the search space. A modification of RTS that uses a population-based exploitation of the search history is proposed. The optimization results show that the introduced modification can further improve the performance of RTS.

Optimum drilling path planning for a rectangular matrix of holes using ant colony optimisation

This paper investigates optimum path planning for CNC drilling machines for a special class of products that involve a large number of holes arranged in a rectangular matrix. Examples of such products include boiler plates, drum and trammel screens, connection flanges in steel structures, food-processing separators, as well as certain portions of printed circuit boards. While most commercial CAD software packages include modules that allow for automated generation of the CNC code, the tool path planning generated from the commercial CAD software is often not fully optimised in terms of the tool travel distance, and ultimately, the total machining time. This is mainly due to the fact that minimisation of the tool travel distance is a travelling salesman problem (TSP). The TSP is a hard problem in the discrete programming context with no known general solution that can be obtained in polynomial time. Several heuristic optimisation algorithms have been applied in the literature to the TSP, with varying levels of success. Among the most successful algorithms for TSP is the ant colony optimisation (ACO) algorithm, which mimics the behaviour of ants in nature. The research in this paper applies the ACO algorithm to the path planning of a CNC drilling tool between holes in a rectangular matrix. In order to take advantage of the rectangular layout of the holes, two modifications to the basic ACO algorithm are proposed. Simulation case studies show that the average discovered path via the modified ACO algorithms exhibit significant reduction in the total tool travel distance compared to the basic ACO algorithm or a typical genetic algorithm.

VEHICLE CRASHWORTHINESS DESIGN VIA A SURROGATE MODEL ENSEMBLE AND A CO-EVOLUTIONARY GENETIC ALGORITHM

This paper presents a new method for designing vehicle structures for crashworthiness using surrogate models and a genetic algorithm. Inspired by the classifier ensemble approaches in pattern recognition, the method estimates the crash performance of a candidate design based on an ensemble of surrogate models constructed from the different sets of samples of finite element analyses. Multiple sub-populations of candidate designs are evolved, in a co-evolutionary fashion, to minimize the different aggregates of the outputs of the surrogate models in the ensemble, as well as the raw output of each surrogate. With the same sample size of finite element analyses, it is expected the method can provide wider ranges potentially high-performance designs than the conventional methods that employ a single surrogate model, by effectively compensating the errors associated with individual surrogate models. Two case studies on simplified and full vehicle models subject to full-overlap frontal crash conditions are presented for demonstration.Copyright

A framework for parallelized efficient global optimization with application to vehicle crashworthiness optimization

This article presents a framework for simulation-based design optimization of computationally expensive problems, where economizing the generation of sample designs is highly desirable. One popular approach for such problems is efficient global optimization (EGO), where an initial set of design samples is used to construct a kriging model, which is then used to generate new ‘infill’ sample designs at regions of the search space where there is high expectancy of improvement. This article attempts to address one of the limitations of EGO, where generation of infill samples can become a difficult optimization problem in its own right, as well as allow the generation of multiple samples at a time in order to take advantage of parallel computing in the evaluation of the new samples. The proposed approach is tested on analytical functions, and then applied to the vehicle crashworthiness design of a full Geo Metro model undergoing frontal crash conditions.

A Co-Evolutionary Approach for Design Optimization via Ensembles of Surrogates With Application to Vehicle Crashworthiness

In many engineering application, where accurate models require lengthy numerical computations, it is a common design practice to perform design of experiments (DOE) and construct surrogate models that provide computationally-inexpensive approximations. Main challenges to that approach are (i) construction of high-fidelity surrogates and (ii) discovery of high performance designs despite the fidelity limitations. An ensemble of surrogates (EOS) is a collection of different surrogates approximating the same process (typically with some form of weighted averaging to get an overall approximation) and has been demonstrated in the literature to often exhibit better performance than any of the individual surrogates. This paper presents a Multi-Scenario Co-evolutionary Genetic Algorithm (MSCGA) for design optimization via EOS. MSCGA simultaneously evolves multiple populations in a multi-objective sense via the predicted performance by the different surrogates within the ensemble. The outputs of the algorithm are solution sets including several designs that are spread over Pareto-optimal space of best-predictions by the surrogates within EOS, as well as best designs as predicted by individual surrogates and the weighted average of the EOS. Studies using analytical test functions show MSCGA to be more likely to discover better performing designs than an individual surrogate or a weighted ensemble. The primary application for MSCGA presented in this paper is that of vehicle structural crashworthiness since it is a typical design application that requires massive computational resources for accurate modeling. Two studies, which include simplified and detailed vehicle models, MSCGA successfully discovers new high performance designs.

Optimal outsourcing for intellectual property protection and production cost minimization

This paper presents a methodology for optimal outsourcing of products. Outsourcing of products can have the advantage of reducing the production cost, but often causes a risk that important technology may leak and get used by competitors. To help reduce the risk of intellectual property (IP) leakage, a model proposed in this paper assumes that it is possible to separate some of the important geometrical features on some of the product parts that are outsourced, and then manufacture them in-house. The model estimates the fraction of IP-value that is subject to risk of leakage based on patent claims and how they relate to the outsourced parts and/or features. Production cost is modelled by assuming a base cost for manufacturing parts in-house, and then a discount rate is applied if the decision to outsource is made. Separation of geometrical features from manufactured parts introduces additional cost, which is modelled as an overhead if the decision to separate features is made. The outsourcing management process is then viewed as a two-objective problem, with the objectives being the minimization of both the fraction of IP-value at risk of leakage, as well as the production cost. A case study of an auto-slide-hinge mechanism is presented, in which the two-objective optimization problem is transformed into a single-objective constrained problem. Genetic algorithm is then applied iteratively on the problem in order generate the Pareto-plot that visualizes the trade-offs between the two objectives.

DESIGN FOR CRASHWORTHINESS OF VEHICLE STRUCTURES VIA EQUIVALENT MECHANISM APPROXIMATIONS AND CRUSH MODE MATCHING

This paper presents a 3D extension to our previous work on vehicle crashworthiness design that utilizes “equivalent” mechanism models of vehicle structures as a tool for the early design exploration. An equivalent mechanism (EM) is a network of rigid links with lumped masses connected by prismatic and revolute joints with nonlinear springs, which approximate aggregated behaviors of structural members during crush. A number of finite element (FE) models of thinwalled beams with typical cross sections and wall thicknesses are analyzed to build a surrogate model that maps the beam dimensions to nonlinear spring properties. Using the surrogate model, an EM model is optimized for given design objectives by selecting the nonlinear springs among the ones realizable by thin-walled beams. The optimum EM model serves to identify a good crash mode (CM), the time history of collapse of the structural members, and to suggest the dimensions of the structural members to attain it. After the optimization, the FE model of an entire structure is “assembled” from the suggested dimensions, which is further modified to attain the good CM identified by the optimum EM model. A case study of a 3D vehicle front half body demonstrates that the proposed approach can help obtain good designs with far less computational resources than the direct optimization of a FE model.

Design Optimization of Vehicle Structures for Crashworthiness Using Equivalent Mechanism Approximations

Passenger vehicle crashworthiness is one of the essential vehicle attributes. According to National Highway Traffic Safety Administration (NHTSA), there were over six million vehicle crashes in the United States in the year 2000, which claimed the lives of more than forty thousand persons. Vehicle crashworthiness is difficult to satisfy in a manner appeasing to other design decisions about the vehicle. This paper aims at developing a novel methodology for crashworthiness optimization of vehicle structures. Based on observations of the manner of structural deformation, the authors propose the abstraction of the actual vehicle structure, which is to be represented as a linkage mechanism having special nonlinear springs at the joints. The special springs are chosen to allow the motion of the mechanism to capture the overall motion of the actual vehicle structure. It thus becomes possible to optimize the mechanism, which is an easier task than directly optimizing the vehicle structure. A realization of the optimized mechanism is then performed to obtain an equivalent structure, and then direct optimization of the realized structure is performed for further tuning. The study presented shows the success of the proposed approach in finding better designs than direct optimization while using comparatively less computational resources.Copyright

Multiobjective Optimization of Turning Cutting Parameters for J-Steel Material

This paper presents a multiobjective optimization study of cutting parameters in turning operation for a heat-treated alloy steel material (J-Steel) with Vickers hardness in the range of HV 365–395 using uncoated, unlubricated Tungsten-Carbide tools. The primary aim is to identify proper settings of the cutting parameters (cutting speed, feed rate, and depth of cut) that lead to reasonable compromises between good surface quality and high material removal rate. Thorough exploration of the range of cutting parameters was conducted via a five-level full-factorial experimental matrix of samples and the Pareto trade-off frontier is identified. The trade-off among the objectives was observed to have a “knee” shape, in which certain settings for the cutting parameters can achieve both good surface quality and high material removal rate within certain limits. However, improving one of the objectives beyond these limits can only happen at the expense of a large compromise in the other objective. An alternative approach for identifying the trade-off frontier was also tested via multiobjective implementation of the Efficient Global Optimization (m-EGO) algorithm. The m-EGO algorithm was successful in identifying two points within the good range of the trade-off frontier with 36% fewer experimental samples.