EngiO – Object-oriented framework for engineering optimization

Berger, Ricarda; Bruns, Marlene; Ehrmann, Andreas; Haldar, A.; Häfele, Jan; Hofmeister, Benedikt; Hübler, Clemens; Rolfes, Raimund

doi:10.1016/j.advengsoft.2020.102959

Cited by 13 publications

(8 citation statements)

References 31 publications

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“…[75] Harmony search (HS) [76] Compared with classic gradient-based methods, a random step size within calculusbased numerical iteration is introduced in stochastic and meta-heuristic approaches; in many cases, no initial guess value is required by algorithms in this category (due to random initialization). There are many types of evolutionary-based algorithms, commonly known as global optimisation methods, which are suitable and convenient for finding the optimum of an engineering optimisation problem [8]. Essentially, evolutionary algorithms use the "survival of the fittest" principle, which is adopted to a population of elements (candidate solutions) [66,77].…”

Section: Evolution Strategy (Es)mentioning

confidence: 99%

“…As research activities are aimed at finding an alternative with the best properties, engineers and researchers eventually enter the field of engineering optimization based on a mathematical approach, the field of optimal control [6,7]. In engineering practice, it is sometimes common that the optimization goal-mathematically defined by the objective function-can be formulated intuitively when taking into account technical or economic requirements [8,9] and, based on experience, subsequently to achieve a system with better properties [10,11]. However, when applying the scientific approach to solve a real engineering optimization problem, one is then confronted with the mathematical formulation of the optimization problem (FOP) [12], with a countless number of optimization methods and algorithms (OM) [13,14], as well as with a wide range of optimization software (OS) [8,15,16].…”

Section: Introductionmentioning

confidence: 99%

“…In engineering practice, it is sometimes common that the optimization goal-mathematically defined by the objective function-can be formulated intuitively when taking into account technical or economic requirements [8,9] and, based on experience, subsequently to achieve a system with better properties [10,11]. However, when applying the scientific approach to solve a real engineering optimization problem, one is then confronted with the mathematical formulation of the optimization problem (FOP) [12], with a countless number of optimization methods and algorithms (OM) [13,14], as well as with a wide range of optimization software (OS) [8,15,16]. As there is no universal optimization algorithm suitable for solving any optimization problem [17], it is important for engineers, as newcomers in the optimization field, to be able and competent in these three areas of scientific interest (FOP, OM, and OS).…”

Section: Introductionmentioning

confidence: 99%

“…To simplify the search for the optimal solution not only for newcomers, researchers are developing optimization frameworks that are appropriate and addressed for solving a specific problem [8]. So far, a large number of optimization frameworks or libraries have been published that facilitate the solution of real optimization problems in engineering practice.…”

Section: Introductionmentioning

confidence: 99%

“…Each of these frameworks targets a different community, so a wide range of programming languages are used [18,19]. As stated in [8], frameworks are programmed in a range from low-level languages such as C and C++, to higher scripting languages such as Python (e.g., OpenMDAO framework), Java (jMetal), and so on. There are numerous engineering optimization problems in which problem specificities such as linearity, convexity, or differentiability of objective functions and constraints are either non-existent, unknown, or un-exploitable; therefore, the scientific community has focused on derivative-free optimization algorithms in the past two decades [20].…”

Section: Introductionmentioning

confidence: 99%

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Selected Mathematical Optimization Methods for Solving Problems of Engineering Practice

2022

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Engineering optimization is the subject of interest for many scientific research teams on a global scale; it is a part of today’s mathematical modelling and control of processes and systems. The attention in this article is focused on optimization modelling of technological processes of surface treatment. To date, a multitude of articles are devoted to the applications of mathematical optimization methods to control technological processes, but the situation is different for surface treatment processes, especially for anodizing. We perceive their lack more, so this state has stimulated our interest, and the article contributes to filling the gap in scientific research in this area. The article deals with the application of non-linear programming (NLP) methods to optimise the process of anodic oxidation of aluminium using MATLAB toolboxes. The implementation of optimization methods is illustrated by solving a specific problem from engineering practice. The novelty of this article lies in the selection of effective approaches to the statement of optimal process conditions for anodizing. To solve this complex problem, a solving strategy based on the design of experiments approach (for five factors), exploratory data analysis, confirmatory analysis, and optimization modelling is proposed. The original results have been obtained through the experiment (performed by using the DOE approach), statistical analysis, and optimization procedure. The main contribution of this study is the developed mathematical-statistical computational (MSC) model predicting the thickness of the resulting aluminium anodic oxide layer (AOL). Based on the MSC model, the main goal has been achieved—the statement of optimal values of factors acting during the anodizing process to achieve the thickness of the protective layer required by clients, namely, for 5, 7, 10, and 15 [μm].

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Section: Evolution Strategy (Es)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Selected Mathematical Optimization Methods for Solving Problems of Engineering Practice

2022

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Validation of an FE model updating procedure for damage assessment using a modular laboratory experiment with a reversible damage mechanism

Wolniak

Hofmeister

Jonscher

et al. 2023

J Civil Struct Health Monit

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In this work, the systematic validation of a deterministic finite element (FE) model updating procedure for damage assessment is presented using a self-developed modular laboratory experiment. A fundamental, systematic validation of damage assessment methods is rarely conducted and in many experimental investigations, only one type of defect is introduced at only one position. Often, the damage inserted is irreversible and inspections are only performed visually. Thus, the damage introduced and, with it, the results of the damage assessment method considered are often not entirely analyzed in terms of quantity and quality. To address this shortcoming, a modular steel cantilever beam is designed with nine reversible damage positions and the option to insert different damage scenarios in a controlled manner. The measurement data are made available in open-access form which enables a systematic experimental validation of damage assessment methods. To demonstrate such a systematic validation using the modular laboratory experiment, a deterministic FE model updating procedure previously introduced by the authors is applied and extended. The FE model updating approach uses different parameterized damage distribution functions to update the stiffness properties of the structure considered. The mathematical formulation allows for an updating procedure that is independent of the FE mesh resolution and free of assumptions about the defect location while only needing few design variables. In this work, the FE model updating procedure is based only on eigenfrequency deviations. The results show a precise localization within $$\pm \, {0.05}{\textrm{m}}$$ ± 0.05 m of the nine different damage positions and a correct relative quantification of the three different damage scenarios considered. With that, first, it is shown that the deterministic FE model updating procedure presented is suitable for precise damage assessment. Second, this work demonstrates that the opportunity to introduce several reversible damage positions and distinctly defined types and severities of damage into the laboratory experiment presented generally enables the systematic experimental validation of damage assessment methods.

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Efficient and user-friendly α-level optimisation for application-orientated fuzzy structural analyses

Hübler

Hofmeister

2021

Engineering Structures

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EngiO – Object-oriented framework for engineering optimization

Cited by 13 publications

References 31 publications

Selected Mathematical Optimization Methods for Solving Problems of Engineering Practice

Selected Mathematical Optimization Methods for Solving Problems of Engineering Practice

Validation of an FE model updating procedure for damage assessment using a modular laboratory experiment with a reversible damage mechanism

Efficient and user-friendly α-level optimisation for application-orientated fuzzy structural analyses

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