Fine-tuning optimization of poly lactic acid impact strength with variation of plasticizer using simple supervised machine learning methods

Fine-tuning optimization of poly lactic acid impact strength with variation of plasticizer using simple supervised machine learning methods

The use of machine learning to fine-tune the properties of materials is a remarkable achievement in the 21st century. Three machine learning (ML) methods were used to fine-tune and optimize the impact strength of polylactic acid (PLA) with different plasticizers: KNN (K-nearest neighbors), SVR (Supp...

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Published in:Express Polymer Letters
Main Author: Fatriansyah J.F.; Kustiyah E.; Surip S.N.; Federico A.; Pradana A.F.; Handayani A.S.; Jaafar M.; Dhaneswara D.
Format: Article
Language:English
Published: BME-PT and GTE 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164596409&doi=10.3144%2fexpresspolymlett.2023.71&partnerID=40&md5=adcc14241fb2e56fc067ab0e5d9396d2
id 2-s2.0-85164596409
spelling 2-s2.0-85164596409
Fatriansyah J.F.; Kustiyah E.; Surip S.N.; Federico A.; Pradana A.F.; Handayani A.S.; Jaafar M.; Dhaneswara D.
Fine-tuning optimization of poly lactic acid impact strength with variation of plasticizer using simple supervised machine learning methods
2023
Express Polymer Letters
17
9
10.3144/expresspolymlett.2023.71
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164596409&doi=10.3144%2fexpresspolymlett.2023.71&partnerID=40&md5=adcc14241fb2e56fc067ab0e5d9396d2
The use of machine learning to fine-tune the properties of materials is a remarkable achievement in the 21st century. Three machine learning (ML) methods were used to fine-tune and optimize the impact strength of polylactic acid (PLA) with different plasticizers: KNN (K-nearest neighbors), SVR (Support Vector Regression), and ANN (artificial neural net-works). The results demonstrated that, though ANN reached a higher R2 score of 0.901 than the other two ML methods, KNN, with an R2 score of 0.839, showed more stability than ANN. Based on the current research, KNN is recommended for experimentalists to fine-tune the impact strength of variational plasticizers. The experiment study case with polyethylene glycol 1000 (PEG1000) and octyl epoxy stearate (OES) plasticizer showed good agreement and prediction with experiments. It even showed the fine-tuned impact strength as a function of plasticizer content results, which cannot be achieved by only experiments. © BMEPT.
BME-PT and GTE
1788618X
English
Article
All Open Access; Gold Open Access
author Fatriansyah J.F.; Kustiyah E.; Surip S.N.; Federico A.; Pradana A.F.; Handayani A.S.; Jaafar M.; Dhaneswara D.
spellingShingle Fatriansyah J.F.; Kustiyah E.; Surip S.N.; Federico A.; Pradana A.F.; Handayani A.S.; Jaafar M.; Dhaneswara D.
Fine-tuning optimization of poly lactic acid impact strength with variation of plasticizer using simple supervised machine learning methods
author_facet Fatriansyah J.F.; Kustiyah E.; Surip S.N.; Federico A.; Pradana A.F.; Handayani A.S.; Jaafar M.; Dhaneswara D.
author_sort Fatriansyah J.F.; Kustiyah E.; Surip S.N.; Federico A.; Pradana A.F.; Handayani A.S.; Jaafar M.; Dhaneswara D.
title Fine-tuning optimization of poly lactic acid impact strength with variation of plasticizer using simple supervised machine learning methods
title_short Fine-tuning optimization of poly lactic acid impact strength with variation of plasticizer using simple supervised machine learning methods
title_full Fine-tuning optimization of poly lactic acid impact strength with variation of plasticizer using simple supervised machine learning methods
title_fullStr Fine-tuning optimization of poly lactic acid impact strength with variation of plasticizer using simple supervised machine learning methods
title_full_unstemmed Fine-tuning optimization of poly lactic acid impact strength with variation of plasticizer using simple supervised machine learning methods
title_sort Fine-tuning optimization of poly lactic acid impact strength with variation of plasticizer using simple supervised machine learning methods
publishDate 2023
container_title Express Polymer Letters
container_volume 17
container_issue 9
doi_str_mv 10.3144/expresspolymlett.2023.71
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164596409&doi=10.3144%2fexpresspolymlett.2023.71&partnerID=40&md5=adcc14241fb2e56fc067ab0e5d9396d2
description The use of machine learning to fine-tune the properties of materials is a remarkable achievement in the 21st century. Three machine learning (ML) methods were used to fine-tune and optimize the impact strength of polylactic acid (PLA) with different plasticizers: KNN (K-nearest neighbors), SVR (Support Vector Regression), and ANN (artificial neural net-works). The results demonstrated that, though ANN reached a higher R2 score of 0.901 than the other two ML methods, KNN, with an R2 score of 0.839, showed more stability than ANN. Based on the current research, KNN is recommended for experimentalists to fine-tune the impact strength of variational plasticizers. The experiment study case with polyethylene glycol 1000 (PEG1000) and octyl epoxy stearate (OES) plasticizer showed good agreement and prediction with experiments. It even showed the fine-tuned impact strength as a function of plasticizer content results, which cannot be achieved by only experiments. © BMEPT.
publisher BME-PT and GTE
issn 1788618X
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
_version_ 1809677887203180544

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