| 1. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
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A framework for simulation-based multi-objective optimization and knowledge discovery of machining process
- 2018
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Ingår i: The International Journal of Advanced Manufacturing Technology. - : Springer Science and Business Media LLC. - 0268-3768 .- 1433-3015. ; 98:9-12, s. 2469-2486
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Tidskriftsartikel (refereegranskat)abstract
- The current study presents an effective framework for automated multi-objective optimization (MOO) of machining processes by using finite element (FE) simulations. The framework is demonstrated by optimizing a metal cutting process in turning AISI-1045, using an uncoated K10 tungsten carbide tool. The aim of the MOO is to minimize tool-chip interface temperature and tool wear depth, that are extracted from FE simulations, while maximizing the material removal rate. The effect of tool geometry parameters, i.e., clearance angle, rake angle, and cutting edge radius, and process parameters, i.e., cutting speed and feed rate on the objective functions are explored. Strength Pareto Evolutionary Algorithm (SPEA2) is adopted for the study. The framework integrates and connects several modules to completely automate the entire MOO process. The capability of performing the MOO in parallel is also enabled by adopting the framework. Basically, automation and parallel computing, accounts for the practicality of MOO by using FE simulations. The trade-off solutions obtained by MOO are presented. A knowledge discovery study is carried out on the trade-off solutions. The non-dominated solutions are analyzed using a recently proposed data mining technique to gain a deeper understanding of the turning process.
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| 2. |
- 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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| 3. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
-
Metamodel based multi-objective optimization of a turning process by using finite element simulation
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This study investigates the advantages and potentials of the metamodelbased multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature and tool wear depth obtained from FE simulations using DEFORM2D software, and maximizing the material removal rate. Tool geometry and process parameters are considered as the input variables. Seven metamodelling methods are employed and evaluated, based on accuracy and suitability. Radial basis functions with a priori bias and Kriging are chosen to model tool–chip interface temperature and tool wear depth, respectively. The non-dominated solutions are found using the strength Pareto evolutionary algorithm SPEA2 and compared with the non-dominated front obtained from pure simulation-based MOO. The metamodel-based MOO method is not only advantageous in terms of reducing the computational time by 70%, but is also able to discover 31 new non-dominated solutions over simulation-based MOO.
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| 4. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
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Metamodel based multi-objective optimization of a turning process by using finite element simulation
- 2020
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Ingår i: Engineering optimization (Print). - : Taylor & Francis Group. - 0305-215X .- 1029-0273. ; 52:7, s. 1261-1278
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Tidskriftsartikel (refereegranskat)abstract
- This study investigates the advantages and potentials of the metamodelbased multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature and tool wear depth obtained from FE simulations using DEFORM2D software, and maximizing the material removal rate. Tool geometry and process parameters are considered as the input variables. Seven metamodelling methods are employed and evaluated, based on accuracy and suitability. Radial basis functions with a priori bias and Kriging are chosen to model tool–chip interface temperature and tool wear depth, respectively. The non-dominated solutions are found using the strength Pareto evolutionary algorithm SPEA2 and compared with the non-dominated front obtained from pure simulation-based MOO. The metamodel-based MOO method is not only advantageous in terms of reducing the computational time by 70%, but is also able to discover 31 new non-dominated solutions over simulation-based MOO.
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| 5. |
- Amouzgar, Kaveh, 1980-
(författare)
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Metamodel Based Multi-Objective Optimization with Finite-Element Applications
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As a result of the increase in accessibility of computational resources and the increase of computer power during the last two decades, designers are able to create computer models to simulate the behavior of complex products. To address global competitiveness, companies are forced to optimize the design of their products and production processes. Optimizing the design and production very often need several runs of computationally expensive simulation models. Therefore, integrating metamodels, as an efficient and sufficiently accurate approximate of the simulation model, with optimization algorithms is necessary. Furthermore, in most of engineering problems, more than one objective function has to be optimized, leading to multi-objective optimization(MOO). However, the urge to employ metamodels in MOO, i.e., metamodel based MOO (MB-MOO), is more substantial.Radial basis functions (RBF) is one of the most popular metamodeling methods. In this thesis, a new approach to constructing RBF with the bias to beset a priori by using the normal equation is proposed. The performance of the suggested approach is compared to the classic RBF and four other well-known metamodeling methods, in terms of accuracy, efficiency and, most importantly, suitability for integration with MOO evolutionary algorithms. It has been found that the proposed approach is accurate in most of the test functions, and it was the fastest compared to other methods. Additionally, the new approach is the most suitable method for MB-MOO, when integrated with evolutionary algorithms. The proposed approach is integrated with the strength Pareto evolutionary algorithm (SPEA2) and applied to two real-world engineering problems: MB-MOO of the disk brake system of a heavy truck, and the metal cutting process in a turning operation. Thereafter, the Pareto-optimal fronts are obtained and the results are presented. The MB-MOO in both case studies has been found to be an efficient and effective method. To validate the results of the latter MB-MOO case study, a framework for automated finite element (FE) simulation based MOO (SB-MOO) of machining processes is developed and presented by applying it to the same metal cutting process in a turning operation. It has been proved that the framework is effective in achieving the MOO of machining processes based on actual FE simulations.
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| 6. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
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Multi-objective optimisation of tool indexing problem : a mathematical model and a modified genetic algorithm
- 2021
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Ingår i: International Journal of Production Research. - : Taylor & Francis Group. - 0020-7543 .- 1366-588X. ; 59:12, s. 3572-3590
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Tidskriftsartikel (refereegranskat)abstract
- Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices – the “tool-indexing problem”. Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation tool indexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision makers to manage their tools more efficiently compared to current practices.
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| 7. |
- 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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| 8. |
- 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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| 9. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
-
Optimizing index positions on CNC tool magazines considering cutting tool life and duplicates
- 2020
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Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271. ; 93, s. 1508-1513
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Tidskriftsartikel (refereegranskat)abstract
- Minimizing the non-machining time of CNC machines requires optimal positioning of cutting tools on indexes (stations) of CNC machine turret magazine. This work presents a genetic algorithm with a novel solution representation and genetic operators to find the best possible index positions while tool duplicates and tools life are taken in to account during the process. The tool allocation in a machining process of a crankshaft with 10 cutting operations, on a 45-index magazine, is optimized for the entire life of the tools on the magazine. The tool-indexing time is considerably reduced compared to the current index positions being used in an automotive factory.
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| 10. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
-
Radial basis functions as surrogate models with a priori bias in comparison with a posteriori bias
- 2017
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Ingår i: Structural and multidisciplinary optimization (Print). - : Springer Science and Business Media LLC. - 1615-147X .- 1615-1488. ; 55:4, s. 1453-1469
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Tidskriftsartikel (refereegranskat)abstract
- In order to obtain a robust performance, the established approach when using radial basis function networks (RBF) as metamodels is to add a posteriori bias which is defined by extra orthogonality constraints. We mean that this is not needed, instead the bias can simply be set a priori by using the normal equation, i.e. the bias becomes the corresponding regression model. In this paper we demonstrate that the performance of our suggested approach with a priori bias is in general as good as, or even for many test examples better than, the performance of RBF with a posteriori bias. Using our approach, it is clear that the global response is modelled with the bias and that the details are captured with radial basis functions. The accuracy of the two approaches are investigated by using multiple test functions with different degrees of dimensionality. Furthermore, several modeling criteria, such as the type of radial basis functions used in the RBFs, dimension of the test functions, sampling techniques and size of samples, are considered to study their affect on the performance of the approaches. The power of RBF with a priori bias for surrogate based design optimization is also demonstrated by solving an established engineering benchmark of a welded beam and another benchmark for different sampling sets generated by successive screening, random, Latin hypercube and Hammersley sampling, respectively. The results obtained by evaluation of the performance metrics, the modeling criteria and the presented optimal solutions, demonstrate promising potentials of our RBF with a priori bias, in addition to the simplicity and straight-forward use of the approach.
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