Sam Brooks, Rajkumar Roy, Jan-Henning Dirks, David Taylor
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A further evaluation of eight SE biological systems is performed using the SE complexity theory; nine experts and 23 students used the complexity theory to complete a ranking exercise. The results of the ranking were analysed and compared, with a final normalised mean plotted for each factor and biological system. From the analysis of both studies, proposed design rules are presented to help designers handle complexity while creating new self-engineering systems inspired by biology.KEYWORDS: Self-healingself-repairself-engineeringbioinspireddesign AcknowledgementsThe authors would first like to thank all the student and expert participants who agreed to take part in the exercise detailed in Section 5. Excellent comments and feedback were provided by both groups.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Engineering and Physical Sciences Research Council: [Grant Number EP/P027121/1].","PeriodicalId":50207,"journal":{"name":"Journal of Engineering Design","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A systematic study of biological SE systems from complexity and design perspectives\",\"authors\":\"Sam Brooks, Rajkumar Roy, Jan-Henning Dirks, David Taylor\",\"doi\":\"10.1080/09544828.2023.2266864\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AbstractPrevious research has presented the concept of self-engineering (SE) systems that aim to identify and preserve system functions autonomously. Examples of self-engineering responses include self-healing, self-repair, self-adapting and self-reconfiguration. Biology already utilises many of these responses to repair and survive, greater understanding of complexity in these biological systems could improve future bioinspired designs. This paper provides a novel systematic evaluation of the complexity of SE biological systems. Eight biological self-engineering systems identified are evaluated using Axiomatic design and complexity. The key functional requirements and design parameters for each biological system are identified. Design matrices were used to highlight different types of complexity. A further evaluation of eight SE biological systems is performed using the SE complexity theory; nine experts and 23 students used the complexity theory to complete a ranking exercise. The results of the ranking were analysed and compared, with a final normalised mean plotted for each factor and biological system. From the analysis of both studies, proposed design rules are presented to help designers handle complexity while creating new self-engineering systems inspired by biology.KEYWORDS: Self-healingself-repairself-engineeringbioinspireddesign AcknowledgementsThe authors would first like to thank all the student and expert participants who agreed to take part in the exercise detailed in Section 5. 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A systematic study of biological SE systems from complexity and design perspectives
AbstractPrevious research has presented the concept of self-engineering (SE) systems that aim to identify and preserve system functions autonomously. Examples of self-engineering responses include self-healing, self-repair, self-adapting and self-reconfiguration. Biology already utilises many of these responses to repair and survive, greater understanding of complexity in these biological systems could improve future bioinspired designs. This paper provides a novel systematic evaluation of the complexity of SE biological systems. Eight biological self-engineering systems identified are evaluated using Axiomatic design and complexity. The key functional requirements and design parameters for each biological system are identified. Design matrices were used to highlight different types of complexity. A further evaluation of eight SE biological systems is performed using the SE complexity theory; nine experts and 23 students used the complexity theory to complete a ranking exercise. The results of the ranking were analysed and compared, with a final normalised mean plotted for each factor and biological system. From the analysis of both studies, proposed design rules are presented to help designers handle complexity while creating new self-engineering systems inspired by biology.KEYWORDS: Self-healingself-repairself-engineeringbioinspireddesign AcknowledgementsThe authors would first like to thank all the student and expert participants who agreed to take part in the exercise detailed in Section 5. Excellent comments and feedback were provided by both groups.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Engineering and Physical Sciences Research Council: [Grant Number EP/P027121/1].
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The Journal of Engineering Design is a leading international publication that provides an essential forum for dialogue on important issues across all disciplines and aspects of the design of engineered products and systems. The Journal publishes pioneering, contemporary, best industrial practice as well as authoritative research, studies and review papers on the underlying principles of design, its management, practice, techniques and methodologies, rather than specific domain applications.
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