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Metamodel Based Multi-Objective Optimization with Finite-Element Applications
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0001-7534-0382
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Högskolan i Skövde , 2018. , s. 179
Serie
Dissertation Series ; 22 (2018)
Nationell ämneskategori
Maskinteknik
Forskningsämne
Produktion och automatiseringsteknik
Identifikatorer
URN: urn:nbn:se:his:diva-15145ISBN: 978-91-984187-4-3 (tryckt)OAI: oai:DiVA.org:his-15145DiVA, id: diva2:1205425
Disputation
2018-05-25, Portalen, Insikten, 10:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2018-05-14 Skapad: 2018-05-14 Senast uppdaterad: 2019-07-04Bibliografiskt granskad
Delarbeten
1. Radial basis functions with a priori bias as surrogate models: A comparative study
Öppna denna publikation i ny flik eller fönster >>Radial basis functions with a priori bias as surrogate models: A comparative study
2018 (Engelska)Ingår i: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 71, s. 28-44Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Radial basis functions are augmented with a posteriori bias in order to perform robustly when used as metamodels. Recently, it has been proposed that the bias can simply be set a priori by using the normal equation, i.e., the bias becomes the corresponding regression model. In this study, we demonstrate the performance of the suggested approach (RBFpri) with four other well-known metamodeling methods; Kriging, support vector regression, neural network and multivariate adaptive regression. The performance of the five methods is investigated by a comparative study, using 19 mathematical test functions, with five different degrees of dimensionality and sampling size for each function. The performance is evaluated by root mean squared error representing the accuracy, rank error representing the suitability of metamodels when coupled with evolutionary optimization algorithms, training time representing the efficiency and variation of root mean squared error representing the robustness. Furthermore, a rigorous statistical analysis of performance metrics is performed. The results show that the proposed radial basis function with a priori bias achieved the best performance in most of the experiments in terms of all three metrics. When considering the statistical analysis results, the proposed approach again behaved the best, while Kriging was relatively as accurate and support vector regression was almost as fast as RBFpri. The proposed RBF is proven to be the most suitable method in predicting the ranking among pairs of solutions utilized in evolutionary algorithms. Finally, the comparison study is carried out on a real-world engineering optimization problem.

Ort, förlag, år, upplaga, sidor
Elsevier, 2018
Nyckelord
Kriging, Metamodeling, Multivariate adaptive regression splines, Neural networks, Radial basis function, Support vector regression, Surrogate models, Errors, Evolutionary algorithms, Functions, Heat conduction, Image segmentation, Interpolation, Mean square error, Optimization, Regression analysis, Statistical methods, Radial basis functions, Support vector regression (SVR), Surrogate model, Radial basis function networks
Nationell ämneskategori
Maskinteknik
Forskningsämne
Materialmekanik; Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-14999 (URN)10.1016/j.engappai.2018.02.006 (DOI)000436213000003 ()2-s2.0-85042877194 (Scopus ID)
Tillgänglig från: 2018-04-01 Skapad: 2018-04-03 Senast uppdaterad: 2019-04-12Bibliografiskt granskad
2. Radial basis functions as surrogate models with a priori bias in comparison with a posteriori bias
Öppna denna publikation i ny flik eller fönster >>Radial basis functions as surrogate models with a priori bias in comparison with a posteriori bias
2017 (Engelska)Ingår i: Structural and multidisciplinary optimization (Print), ISSN 1615-147X, E-ISSN 1615-1488, Vol. 55, nr 4, s. 1453-1469Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Springer, 2017
Nyckelord
Metamodeling, Radial basis function, Design optimization, Design of experiment
Nationell ämneskategori
Maskinteknik Matematik
Forskningsämne
INF201 Virtual Production Development; INF203 Virtual Machining; Materialmekanik
Identifikatorer
urn:nbn:se:his:diva-13556 (URN)10.1007/s00158-016-1569-0 (DOI)000398951100020 ()2-s2.0-84989170510 (Scopus ID)
Tillgänglig från: 2017-05-11 Skapad: 2017-05-11 Senast uppdaterad: 2019-01-22Bibliografiskt granskad
3. A framework for simulation based multi-objective optimization and knowledge discovery of machining process
Öppna denna publikation i ny flik eller fönster >>A framework for simulation based multi-objective optimization and knowledge discovery of machining process
2018 (Engelska)Ingår i: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 98, nr 9-12, s. 2469-2486Artikel i tidskrift (Refereegranskat) Published
Nationell ämneskategori
Maskinteknik
Forskningsämne
Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-15136 (URN)10.1007/s00170-018-2360-8 (DOI)000444704300020 ()2-s2.0-85049664435 (Scopus ID)
Tillgänglig från: 2018-05-09 Skapad: 2018-05-09 Senast uppdaterad: 2019-05-08
4. Metamodel based multi-objective optimization of a turning process by using finite element simulation
Öppna denna publikation i ny flik eller fönster >>Metamodel based multi-objective optimization of a turning process by using finite element simulation
(Engelska)Artikel i tidskrift (Refereegranskat) Submitted
Abstract [en]

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.

Nyckelord
Metamodeling, Surrogate models, Machining, Turning, Multi-objective optimization
Nationell ämneskategori
Maskinteknik
Forskningsämne
Produktion och automatiseringsteknik; Materialmekanik
Identifikatorer
urn:nbn:se:his:diva-15139 (URN)
Anmärkning

"Preprint submitted to the Journal of Simulation Modelling Practice and Theory"

Tillgänglig från: 2018-05-09 Skapad: 2018-05-09 Senast uppdaterad: 2019-10-02Bibliografiskt granskad

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