دورية أكاديمية
Machine learning based approach for shape memory polymer behavioural characterization
| العنوان: | Machine learning based approach for shape memory polymer behavioural characterization |
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| المؤلفون: | Ritaban Dutta, David Renshaw, Cherry Chen, Daniel Liang |
| المصدر: | Array, Vol 7, Iss , Pp 100036- (2020) |
| بيانات النشر: | Elsevier, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Computer engineering. Computer hardware LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | Shape memory polymer, Machine learning, Data driven modelling, Data science, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | In this article we aim to combine video data analysis techniques, scalable machine learning, and Shape memory polymers (SMPs) materials to develop a model-based architecture for the advancement of rapid characterization of a novel material. Although artificially intelligent machines, e.g. soft robotics systems, with high flexibility have conquered the production line and other controlled, predictable environments, their use in complex real-world scenarios has to date remained limited. Newly discovered and experimented SMPs are increasingly being used for application solutions in automotive, aerospace, construction and commercial field. But being a nascent field there is little knowledge on the shape recovery behaviour of laminates with a SMP film and there are only methods reported in literature for quantifying the material behaviour. Through various experimental data gathering and predictive modelling it was established that proposed methodology can rapidly characterize novel materials. The proposed modelling workflow showed accuracy of 90% with 92% sensitivity and 94% specificity while predicting recovery behaviour of SMP body, showcasing high potential for data driven rapid characterisation of shape memory materials. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2590-0056 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2590005620300217; https://doaj.org/toc/2590-0056 |
| DOI: | 10.1016/j.array.2020.100036 |
| URL الوصول: | https://doaj.org/article/139631ca73f54a0781932d4011e44076 |
| رقم الأكسشن: | edsdoj.139631ca73f54a0781932d4011e44076 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 25900056 |
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| DOI: | 10.1016/j.array.2020.100036 |