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- Achtenhagen, Leona, et al.
(författare)
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Investigating Patterns of Development in SMEs
- 2006
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Ingår i: Paper presented at the Academy of Management Annual Meeting, Atlanta, Georgia, August 11-16, 2006.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 5. |
- Achtenhagen, Leona, et al.
(författare)
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Towards a re-conceptualization of firm growth
- 2004
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Ingår i: Frontiers of entrepreneurship research 2004. - Babson Park, Mass. : Arthur M. Blank Center for Entrepreneurship, Babson College. - 0910897255
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 8. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
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An approach towards generating surrogate models by using RBFN with a priori bias
- 2014
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Ingår i: Proceedings of the ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, 2014, Vol. 2B. - New York, USA : ASME Press. - 9780791846322
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Konferensbidrag (refereegranskat)abstract
- In this paper, an approach to generate surrogate modelsconstructed by radial basis function networks (RBFN) with a prioribias is presented. RBFN as a weighted combination of radialbasis functions only, might become singular and no interpolationis found. The standard approach to avoid this is to add a polynomialbias, where the bias is defined by imposing orthogonalityconditions between the weights of the radial basis functionsand the polynomial basis functions. Here, in the proposed a prioriapproach, the regression coefficients of the polynomial biasare simply calculated by using the normal equation without anyneed of the extra orthogonality prerequisite. In addition to thesimplicity of this approach, the method has also proven to predictthe actual functions more accurately compared to the RBFNwith a posteriori bias. Several test functions, including Rosenbrock,Branin-Hoo, Goldstein-Price functions and two mathematicalfunctions (one large scale), are used to evaluate the performanceof the proposed method by conducting a comparisonstudy and error analysis between the RBFN with a priori and aposteriori known biases. Furthermore, the aforementioned approachesare applied to an engineering design problem, that ismodeling of the material properties of a three phase sphericalgraphite iron (SGI) . The corresponding surrogate models arepresented and compared
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| 9. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
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Multi-objective optimization of a disc brake system by using SPEA2 and RBFN
- 2013
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Ingår i: ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. - New York : ASME Press. - 9780791855898
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Many engineering design optimization problems involve multiple conflicting objectives, which today often are obtained by computational expensive finite element simulations. Evolutionary multi-objective optimization (EMO) methods based on surrogate modeling is one approach of solving this class of problems. In this paper, multi-objective optimization of a disc brake system to a heavy truck by using EMO and radial basis function networks (RBFN) is presented. Three conflicting objectives are considered. These are: 1) minimizing the maximum temperature of the disc brake, 2) maximizing the brake energy of the system and 3) minimizing the mass of the back plate of the brake pad. An iterative Latin hypercube sampling method is used to construct the design of experiments (DoE) for the design variables. Next, thermo-mechanical finite element analysis of the disc brake, including frictional heating between the pad and the disc, is performed in order to determine the values of the first two objectives for the DoE. Surrogate models for the maximum temperature and the brake energy are created using RBFN with polynomial biases. Different radial basis functions are compared using statistical errors and cross validation errors (PRESS) to evaluate the accuracy of the surrogate models and to select the most accurate radial basis function. The multi-objective optimization problem is then solved by employing EMO using the strength Pareto evolutionary algorithm (SPEA2). Finally, the Pareto fronts generated by the proposed methodology are presented and discussed.
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| 10. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
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Multi-objective optimization of material model parameters of an adhesive layer by using SPEA2
- 2015
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Ingår i: Advances in structural and multidisciplinary optimization. - : The International Society for Structural and Multidisciplinary Optimization (ISSMO). - 9780646943947 ; , s. 249-254
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Konferensbidrag (refereegranskat)abstract
- The usage of multi material structures in industry, especially in the automotive industry are increasing. To overcome the difficulties in joining these structures, adhesives have several benefits over traditional joining methods. Therefore, accurate simulations of the entire process of fracture including the adhesive layer is crucial. In this paper, material parameters of a previously developed meso mechanical finite element (FE) model of a thin adhesive layer are optimized using the Strength Pareto Evolutionary Algorithm (SPEA2). Objective functions are defined as the error between experimental data and simulation data. The experimental data is provided by previously performed experiments where an adhesive layer was loaded in monotonically increasing peel and shear. Two objective functions are dependent on 9 model parameters (decision variables) in total and are evaluated by running two FEsimulations, one is loading the adhesive layer in peel and the other in shear. The original study converted the two objective functions into one function that resulted in one optimal solution. In this study, however, a Pareto frontis obtained by employing the SPEA2 algorithm. Thus, more insight into the material model, objective functions, optimal solutions and decision space is acquired using the Pareto front. We compare the results and show good agreement with the experimental data.
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