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Schmitt, T., Viklund, P., Sjölander, M., Hanson, L., Amouzgar, K. & Urenda Moris, M. (2023). Augmented reality for machine monitoring in industrial manufacturing: framework and application development. Paper presented at 56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023 Cape Town 24 October 2023 through 26 October 2023. Procedia CIRP, 1327-1332
Åpne denne publikasjonen i ny fane eller vindu >>Augmented reality for machine monitoring in industrial manufacturing: framework and application development
Vise andre…
2023 (engelsk)Inngår i: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, s. 1327-1332Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Enhancing data visualization on the shop floor provides support for dealing with the increasing complexity of production and the need for progressing towards emerging goals like energy efficiency. It enables personnel to make informed decisions based on real-time data displayed on user-friendly interfaces. Augmented reality (AR) technology provides a promising solution to this problem by allowing for the visualization of data in a more immersive and interactive way. The aim of this study is to present a framework to visualize live and historic data about energy consumption in AR, using Power BI and Unity, and discuss the applications' capabilities. The study demonstrated that both Power BI and Unity can effectively visualize near-real-time machine data with the aid of appropriate data pipelines. While both applications have their respective strengths and limitations, they can support informed decision-making and proactive measures to improve energy utilization. Additional research is needed to examine the correlation between energy consumption and production dynamics, as well as to assess the user-friendliness of the data presentation for effective decision-making support. 

sted, utgiver, år, opplag, sider
Elsevier, 2023
Emneord
augmented reality, data pipelines, energy efficiency, user interface, Visualization
HSV kategori
Forskningsprogram
Användarcentrerad produktdesign
Identifikatorer
urn:nbn:se:his:diva-23628 (URN)10.1016/j.procir.2023.09.171 (DOI)2-s2.0-85184584712 (Scopus ID)
Konferanse
56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023 Cape Town 24 October 2023 through 26 October 2023
Prosjekter
EXPLAIN
Merknad

CC BY-NC-ND 4.0 DEED

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing Systems 2023.

Correspondence Address: T. Schmitt; Scania CV AB, Smart Factory Lab, Södertälje, Verkstadsvägen 17, 151 38, Sweden; email: thomas.schmitt@scania.com

This paper is produced as part of the EXPLAIN project, which is partly funded by the Swedish research and development agency Vinnova.

Tilgjengelig fra: 2024-02-22 Laget: 2024-02-22 Sist oppdatert: 2024-02-26bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm
2021 (engelsk)Inngår i: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 59, nr 12, s. 3572-3590Artikkel i tidsskrift (Fagfellevurdert) 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. 

sted, utgiver, år, opplag, sider
Taylor & Francis Group, 2021
Emneord
Tool indexing, genetic algorithm, non-machining time, multi-objective optimisation, SPEA2, mathematical model
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik; VF-KDO
Identifikatorer
urn:nbn:se:his:diva-19535 (URN)10.1080/00207543.2021.1897174 (DOI)000628710300001 ()2-s2.0-85102698141 (Scopus ID)
Prosjekter
VF-KDO
Forskningsfinansiär
Knowledge Foundation, HSK2019/20
Merknad

CC BY-NC-ND 4.0

Published online: 13 Mar 2021

Tilgjengelig fra: 2021-03-15 Laget: 2021-03-15 Sist oppdatert: 2023-02-22bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Metamodel based multi-objective optimization of a turning process by using finite element simulation
2020 (engelsk)Inngår i: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273, Vol. 52, nr 7, s. 1261-1278Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Taylor & Francis Group, 2020
Emneord
Metamodeling, Surrogate models, Machining, Turning, Multi-objective optimization
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik; Virtual Manufacturing Processes; VF-KDO
Identifikatorer
urn:nbn:se:his:diva-17520 (URN)10.1080/0305215X.2019.1639050 (DOI)000477101800001 ()2-s2.0-85086011026 (Scopus ID)
Tilgjengelig fra: 2019-08-12 Laget: 2019-08-12 Sist oppdatert: 2023-02-22bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Optimizing index positions on CNC tool magazines considering cutting tool life and duplicates
2020 (engelsk)Inngår i: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 93, s. 1508-1513Artikkel i tidsskrift (Fagfellevurdert) 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. 

sted, utgiver, år, opplag, sider
Elsevier, 2020
Emneord
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
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik; VF-KDO
Identifikatorer
urn:nbn:se:his:diva-19401 (URN)10.1016/j.procir.2020.03.044 (DOI)2-s2.0-85098719766 (Scopus ID)
Konferanse
53rd CIRP Conference on Manufacturing Systems, CMS 2020, Northwestern University, Chicago, United States, 1 July 2020 through 3 July 2020, Code 163174
Merknad

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).

Tilgjengelig fra: 2021-01-14 Laget: 2021-01-14 Sist oppdatert: 2023-02-24bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Management of virtual models with provenance information in the context of product lifecycle management: industrial case studies
2019 (engelsk)Inngår i: Product Lifecycle Management (Volume 4): The Case Studies / [ed] John Stark, Cham: Springer, 2019, 1, s. 153-170Kapittel i bok, del av antologi (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Cham: Springer, 2019 Opplag: 1
Serie
Decision Engineering, ISSN 1619-5736, E-ISSN 2197-6589
Emneord
Virtual models, Provenance, Product lifecycle management, virtual models, CAx, Discrete event simulation, Meta model, Cutting simulation
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik
Identifikatorer
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)
Prosjekter
knowledge-driven decision making in Swedish industry (KDDS)
Tilgjengelig fra: 2019-10-07 Laget: 2019-10-07 Sist oppdatert: 2021-03-30bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>A framework for simulation based multi-objective optimization and knowledge discovery of machining process
2018 (engelsk)Inngår i: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 98, nr 9-12, s. 2469-2486Artikkel i tidsskrift (Fagfellevurdert) Published
sted, utgiver, år, opplag, sider
Springer, 2018
HSV kategori
Forskningsprogram
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)
Tilgjengelig fra: 2018-05-09 Laget: 2018-05-09 Sist oppdatert: 2020-11-02
Amouzgar, K. (2018). Metamodel Based Multi-Objective Optimization with Finite-Element Applications. (Doctoral dissertation). Högskolan i Skövde
Åpne denne publikasjonen i ny fane eller vindu >>Metamodel Based Multi-Objective Optimization with Finite-Element Applications
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Högskolan i Skövde, 2018. s. 179
Serie
Dissertation Series ; 22 (2018)
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-15145 (URN)978-91-984187-4-3 (ISBN)
Disputas
2018-05-25, Portalen, Insikten, 10:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-05-14 Laget: 2018-05-14 Sist oppdatert: 2020-01-29bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Radial basis functions with a priori bias as surrogate models: A comparative study
2018 (engelsk)Inngår i: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 71, s. 28-44Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Elsevier, 2018
Emneord
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
HSV kategori
Forskningsprogram
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)
Merknad

©2018 Elsevier Ltd. All rights reserved.

Tilgjengelig fra: 2018-04-01 Laget: 2018-04-03 Sist oppdatert: 2021-01-07bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>A methodology for microstructure-based structural optimization of cast and injection moulded parts using knowledge-based design automation
2017 (engelsk)Inngår i: Advances in Engineering Software, ISSN 0965-9978, E-ISSN 1873-5339, Vol. 109, s. 44-52Artikkel i tidsskrift (Fagfellevurdert) 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. 

sted, utgiver, år, opplag, sider
Elsevier, 2017
Emneord
Component casting, Injection moulding, Design automation, Knowledge based engineering, Finite element analysis, Multi-objective optimization
HSV kategori
Forskningsprogram
INF000; Materialmekanik
Identifikatorer
urn:nbn:se:his:diva-13576 (URN)10.1016/j.advengsoft.2017.03.003 (DOI)000400217700004 ()2-s2.0-85016937770 (Scopus ID)
Merknad

©2017 Elsevier

Tilgjengelig fra: 2017-05-23 Laget: 2017-05-23 Sist oppdatert: 2021-01-05bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Radial basis functions as surrogate models with a priori bias in comparison with a posteriori bias
2017 (engelsk)Inngår i: Structural and multidisciplinary optimization (Print), ISSN 1615-147X, E-ISSN 1615-1488, Vol. 55, nr 4, s. 1453-1469Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Springer, 2017
Emneord
Metamodeling, Radial basis function, Design optimization, Design of experiment
HSV kategori
Forskningsprogram
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)
Tilgjengelig fra: 2017-05-11 Laget: 2017-05-11 Sist oppdatert: 2020-01-22bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0001-7534-0382