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Amouzgar, K., Nourmohammadi, A. & Ng, A. H. C. (2021). Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm. International Journal of Production Research, 59(12), 3572-3590
Open this publication in new window or tab >>Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm
2021 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 59, no 12, p. 3572-3590Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
Taylor & Francis Group, 2021
Keywords
Tool indexing, genetic algorithm, non-machining time, multi-objective optimisation, SPEA2, mathematical model
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-19535 (URN)10.1080/00207543.2021.1897174 (DOI)000628710300001 ()2-s2.0-85102698141 (Scopus ID)
Projects
VF-KDO
Funder
Knowledge Foundation, HSK2019/20
Note

CC BY-NC-ND 4.0

Published online: 13 Mar 2021

Available from: 2021-03-15 Created: 2021-03-15 Last updated: 2023-02-22Bibliographically approved
Amouzgar, K., Bandaru, S., Andersson, T. & Ng, A. H. C. (2020). Metamodel based multi-objective optimization of a turning process by using finite element simulation. Engineering optimization (Print), 52(7), 1261-1278
Open this publication in new window or tab >>Metamodel based multi-objective optimization of a turning process by using finite element simulation
2020 (English)In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273, Vol. 52, no 7, p. 1261-1278Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2020
Keywords
Metamodeling, Surrogate models, Machining, Turning, Multi-objective optimization
National Category
Mechanical Engineering
Research subject
Production and Automation Engineering; Virtual Manufacturing Processes; VF-KDO
Identifiers
urn:nbn:se:his:diva-17520 (URN)10.1080/0305215X.2019.1639050 (DOI)000477101800001 ()2-s2.0-85086011026 (Scopus ID)
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2023-02-22Bibliographically approved
Amouzgar, K., Ng, A. H. C. & Ljustina, G. (2020). Optimizing index positions on CNC tool magazines considering cutting tool life and duplicates. Paper presented at 53rd CIRP Conference on Manufacturing Systems, CMS 2020, Northwestern University, Chicago, United States, 1 July 2020 through 3 July 2020, Code 163174. Procedia CIRP, 93, 1508-1513
Open this publication in new window or tab >>Optimizing index positions on CNC tool magazines considering cutting tool life and duplicates
2020 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 93, p. 1508-1513Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Cutting tools, Genetic algorithm, Non-machining time, Tool indexing, Tool life, Computer control systems, Genetic algorithms, Machinery, Machining, Manufacture, Cutting operations, Genetic operators, Machining Process, Machining time, Optimal positioning, Solution representation, Tool allocation, Tool magazine
National Category
Production Engineering, Human Work Science and Ergonomics Manufacturing, Surface and Joining Technology
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-19401 (URN)10.1016/j.procir.2020.03.044 (DOI)2-s2.0-85098719766 (Scopus ID)
Conference
53rd CIRP Conference on Manufacturing Systems, CMS 2020, Northwestern University, Chicago, United States, 1 July 2020 through 3 July 2020, Code 163174
Note

CC BY-NC-ND 4.0

Edited by Robert X. Gao, Kornel Ehmann

This work was partially financed by the Knowledge Foundation (KKS), Sweden, through the Synergy project, Knowledge-Driven Decision Support via Optimization (KDDS).

Available from: 2021-01-14 Created: 2021-01-14 Last updated: 2023-02-24Bibliographically approved
Morshedzadeh, I., Ng, A. H. C. & Amouzgar, K. (2019). Management of virtual models with provenance information in the context of product lifecycle management: industrial case studies (1ed.). In: John Stark (Ed.), Product Lifecycle Management (Volume 4): The Case Studies (pp. 153-170). Cham: Springer
Open this publication in new window or tab >>Management of virtual models with provenance information in the context of product lifecycle management: industrial case studies
2019 (English)In: Product Lifecycle Management (Volume 4): The Case Studies / [ed] John Stark, Cham: Springer, 2019, 1, p. 153-170Chapter in book (Refereed)
Abstract [en]

Using virtual models instead of physical models can help industries reduce the time and cost of developments, despite the time consuming process of building virtual models. Therefore, reusing previously built virtual models instead of starting from scratch can eliminate a large amount of work from users. Is having a virtual model enough to reuse it in another study or task? In most cases, not. Information about the history of that model makes it clear for the users to decide if they can reuse this model or to what extent the model is needed to be modified. A provenance management system (PMS) has been designed to manage provenance information, and it has been used with product lifecycle management system (PLM) and computer-aided technologies (CAx) to save and present historical information about a virtual model. This chapter presents a sequence-based framework of the CAx-PLM-PMS chain and two application case studies considering the implementation of this framework.

Place, publisher, year, edition, pages
Cham: Springer, 2019 Edition: 1
Series
Decision Engineering, ISSN 1619-5736, E-ISSN 2197-6589
Keywords
Virtual models, Provenance, Product lifecycle management, virtual models, CAx, Discrete event simulation, Meta model, Cutting simulation
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17765 (URN)10.1007/978-3-030-16134-7_13 (DOI)978-3-030-16133-0 (ISBN)978-3-030-16134-7 (ISBN)
Projects
knowledge-driven decision making in Swedish industry (KDDS)
Available from: 2019-10-07 Created: 2019-10-07 Last updated: 2021-03-30Bibliographically approved
Amouzgar, K., Bandaru, S., Andersson, T. J. & Ng, A. H. C. (2018). A framework for simulation based multi-objective optimization and knowledge discovery of machining process. The International Journal of Advanced Manufacturing Technology, 98(9-12), 2469-2486
Open this publication in new window or tab >>A framework for simulation based multi-objective optimization and knowledge discovery of machining process
2018 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 98, no 9-12, p. 2469-2486Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Springer, 2018
National Category
Mechanical Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15136 (URN)10.1007/s00170-018-2360-8 (DOI)000444704300020 ()2-s2.0-85049664435 (Scopus ID)
Available from: 2018-05-09 Created: 2018-05-09 Last updated: 2020-11-02
Amouzgar, K. (2018). Metamodel Based Multi-Objective Optimization with Finite-Element Applications. (Doctoral dissertation). Högskolan i Skövde
Open this publication in new window or tab >>Metamodel Based Multi-Objective Optimization with Finite-Element Applications
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Högskolan i Skövde, 2018. p. 179
Series
Dissertation Series ; 22 (2018)
National Category
Mechanical Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15145 (URN)978-91-984187-4-3 (ISBN)
Public defence
2018-05-25, Portalen, Insikten, 10:00 (English)
Opponent
Supervisors
Available from: 2018-05-14 Created: 2018-05-14 Last updated: 2020-01-29Bibliographically approved
Amouzgar, K., Bandaru, S. & Ng, A. H. C. (2018). Radial basis functions with a priori bias as surrogate models: A comparative study. Engineering applications of artificial intelligence, 71, 28-44
Open this publication in new window or tab >>Radial basis functions with a priori bias as surrogate models: A comparative study
2018 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 71, p. 28-44Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
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
National Category
Mechanical Engineering
Research subject
Mechanics of Materials; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-14999 (URN)10.1016/j.engappai.2018.02.006 (DOI)000436213000003 ()2-s2.0-85042877194 (Scopus ID)
Note

©2018 Elsevier Ltd. All rights reserved.

Available from: 2018-04-01 Created: 2018-04-03 Last updated: 2021-01-07Bibliographically approved
Olofsson, J., Salomonsson, K., Johansson, J. & Amouzgar, K. (2017). A methodology for microstructure-based structural optimization of cast and injection moulded parts using knowledge-based design automation. Advances in Engineering Software, 109, 44-52
Open this publication in new window or tab >>A methodology for microstructure-based structural optimization of cast and injection moulded parts using knowledge-based design automation
2017 (English)In: Advances in Engineering Software, ISSN 0965-9978, E-ISSN 1873-5339, Vol. 109, p. 44-52Article in journal (Refereed) Published
Abstract [en]

The local material behaviour of cast metal and injection moulded parts is highly related to the geometrical design of the part as well as to a large number of process parameters. In order to use structural optimization methods to find the geometry that gives the best possible performance, both the geometry and the effect of the production process on the local material behaviour thus has to be considered. In this work, a multidisciplinary methodology to consider local microstructure-based material behaviour in optimizations of the design of engineering structures is presented. By adopting a knowledge based industrial product realisation perspective combined with a previously presented simulation strategy for microstructure-based material behaviour in Finite Element Analyses (FEA), the methodology integrates Computer Aided Design (CAD), casting and injection moulding simulations, FEA, design automation and a multi-objective optimization scheme into a novel structural optimization method for cast metal and injection moulded polymeric parts. The different concepts and modules in the methodology are described, their implementation into a prototype software is outlined, and the application and relevance of the methodology is discussed. 

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Component casting, Injection moulding, Design automation, Knowledge based engineering, Finite element analysis, Multi-objective optimization
National Category
Mechanical Engineering
Research subject
INF000; Mechanics of Materials
Identifiers
urn:nbn:se:his:diva-13576 (URN)10.1016/j.advengsoft.2017.03.003 (DOI)000400217700004 ()2-s2.0-85016937770 (Scopus ID)
Note

©2017 Elsevier

Available from: 2017-05-23 Created: 2017-05-23 Last updated: 2021-01-05Bibliographically approved
Amouzgar, K. & Strömberg, N. (2017). Radial basis functions as surrogate models with a priori bias in comparison with a posteriori bias. Structural and multidisciplinary optimization (Print), 55(4), 1453-1469
Open this publication in new window or tab >>Radial basis functions as surrogate models with a priori bias in comparison with a posteriori bias
2017 (English)In: Structural and multidisciplinary optimization (Print), ISSN 1615-147X, E-ISSN 1615-1488, Vol. 55, no 4, p. 1453-1469Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Metamodeling, Radial basis function, Design optimization, Design of experiment
National Category
Mechanical Engineering Mathematics
Research subject
INF201 Virtual Production Development; INF203 Virtual Machining; Mechanics of Materials
Identifiers
urn:nbn:se:his:diva-13556 (URN)10.1007/s00158-016-1569-0 (DOI)000398951100020 ()2-s2.0-84989170510 (Scopus ID)
Available from: 2017-05-11 Created: 2017-05-11 Last updated: 2020-01-22Bibliographically approved
Amouzgar, K., Cenanovic, M. & Salomonsson, K. (2015). Multi-objective optimization of material model parameters of an adhesive layer by using SPEA2. In: Qing Li; Grant P. Steven; Zhongpu (Leo) Zhang (Ed.), Advances in structural and multidisciplinary optimization: Proceedings of the 11th World Congress of Structural and Multidisciplinary Optimization (WCSMO-11), June7-12, 2015, Sydney, Australia. Paper presented at 11th World Congress of Structural and Multidisciplinary Optimization (WCSMO-11), June7-12, 2015, Sydney, Australia (pp. 249-254). The International Society for Structural and Multidisciplinary Optimization (ISSMO)
Open this publication in new window or tab >>Multi-objective optimization of material model parameters of an adhesive layer by using SPEA2
2015 (English)In: Advances in structural and multidisciplinary optimization: Proceedings of the 11th World Congress of Structural and Multidisciplinary Optimization (WCSMO-11), June7-12, 2015, Sydney, Australia / [ed] Qing Li; Grant P. Steven; Zhongpu (Leo) Zhang, The International Society for Structural and Multidisciplinary Optimization (ISSMO) , 2015, p. 249-254Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
The International Society for Structural and Multidisciplinary Optimization (ISSMO), 2015
Keywords
Multi-objective optimization, parameter identification, micro mechanical model, adhesive, CZM
National Category
Computer Engineering Mechanical Engineering
Identifiers
urn:nbn:se:his:diva-21908 (URN)978-0-646-94394-7 (ISBN)
Conference
11th World Congress of Structural and Multidisciplinary Optimization (WCSMO-11), June7-12, 2015, Sydney, Australia
Available from: 2022-10-05 Created: 2022-10-05 Last updated: 2022-10-05Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7534-0382

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