{'fraction_non_alphanumeric': 0.01816051552431166, 'fraction_numerical': 0.0, 'mean_word_length': 5.62015503875969, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Predicting dimensional distortions in roll forming of comingled polypropylene/glass fiber thermoplastic composites: On the effect of matrix viscoelasticity

Thermal deformations that occur during formation of long-fiber-reinforced composites have been a continued challenge for manufacturers as the final shape of a given part can be different from the original mold shape. The ensuing dimensional distortions can be difficult to predict due to complex thermo-mechanical behaviour of composite laminates during different forming cycles. This study intends to model the fundamental mechanisms that lead to thermal deformations during forming of a thermoplastic matrix composite comprised of comingled polypropylene and E-glass fibers. While the discussion is framed around a custom-design multi-stage roll-forming process, it is also relevant to a wider range of thermoplastic composites manufacturing processes. A methodology is developed to characterize the thermal mechanical behavior of the material, optimize the manufacturing process, and predict the magnitude of resulting spring-in angle due to thermal deformations. It is found that the process control parameters can be optimized first such that the crystallization of the matrix occurs at an ideal position along the forming line. Once the process is optimized, the developed numerical model, with a thermoelastic material behaviour, can give an adequate prediction of spring-in at the end of the process. Finally, through a comparative study, it is discussed how for other manufacturing processes, such as compression molding, including a thermoviscoelastic liquid/solid material behaviour may be required to yield accurate spring-in predictions.