ROBUSTNESS IMPROVEMENT USING OPEN SOURCE CODE LIBRARIES

J. Sanchez; Z. Björkman; K. N. Otto

doi:10.1017/dsd.2020.82

ROBUSTNESS IMPROVEMENT USING OPEN SOURCE CODE LIBRARIES

J. Sanchez, Z. Björkman, K. N. Otto

Department of Mechanical Engineering

Research output: Contribution to journal › Conference article › Scientific › peer-review

2 Citations (Scopus)

55 Downloads (Pure)

Abstract

Computer tools are commonly used to assess designs. We develop a toolchain using open source code libraries in Python to provide an open source, interactive robust design improvement toolchain. A reference folder contains a script that reads an input parameter value file and runs the simulation. The toolchain executes uncertainty quantification steps by replicating the reference folder. This is repeated for design points, and mean and sigma graphs generated versus each design variable. This fits within a workflow of defining variation modes, design variables, and toolchain execution.

Original language	English
Pages (from-to)	365-374
Number of pages	10
Journal	Proceedings of the Design Society: DESIGN Conference
Volume	1
DOIs	https://doi.org/10.1017/dsd.2020.82
Publication status	Published - 11 Jun 2020
MoE publication type	A4 Conference publication
Event	International Design Conference - Virtual, Online Duration: 26 Oct 2020 → 29 Oct 2020 Conference number: 16

Keywords

computational design methods
design automation
open source design
robust design

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10.1017/dsd.2020.82

ENG_Sanchez_et_al_Robustness_improvement_using_International_Design_ConferenceFinal published version, 497 KB

Cite this

@article{d5eeba90c9534edb8a9bbc92bae563fe,

title = "ROBUSTNESS IMPROVEMENT USING OPEN SOURCE CODE LIBRARIES",

abstract = "Computer tools are commonly used to assess designs. We develop a toolchain using open source code libraries in Python to provide an open source, interactive robust design improvement toolchain. A reference folder contains a script that reads an input parameter value file and runs the simulation. The toolchain executes uncertainty quantification steps by replicating the reference folder. This is repeated for design points, and mean and sigma graphs generated versus each design variable. This fits within a workflow of defining variation modes, design variables, and toolchain execution.",

keywords = "computational design methods, design automation, open source design, robust design",

author = "J. Sanchez and Z. Bj{\"o}rkman and Otto, {K. N.}",

note = "Funding Information: This work was made possible with support from an Academy of Finland, Project Number 310252. Publisher Copyright: {\textcopyright} The Author(s), 2020. Published by Cambridge University Press.; International Design Conference, DESIGN ; Conference date: 26-10-2020 Through 29-10-2020",

year = "2020",

month = jun,

day = "11",

doi = "10.1017/dsd.2020.82",

language = "English",

volume = "1",

pages = "365--374",

journal = "Proceedings of the Design Society: DESIGN Conference",

issn = "2633-7762",

publisher = "The Design Society",

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T1 - ROBUSTNESS IMPROVEMENT USING OPEN SOURCE CODE LIBRARIES

AU - Sanchez, J.

AU - Björkman, Z.

AU - Otto, K. N.

N1 - Conference code: 16

PY - 2020/6/11

Y1 - 2020/6/11

N2 - Computer tools are commonly used to assess designs. We develop a toolchain using open source code libraries in Python to provide an open source, interactive robust design improvement toolchain. A reference folder contains a script that reads an input parameter value file and runs the simulation. The toolchain executes uncertainty quantification steps by replicating the reference folder. This is repeated for design points, and mean and sigma graphs generated versus each design variable. This fits within a workflow of defining variation modes, design variables, and toolchain execution.

AB - Computer tools are commonly used to assess designs. We develop a toolchain using open source code libraries in Python to provide an open source, interactive robust design improvement toolchain. A reference folder contains a script that reads an input parameter value file and runs the simulation. The toolchain executes uncertainty quantification steps by replicating the reference folder. This is repeated for design points, and mean and sigma graphs generated versus each design variable. This fits within a workflow of defining variation modes, design variables, and toolchain execution.

KW - computational design methods

KW - design automation

KW - open source design

KW - robust design

UR - http://www.scopus.com/inward/record.url?scp=85110331947&partnerID=8YFLogxK

U2 - 10.1017/dsd.2020.82

DO - 10.1017/dsd.2020.82

M3 - Conference article

AN - SCOPUS:85110331947

SN - 2633-7762

VL - 1

SP - 365

EP - 374

JO - Proceedings of the Design Society: DESIGN Conference

JF - Proceedings of the Design Society: DESIGN Conference

T2 - International Design Conference

Y2 - 26 October 2020 through 29 October 2020

ER -

ROBUSTNESS IMPROVEMENT USING OPEN SOURCE CODE LIBRARIES

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System Performance Quality Assurance through Digitalization

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ROBUSTNESS IMPROVEMENT USING OPEN SOURCE CODE LIBRARIES

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Keywords

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System Performance Quality Assurance through Digitalization

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