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Designing experiments to identify improvement in products that are assembled from manufactured components does not readily fit into conventional design of experiments methods and can be costly. Ecient methods are explored for determining designs for engineering problems where some, or all, of the factors of interest are (a) not easily set to prescr...
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... experiment was planned to investigate the sound output from an electro-acoustic transducer manufactured by Hosiden Besson Ltd. and illustrated in Figure 2. In this device an oscillating input electrical signal is fed to the bobbin windings which causes the armature to rock on the pivot created by the pip. ...
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Fourier properties are used as the guiding principle for designing quasi-random nanostructures for light trapping in photovoltaics. This rule is used to determine the minimum required feature sizes and to speed up e-beam fabrication.
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... It should be noted that the tuning issue is not unique to the construction of a Pareto front, but is a general issue with genetic algorithms (Limmun et al. 2013). Previous work in the exper-imental design community (Borkowski 2003;Sexton et al. 2006) suggests augmenting the genetic algorithm with a local grid search on the generated solutions. While this suggestion may be practical for some situations in single-objective design optimization, its implementation for populating the Pareto front would result in a great deal of computational inefficiency. ...
This paper presents a new Pareto-based coordinate exchange algorithm for populating or approximating the true Pareto front for multi-criteria optimal experimental design problems that arise naturally in a range of industrial applications. This heuristic combines an elitist-like operator inspired by evolutionary multi-objective optimization algorithms with a coordinate exchange operator that is commonly used to construct optimal designs. Benchmarking results from both a two-dimensional and three-dimensional example demonstrate that the proposed hybrid algorithm can generate highly reliable Pareto fronts with less computational effort than existing procedures in the statistics literature. The proposed algorithm also utilizes a multi-start operator, which makes it readily parallelizable for high performance computing infrastructures.
... A different approach for efficiently designing experimental strategies is to adopt an evolutionary paradigm, [22,23,24,33,25,26]. This approach is fundamentally led by an optimization process that iteratively evolves the initial design toward some optimal regions of the experimental space or until a certain convergence criterion is satisfied. ...
This paper deals with optimal experimental design criteria and neural networks in the aim of building experimental designs from observational data. It addresses the following three main issues: (i) the introduction of two radically different approaches, namely T-optimal designs extended to Generalized Linear Models and Evolutionary Neural Networks Design; (ii) the proposal of two algorithms, based on model selection procedures, to exploit the information of already collected data; and (iii) the comparison of the suggested methods and corresponding algorithms by means of a simulated case study in the technological field.Results are compared by considering elements of the proposed algorithms, in terms of models and experimental design strategies. In particular, we highlight the algorithmic features, the performances of the approaches, the optimal solutions and the optimal levels of variables involved in a simulated foaming process. The optimal solutions obtained by the two proposed algorithms are very similar, nevertheless, the differences between the paths followed by the two algorithms to reach optimal values are substantial, as detailed step-by-step in the discussion. Copyright © 2012 John Wiley & Sons, Ltd.
This work explores the application of genetic algorithms to solve multi-objective optimization in building construction management from the aspect of Artificial Intelligence. Firstly, a comprehensive description of the four types of parameter coding methods and genetic operations is given. Then, the characteristics of various types of crossover and variation operators are elaborated and their applicability to the four types of parameter coding is analyzed. The results show that the floating-point coding method can improve the efficiency of dealing with multi-objective optimal management problems in building construction.
Ensuring high product quality is an important success factor in modern industry. Data-driven models are increasingly used for this purpose and need to be integrated into industrial processes as early as possible. As these models require high-quality training data, strategies for obtaining such data in the commissioning phase of a process are needed. Design of experiments (DoE) methods can be used for this task, but are not directly applicable for all types of processes. A common class of processes, to which most DoE methods cannot be applied, are assembly processes. The approach described in this paper aims to gather as much information as possible from an assembly process with minimal effort. It makes use of given sets of components from which optimal combinations are created, in order to achieve a space-filling design. Further design improvements are achieved by additionally specifying different mounting position of parts in the design and by using a subset selection procedure to remove less informative design points. The approach was successfully applied to an assembly process of washing machine drums to predict the radial deviation of assembled drums.
KeywordsDesign of experimentsOptimal designQuality predictionAssembly processes
Designed experiments are powerful tools when developing new products or processes. They allow changing the settings in a systematic way such that a minimal experimental effort results in maximal information. In the food industry, performing designed experiments can be challenging because products and processes are often complex and involve many factors of different kinds. Moreover, some of the factors (for instance, the ingredients of a new formulation) may have intrinsic properties that can be measured but cannot be changed. These are referred to as pre-specified factors or covariates. In this paper, we discuss a representative case study and show how these complications often encountered in the food industry are handled. The goal of the case study is to lower the lipid content of potato crisps by means of a split-plot type of designed experiment involving a complex mixture of ingredients, various constraints on the proportions of the mixture ingredients, and a limited number of batches that each come with a set of covariates. We show how our approach provides insight into the most important factors and allows the product quality to be optimized in an efficient way.
Genetic algorithms (GAs) have been used in many disciplines to optimize solutions for a broad range of problems. In the last 20 years, the statistical literature has seen an increase in the use and study of this optimization algorithm for generating optimal designs in a diverse set of experimental settings. These efforts are due in part to an interest in implementing a novel methodology as well as the hope that careful application of elements of the GA framework to the unique aspects of a designed experiment problem might lead to an efficient means of finding improved or optimal designs. In this paper, we explore the merits of using this approach, some of the aspects of design that make it a unique application relative to other optimization scenarios, and discuss elements which should be considered for an effective implementation. We conclude that the current GA implementations can, but do not always, provide a competitive methodology to produce substantial gains over standard optimal design strategies. We consider both the probability of finding a globally optimal design as well as the computational efficiency of this approach. Copyright © 2014 John Wiley & Sons, Ltd.
Designed experiments allow researchers to explore and understand the relation between sets of experimental factors and one or more responses. However, there are many cases where the experimenter cannot determine the factor levels freely. In this article, we study a baking experiment where the interest is in understanding the relationship between the characteristics of flour and the bread quality. The experimental factors are six mixture components and one observable process variable, so that the baking experiment is a mixture-process variable (MPV) experiment. The starting point for the study is an available stock of 30 flour samples for which the mixture component proportions and the level of the process variable have been determined using an expensive chemical analysis. The challenge for the experimenter is to determine which flour samples, or which combinations of flour samples, should be used in the actual experiment. Since the design region is highly constrained and the usable factor levels are dictated by the set of 30 initial flour samples, classical MPV designs are not applicable and an optimal design approach is required. We compare two different design strategies, one based on a point-exchange algorithm and another based on a coordinate-exchange algorithm. The coordinate-exchange algorithm is the most efficient one since it is not bounded by a given candidate set of points. However, it requires the calculation of the convex hull of the set of 30 flour samples along with the related constraints. Because of the high dimensionality of the problem, determining the convex hull and using it to create the constraints for the experimental design is a tedious process. In contrast, the point-exchange algorithm starts from a candidate set of possible factor level combinations, a limitation that lowers the efficiency of the design but results in a design that is very easy to implement.
