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Situated Design Optimization of Haptic Devices
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).ORCID iD: 0000-0002-6528-1371
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).ORCID iD: 0000-0001-6692-2794
2016 (English)In: Procedia CIRP, Elsevier, 2016, Vol. 50, 293-298 p.Conference paper, Published paper (Refereed)
Abstract [en]

It is a complex task to develop and optimize a high-performing haptic device. Design optimization scenarios with predefined and fixed sets of performance requirements are presented in literature. However, the early design optimization phases for haptic devices are characterized by requirement conflicting requirements with uncertainties. With a lack of knowledge, and/or an ill-defined design problem, the challenges are not only to find a high quality solution with reasonable computational effort. In this paper, a previously proposed model-based framework and methodology for multi-disciplinary design optimization of haptic devices is further developed to enable situated design scenarios, i.e. design cases that may be characterized by changing requirements, constraints, and/or performance objectives, driven by the knowledge gained in the design and optimization process itself. To provide both precision and computational efficiency, the proposed situated, i.e. flexible and adaptable, framework is based on an approach that combines design-of-experiments (DOE) with meta-modelling methods for multi-objective optimization problems. The proposed methodology is described and verified with a 6 degree-of-freedom (DOF) TAU haptic device optimization scenario, with changing ranges for the design variables and the constraints. Results from the case study strongly indicate that a thoroughly balanced and sequential DOE and metamodelling process is capable of being both effective and efficient in a situated design scenario. It is shown that the knowledge gained in the process, e.g. the number of sampling points and the most appropriate training method, may be used to efficiently balance the required computational effort with the required level of accuracy.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 50, 293-298 p.
Series
Procedia CIRP, ISSN 2212-8271
Keyword [en]
design of system, haptic devices, Optimization
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-195403DOI: 10.1016/j.procir.2016.04.096ISI: 000387666600049Scopus ID: 2-s2.0-84986626890OAI: oai:DiVA.org:kth-195403DiVA: diva2:1048450
Conference
26th CIRP Design Conference, 2016, KTH Royal Institute of Technology Stockholm, Sweden, 15 June 2016 through 17 June 2016
Note

QC 20161121

Available from: 2016-11-21 Created: 2016-11-03 Last updated: 2016-12-14Bibliographically approved

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Sun, XuanSellgren, UlfAndersson, Kjell
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CiteExportLink to record
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