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Temperature variation during continuous laser-assisted adjacent hoop winding of type-IV pressure vessels: An experimental analysis

: Zaami, Amin; Schäkel, Martin; Baran, Ismet; Bor, Ton C.; Janssen, Henning; Akkerman, Remko

Fulltext ()

Journal of composite materials : JCM 54 (2020), No.13, pp.1717-1739
ISSN: 0021-9983
ISSN: 1530-793X
European Commission EC
H2020; 678875; ambliFibre
Journal Article, Electronic Publication
Fraunhofer IPT ()
laser-assisted tape winding; thermoplastic composites; experimental analysis; coefficient of variation

Laser-assisted tape winding is an automated process to produce tubular or tube-like continuous fiber-reinforced polymer composites by winding a tape around a mandrel or liner. Placing additional layers on a previously heated substrate and variation in material and process parameters causes a variation in the bonding temperature of fiber-reinforced thermoplastic tapes which need to be understood and described well in order to have a reliable manufacturing process. In order to quantify the variation in this critical bonding temperature, a comprehensive temperature analysis of an adjacent hoop winding process of type-IV pressure vessels is performed. A total of five tanks are manufactured in which three glass/HDPE tapes are placed on an HDPE liner. The tape and substrate temperatures, roller force and tape feeding velocity are measured. The coefficient of variation for each round is characterized for the first time. According to the statistical analysis, the coefficient of variation in substrate temperature is found to be approximately 4.88.8% which is larger than the coefficient of variation of the tape temperature which is 2.17.8%. The coefficient of variations of the substrate temperatures in the third round decrease as compared with the coefficient of variations in the second round mainly due to the change in gap/overlap behavior of the deposited tapes. Fourier and thermographic analysis evince that the geometrical disturbances such as unroundness and eccentricity have a direct effect on the temperature variation. In addition to the temperature feedback control, a real-time object detection technique with deep learning algorithms can be used to mitigate the unwanted temperature variation and to have a more reliable thermal history.