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A methodology for situated and effective design of haptic devices
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Machine Design (Div.).ORCID iD: 0000-0002-6528-1371
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The realism of virtual surgery through a surgical simulator depends largely on the precision and reliability of the haptic device. The quality of perception depends on the design of the haptic device, which presents a complex design task due to the multi-criteria and conflicting character of the functional and performance requirements. In the model-based evaluation of the performance criteria of a haptic device, the required computational resources increase with the complexity of the device structure as well as with the increased level of detail that is created in the detail design phases. Due to uncertain requirements and a significant knowledge gap, the design task is fuzzy and more complex in the early design phases.

The goal of this thesis is to propose a situated, i.e., flexible, scalable and efficient, methodology for multi-objective and multi-disciplinary design optimization of high-performing 6-DOF haptic devices.

The main contributions of this thesis are:

1. A model-based and simulation-driven engineering design methodology and a flexible pilot framework are proposed for design optimization of high-performing haptic devices. The multi-disciplinary design optimization method was utilized to balance the conflicting criteria/requirements of a multi-domain design case and to solve the design optimization problems concurrently.

2. A multi-tool framework is proposed. The framework integrates metamodel-based design optimization with complementary engineering tools from different software vendors, which was shown to significantly reduce the total computationally effort.

3. The metamodeling methods and sampling sizes for specific performance indices found from case studies were shown to be applicable and usable for several kinds of 6-degrees-of-freedom haptic devices.

4. The multi-tool framework and the assisting methodology were further developed to enable computationally efficient and situated design multi-objective optimization of high-performing haptic devices. The design-of-experiment (DOE) and metamodeling techniques are integrated with the optimization process in the framework as an option to solve the design optimization case with a process that depends on the present system complexity.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2017. , p. 66
Series
TRITA-MMK, ISSN 1400-1179 ; 2017:13
Keywords [en]
Design optimization, haptic devices, metamodel, multi-criteria, situatedness
National Category
Mechanical Engineering
Research subject
Machine Design
Identifiers
URN: urn:nbn:se:kth:diva-217327ISBN: 978-91-7729-573-0 (print)OAI: oai:DiVA.org:kth-217327DiVA, id: diva2:1155519
Public defence
2017-11-29, Gladan, Brinellvägen 85, Stockholm, 10:00 (English)
Supervisors
Note

QC 20171108

Available from: 2017-11-08 Created: 2017-11-08 Last updated: 2017-11-08Bibliographically approved
List of papers
1. Design optimization of haptic device - A systematic literature review
Open this publication in new window or tab >>Design optimization of haptic device - A systematic literature review
(English)In: Article in journal (Refereed) Submitted
Abstract [en]

Performance requirements for high-performing haptic devices are usually multi-criteria. Sometimes the requirements are interacting, and several of them are conflicting. Optimization is one of the main approaches to scrutinize the design space and to search for a design that satisfies all requirements. Many researchers have used and published optimization approaches to search for an optimal haptic device design. However, predicting the performance of a high-performing haptic device usually involves computationally intensive simulations and analyses with complex and heterogeneous models. In order to study what are the common design and performance requirements of haptic devices and what optimization approaches have been used to improve optimization effectiveness and efficiency, a literature review on the present state-of-the-art in these areas has been performed. The most commonly used performance requirements presented in the literature are the number of degrees-of-freedom, dynamic inertia, kinematic isotropy, stiffness, peak and continuous force, position/force resolution, and bandwidth. Furthermore, parallel and hybrid kinematic structures are more commonly used than serial structures. Multi-objective optimization (MOO) is a commonly used approach to simultaneously optimize all performance criteria. The most common optimization targets, as presented in published literature, are to maximize workspace, kinematic isotropy, as well as the peak force/torque provided by the device, and to minimize the dynamic inertia. Commonly used indices to constrain the design space are a minimum workspace, avoidance of singularities and motion limits of active and passive joints. The number of design variables varies from 2 to 9, and the most commonly used design variables are a set of mechanical parameters, such as the lengths and diameters of the mechanical components. To increase the efficiency of complex and multi-criteria optimization tasks, the Pareto-front approach combined with multidisciplinary design optimization (MDO) and metamodel techniques are recommended.

Keywords
Product design, design optimization, haptic interface, multi-criteria, system requirement
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:kth:diva-217324 (URN)
Note

QC 20171108

Available from: 2017-11-08 Created: 2017-11-08 Last updated: 2017-11-08Bibliographically approved
2. Towards a Methodology for multidisciplinary Design Optimization of Haptic Devices
Open this publication in new window or tab >>Towards a Methodology for multidisciplinary Design Optimization of Haptic Devices
2015 (English)In: Proceedings of the ASME 2015 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2015, 2015Conference paper, Published paper (Refereed)
Keywords
Haptics, optimization
National Category
Engineering and Technology Applied Mechanics
Research subject
Machine Design
Identifiers
urn:nbn:se:kth:diva-179047 (URN)10.13140/RG.2.1.4746.1203 (DOI)000379883800035 ()2-s2.0-84978958094 (Scopus ID)
Conference
ASME 2015 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2015,August 2-5, 2015, Boston, Massachusetts, USA
Note

QC 20151210

Available from: 2015-12-09 Created: 2015-12-09 Last updated: 2017-11-08Bibliographically approved
3. The search for an efficient design optimization methodology for haptic devices
Open this publication in new window or tab >>The search for an efficient design optimization methodology for haptic devices
(English)In: Engineering with Computers, ISSN 0177-0667, E-ISSN 1435-5663Article in journal (Refereed) Submitted
Abstract [en]

Design optimization of haptic devices must manage multiple objectives,complex models, and time-consuming simulations. This paper proposes amethodology to eciently and eectively optimize the design of a high-performing6-degree-of-freedom (DOF) haptic device. The proposed methodology adapts ametamodel selection method called Predictive Estimation of Model Fidelity (PEMF)method, and integrates it with metamodel-based design optimization (MBDO)process in a multi-tool framework. A 6-DOF TAU haptic device, with two designobjectives, the global isotropy index (GII) and the dexterous workspace volumeindex (VI), is used to exemplify and verify the methodology. Two additional studiesare made to nd the most suitable sample size for metamodel training, and tostudy the eects of metamodel combinations on the solution accuracy. The generalityof the ndings is veried with MBDO of a completely dierent 6-DOF hapticdevice based on a Stewart Platform. The proposed metamodel-based methodologyreduces the computational time about 27 times as compared to using the fullmodel. For the specied design optimization problem, 300 sample points are ableto provide accurate enough metamodels and system solutions. The combination ofthe best-t metamodel for each design objective, Kriging (KR) for GII and RadialBasis Functions (RBF) for VI, can provide suciently accurate solutions whichlikely are applicable for all types of 6-DOF haptic devices.

Keywords
design optimization, multi-objective, metamodel, predictive estimation of model fidelity
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:kth:diva-217325 (URN)
Note

QC 20171108

Available from: 2017-11-08 Created: 2017-11-08 Last updated: 2017-11-08Bibliographically approved
4. Situated Design Optimization of Haptic Devices
Open this publication in new window or tab >>Situated Design Optimization of Haptic Devices
2016 (English)In: Procedia CIRP, Elsevier, 2016, Vol. 50, p. 293-298Conference 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
Series
Procedia CIRP, ISSN 2212-8271
Keywords
design of system, haptic devices, Optimization
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:kth:diva-195403 (URN)10.1016/j.procir.2016.04.096 (DOI)000387666600049 ()2-s2.0-84986626890 (Scopus ID)
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: 2017-11-08Bibliographically approved
5. Efficient and situated design of haptic devices
Open this publication in new window or tab >>Efficient and situated design of haptic devices
(English)In: Article in journal (Refereed) Submitted
Abstract [en]

It is a challenging task to develop and optimize a high-performing haptic device for, e.g., medical training,because of the multi-objective performance requirements, and the complex relations between the design variablesand the performance objectives. Consequently, the solution space is scrutinized with efficient optimization techniques,and the cause-effect relations are preferably represented in a way that enables further reasoning in amulti-discipline setting, e.g., as Pareto-front curves and surfaces. Furthermore, if we have found a Pareto-optimalsolution, how can we benefit from the knowledge and optimization results obtained in that process if we are facinga trailing challenge (a new design situation) to re-design the initial solution to another application and/or otherrequirements, i.e. how can we address this new situation by efficiently re-using as much as possible of the initialsolutions? In this paper, we first present multi-objective optimizations of a 6-degree-of-freedom TAU haptic device.To investigate how we efficiently can address new design situations, by making use of the initial solutions, we havedefined, solved and analyzed six re-design cases. For the different cases, it is shown what can be reused and inwhat way that can be done. The six studied cases are then analyzed and a generic process for situated designoptimization, based on available knowledge and Pareto-optimal solutions, is proposed.

Keywords
Design optimization, haptic devices, situatedness, multi-criteria, metamodel, multidisciplinary design optimization
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:kth:diva-217326 (URN)
Note

QC 20171108

Available from: 2017-11-08 Created: 2017-11-08 Last updated: 2017-11-08Bibliographically approved

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