Concept for Predictive Quality in Carbon Fibre Manufacturing
Remarkable mechanical properties make carbon fibres attractive for many industrial applications. However, up to today, carbon fibres come with a significant environmental backpack, undermining their advantages in light of a strong demand for absolute sustainability of new industrial products. Conseq...
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MDPI AG
2024-11-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| Online Access: | https://www.mdpi.com/2504-4494/8/6/272 |
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| author | Sebastian Gellrich Thomas Groetsch Maxime Maghe Claudia Creighton Russell Varley Anna-Sophia Wilde Christoph Herrmann |
| author_facet | Sebastian Gellrich Thomas Groetsch Maxime Maghe Claudia Creighton Russell Varley Anna-Sophia Wilde Christoph Herrmann |
| author_sort | Sebastian Gellrich |
| collection | DOAJ |
| description | Remarkable mechanical properties make carbon fibres attractive for many industrial applications. However, up to today, carbon fibres come with a significant environmental backpack, undermining their advantages in light of a strong demand for absolute sustainability of new industrial products. Consequently, there is considerable demand for high-quality carbon fibre manufacturing, low waste production, or alternative precursor systems allowing minimization of environmental impacts. Therefore, this paper investigates the capabilities of data analytics with a special emphasis on predictive quality in order to advance the quality management of carbon fibre manufacturing. Although existing research supports the applicability of machine learning in carbon fibre production, there is a notable scarcity of case studies and a lack of a structured repetitive data analytics concept. To address this gap, the study proposes a holistic framework for predictive quality in carbon fibre manufacturing that outlines specific data analytics requirements based on the process properties of carbon fibre production. Additionally, it introduces a systematic method for processing trend data. Finally, a case study of polyacrylonitrile (PAN)-based carbon fibre manufacturing exemplifies the concept, giving indications on feature importance and sensitivity related to the expected fibre properties. Future research can build on the comprehensive overview of predictive quality potentials and its implementation concept by extending the underlying data set and investigating the transfer to alternative precursors. |
| format | Article |
| id | doaj-art-85ca7eb4c8ea4acf9db4ff0fc9c9ba04 |
| institution | Kabale University |
| issn | 2504-4494 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj-art-85ca7eb4c8ea4acf9db4ff0fc9c9ba042024-12-27T14:32:51ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942024-11-018627210.3390/jmmp8060272Concept for Predictive Quality in Carbon Fibre ManufacturingSebastian Gellrich0Thomas Groetsch1Maxime Maghe2Claudia Creighton3Russell Varley4Anna-Sophia Wilde5Christoph Herrmann6Institute of Machine Tools and Production Technology (IWF), Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, GermanyCarbon Nexus at the Institute for Frontier Materials, Deakin University, Geelong, VIC 3216, AustraliaCarbon Nexus at the Institute for Frontier Materials, Deakin University, Geelong, VIC 3216, AustraliaCarbon Nexus at the Institute for Frontier Materials, Deakin University, Geelong, VIC 3216, AustraliaCarbon Nexus at the Institute for Frontier Materials, Deakin University, Geelong, VIC 3216, AustraliaInstitute of Machine Tools and Production Technology (IWF), Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, GermanyInstitute of Machine Tools and Production Technology (IWF), Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, GermanyRemarkable mechanical properties make carbon fibres attractive for many industrial applications. However, up to today, carbon fibres come with a significant environmental backpack, undermining their advantages in light of a strong demand for absolute sustainability of new industrial products. Consequently, there is considerable demand for high-quality carbon fibre manufacturing, low waste production, or alternative precursor systems allowing minimization of environmental impacts. Therefore, this paper investigates the capabilities of data analytics with a special emphasis on predictive quality in order to advance the quality management of carbon fibre manufacturing. Although existing research supports the applicability of machine learning in carbon fibre production, there is a notable scarcity of case studies and a lack of a structured repetitive data analytics concept. To address this gap, the study proposes a holistic framework for predictive quality in carbon fibre manufacturing that outlines specific data analytics requirements based on the process properties of carbon fibre production. Additionally, it introduces a systematic method for processing trend data. Finally, a case study of polyacrylonitrile (PAN)-based carbon fibre manufacturing exemplifies the concept, giving indications on feature importance and sensitivity related to the expected fibre properties. Future research can build on the comprehensive overview of predictive quality potentials and its implementation concept by extending the underlying data set and investigating the transfer to alternative precursors.https://www.mdpi.com/2504-4494/8/6/272data analyticsmachine learningcarbon fibre manufacturingPANquality predictiontrend data pre-processing |
| spellingShingle | Sebastian Gellrich Thomas Groetsch Maxime Maghe Claudia Creighton Russell Varley Anna-Sophia Wilde Christoph Herrmann Concept for Predictive Quality in Carbon Fibre Manufacturing Journal of Manufacturing and Materials Processing data analytics machine learning carbon fibre manufacturing PAN quality prediction trend data pre-processing |
| title | Concept for Predictive Quality in Carbon Fibre Manufacturing |
| title_full | Concept for Predictive Quality in Carbon Fibre Manufacturing |
| title_fullStr | Concept for Predictive Quality in Carbon Fibre Manufacturing |
| title_full_unstemmed | Concept for Predictive Quality in Carbon Fibre Manufacturing |
| title_short | Concept for Predictive Quality in Carbon Fibre Manufacturing |
| title_sort | concept for predictive quality in carbon fibre manufacturing |
| topic | data analytics machine learning carbon fibre manufacturing PAN quality prediction trend data pre-processing |
| url | https://www.mdpi.com/2504-4494/8/6/272 |
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