Thermally controlled shape programming via image-based optimization towards distortion-reduced composite curing

Abstract

The complex mechanisms involved in thermosetting composite curing process contribute to the cure-induced distortion (CID) that deteriorates load-bearing capacity as well as in-service life of the assembled component. The emergence of novel approaches for flexible thermal control, such as microwave or self-resistance heating devices, enables shape manipulation through customized temperature field programming, which opens up a new avenue for reducing CID. The determination of such temperature field is however a typical inverse problem that conventionally requires accurate modeling of the forward curing process together with an iterative solver, which becomes intractable due to the high-dimensional space required to identify a temperature field. This paper proposed an image-based optimization scheme to deliver a distortion-reduced temperature field. A simply yet effective thermomechanical relationship is primarily established to generate a grayscale image that encodes a nominal temperature field. The adaptive thresholding algorithm based on image processing is then developed to regularize the temperature field, maintaining global features while eliminating high-frequency outliers, in order to comply with applicable thermal control requirements. Experimental results demonstrate noticeable reduction of the CID distribution, where the mean CID could be reduced by 25–50%, as verified in three typical freeform parts using optimized temperature field.