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Publications (10 of 169) Show all publications
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
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 1433-3015Article in journal (Refereed) Published
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:his:diva-15136 (URN)10.1007/s00170-018-2360-8 (DOI)
Available from: 2018-05-09 Created: 2018-05-09 Last updated: 2018-07-11
Linnéusson, G., Ng, A. H. C. & Aslam, T. (2018). A hybrid simulation-based optimization framework for supporting strategic maintenance to improve production performance. European Journal of Operational Research
Open this publication in new window or tab >>A hybrid simulation-based optimization framework for supporting strategic maintenance to improve production performance
2018 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860Article in journal (Refereed) Submitted
National Category
Production Engineering, Human Work Science and Ergonomics Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15064 (URN)
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2018-07-31
Senington, R., Baumeister, F., Ng, A. & Oscarsson, J. (2018). A linked data approach for the connection of manufacturing processes with production simulation models. In: Florent Laroche, Alain Bernard (Ed.), 28th CIRP Design Conference 2018, 23-25 May 2018, Nantes, France: . Paper presented at 28th CIRP Design Conference, Nantes, France, May 23-25, 2018 (pp. 440-445). Elsevier, 70
Open this publication in new window or tab >>A linked data approach for the connection of manufacturing processes with production simulation models
2018 (English)In: 28th CIRP Design Conference 2018, 23-25 May 2018, Nantes, France / [ed] Florent Laroche, Alain Bernard, Elsevier, 2018, Vol. 70, p. 440-445Conference paper (Refereed)
Abstract [en]

This paper discusses the expected benefits of using linked data for the tasks of gathering, managing and understanding the data of smart factories. It has the further specific focus of using this data to maintaining a Digital Twin for the purposes of analysis and optimisation of such factories. The proposals are motivated by the use of an industrial example looking at the types of information required, the variation in data which is available and the requirements of an analysis platform to provide parameters for seamless, automated simulation and optimisation. 

Place, publisher, year, edition, pages
Elsevier, 2018
Series
Procedia CIRP, ISSN 2212-8271 ; 70
Keywords
Smart Factory, Digital Twin, Linked Data
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16005 (URN)10.1016/j.procir.2018.03.243 (DOI)000437126800074 ()
Conference
28th CIRP Design Conference, Nantes, France, May 23-25, 2018
Available from: 2018-07-20 Created: 2018-07-20 Last updated: 2018-07-23
Bandaru, S. & Ng, A. H. C. (2018). An empirical comparison of metamodeling strategies in noisy environments. In: Hernan Aguirre (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018): . Paper presented at Genetic and Evolutionary Computation Conference (GECCO-2018), Kyoto, July 15th-19th 2018 (pp. 817-824). New York, NY, USA: ACM Digital Library, Article ID 3205509.
Open this publication in new window or tab >>An empirical comparison of metamodeling strategies in noisy environments
2018 (English)In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018) / [ed] Hernan Aguirre, New York, NY, USA: ACM Digital Library, 2018, p. 817-824, article id 3205509Conference paper, Published paper (Refereed)
Abstract [en]

Metamodeling plays an important role in simulation-based optimization by providing computationally inexpensive approximations for the objective and constraint functions. Additionally metamodeling can also serve to filter noise, which is inherent in many simulation problems causing optimization algorithms to be mislead. In this paper, we conduct a thorough statistical comparison of four popular metamodeling methods with respect to their approximation accuracy at various levels of noise. We use six scalable benchmark problems from the optimization literature as our test suite. The problems have been chosen to represent different types of fitness landscapes, namely, bowl-shaped, valley-shaped, steep ridges and multi-modal, all of which can significantly influence the impact of noise. Each metamodeling technique is used in combination with four different noise handling techniques that are commonly employed by practitioners in the field of simulation-based optimization. The goal is to identify the metamodeling strategy, i.e. a combination of metamodeling and noise handling, that performs significantly better than others on the fitness landscapes under consideration. We also demonstrate how these results carry over to a simulation-based optimization problem concerning a scalable discrete event model of a simple but realistic production line.

Place, publisher, year, edition, pages
New York, NY, USA: ACM Digital Library, 2018
Series
GECCO '18
Keywords
simulation, optimization, metamodeling, noise
National Category
Computer Sciences Other Mechanical Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15966 (URN)10.1145/3205455.3205509 (DOI)978-1-4503-5618-3 (ISBN)
Conference
Genetic and Evolutionary Computation Conference (GECCO-2018), Kyoto, July 15th-19th 2018
Projects
Synergy KDDS
Funder
Knowledge Foundation, 41231
Available from: 2018-07-12 Created: 2018-07-12 Last updated: 2018-07-12Bibliographically approved
Ayani, M., Ganebäck, M. & Ng, A. H. C. (2018). Digital Twin: Applying emulation for machine reconditioning. In: Lihui Wang (Ed.), 51st CIRP Conference on Manufacturing Systems: . Paper presented at 51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018 (pp. 243-248). Elsevier, 72
Open this publication in new window or tab >>Digital Twin: Applying emulation for machine reconditioning
2018 (English)In: 51st CIRP Conference on Manufacturing Systems / [ed] Lihui Wang, Elsevier, 2018, Vol. 72, p. 243-248Conference paper, Published paper (Refereed)
Abstract [en]

Old machine reconditioning projects extend the life length of machines with reduced investments, however they frequently involve complex challenges. Due to the lack of technical documentation and the fact that the machines are running in production, they can require a reverse engineering phase and extremely short commissioning times. Recently, emulation software has become a key tool to create Digital Twins and carry out virtual commissioning of new manufacturing systems, reducing the commissioning time and increasing its final quality. This paper presents an industrial application study in which an emulation model is used to support a reconditioning project and where the benefits gained in the working process are highlighted.

Place, publisher, year, edition, pages
Elsevier, 2018
Series
Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271 ; 72
Keywords
Digital twin, Emulation, Virtual commissioning, Industry 4.0, Reconditioning, Retrofitting
National Category
Control Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15982 (URN)10.1016/j.procir.2018.03.139 (DOI)
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Projects
Twin
Available from: 2018-07-16 Created: 2018-07-16 Last updated: 2018-07-30Bibliographically approved
Morshedzadeh, I., Oscarsson, J., Ng, A. H. C., Jeusfeld, M. A. & Sillanpaa, J. (2018). Product lifecycle management with provenance management and virtual models: an industrial use-case study. In: Lihui Wang (Ed.), 51st CIRP Conference on Manufacturing Systems: . Paper presented at 51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018), 16-18 May 2018, Stockholm, Sweden (pp. 1190-1195). Elsevier
Open this publication in new window or tab >>Product lifecycle management with provenance management and virtual models: an industrial use-case study
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2018 (English)In: 51st CIRP Conference on Manufacturing Systems / [ed] Lihui Wang, Elsevier, 2018, , p. 6p. 1190-1195Conference paper, Published paper (Refereed)
Abstract [en]

Saving and managing virtual models’ provenance information (models’ history) can increase the level of reusability of those models. This paper describes a provenance management system (PMS) that has been developed based on an industrial case study.

The product lifecycle management (PLM) system, as a main data management system, is responsible for receiving virtual models and their related data from Computer-Aided technologies (CAx) and providing this information for the PMS. In this paper, the management of discrete event simulation data with the PLM system will be demonstrated as the first link of provenance data management chain (CAx-PLM-PMS).

Place, publisher, year, edition, pages
Elsevier, 2018. p. 6
Series
Procedia CIRP, ISSN 2212-8271 ; 72
Keywords
Discrete event simulation, Provenance, Product lifecycle
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering; Information Systems
Identifiers
urn:nbn:se:his:diva-15920 (URN)10.1016/j.procir.2018.03.157 (DOI)
Conference
51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018), 16-18 May 2018, Stockholm, Sweden
Available from: 2018-07-03 Created: 2018-07-03 Last updated: 2018-07-10Bibliographically approved
Linnéusson, G., Ng, A. H. C. & Aslam, T. (2018). Quantitative analysis of a conceptual system dynamics maintenance performance model using multi-objective optimisation. Journal of Simulation, 12(2), 171-189
Open this publication in new window or tab >>Quantitative analysis of a conceptual system dynamics maintenance performance model using multi-objective optimisation
2018 (English)In: Journal of Simulation, ISSN 1747-7778, E-ISSN 1747-7786, Vol. 12, no 2, p. 171-189Article in journal (Refereed) Published
National Category
Reliability and Maintenance Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15063 (URN)10.1080/17477778.2018.1467849 (DOI)000432552700008 ()2-s2.0-85047239919 (Scopus ID)
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2018-08-08Bibliographically 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. © 2018 Elsevier Ltd

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)
Available from: 2018-04-01 Created: 2018-04-03 Last updated: 2018-07-13
Linnéusson, G., Ng, A. H. C. & Aslam, T. (2018). Relating strategic time horizons and proactiveness in equipment maintenance: a simulation-based optimization study. In: Lihui Wang (Ed.), 51st CIRP Conference on Manufacturing Systems: . Paper presented at 51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018 (pp. 1293-1298). Elsevier, 72
Open this publication in new window or tab >>Relating strategic time horizons and proactiveness in equipment maintenance: a simulation-based optimization study
2018 (English)In: 51st CIRP Conference on Manufacturing Systems / [ed] Lihui Wang, Elsevier, 2018, Vol. 72, p. 1293-1298Conference paper, Published paper (Refereed)
Abstract [en]

Identifying sustainable strategies to develop maintenance performance within the short-termism framework is indeed challenging. It requires reinforcing long-term capabilities while managing short-term requirements. This study explores differently applied time horizons when optimizing the tradeoff between conflicting objectives, in maintenance performance, which are: maximize availability, minimize maintenance costs, and minimize maintenance consequence costs. The study has applied multi-objective optimization on a maintenance performance system dynamics model that contains feedback structures that explains reactive and proactive maintenance behavior on a general level. The quantified results provide insights on how different time frames are conditional to enable more or less proactive maintenance behavior in servicing production.

Place, publisher, year, edition, pages
Elsevier, 2018
Series
Procedia CIRP, ISSN 2212-8271 ; 72
Keywords
strategic development, maintenance performance, proactive maintenance, multi-objective optimization, system dynamics, simulation
National Category
Reliability and Maintenance Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15066 (URN)10.1016/j.procir.2018.03.219 (DOI)2-s2.0-85049594037 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2018-07-30Bibliographically approved
Goienetxea Uriarte, A., Ng, A. H. C. & Urenda Moris, M. (2018). Supporting the lean journey with simulation and optimization in the context of Industry 4.0. Paper presented at 8th Swedish Production Symposium, SPS 2018, Stockholm, Sweden, May 16-18, 2018. Procedia Manufacturing, 25, 586-593
Open this publication in new window or tab >>Supporting the lean journey with simulation and optimization in the context of Industry 4.0
2018 (English)In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 586-593Article in journal (Refereed) Published
Abstract [en]

The new industrial revolution brings important changes to organizations that will need to adapt their machines, systems and employees’ competences to sustain their business in a highly competitive market. Management philosophies such as lean will also need to adapt to the improvement possibilities that Industry 4.0 brings. This paper presents a review on the role of lean and simulation in the context of Industry 4.0. Additionally, the paper presents a conceptual framework where simulation and optimization will make the lean approach more efficient, speeding up system improvements and reconfiguration, by means of an enhanced decision-making process and supported organizational learning.

Keywords
Lean, Simulation, Optimization, Industry 4.0, Simulation-based optimization, Decision-making
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15978 (URN)10.1016/j.promfg.2018.06.097 (DOI)
Conference
8th Swedish Production Symposium, SPS 2018, Stockholm, Sweden, May 16-18, 2018
Available from: 2018-07-16 Created: 2018-07-16 Last updated: 2018-08-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0111-1776

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