Research on sock knitting parameters based on a foot model

Abstract

Traditional custom-made socks primarily focus on ensuring the accuracy of key metrics such as foot length and height, failing to address the demand for more intricate customization. The human foot model encapsulates precise data essential for designing custom socks. This study proposes a methodology to derive accurate process parameters based on user-specific foot models and input them into sock-knitting machinery to produce customized socks. Initially, this study introduces an algorithm designed to generate customized sock models derived from foot models. The proposed algorithm leverages the alpha wrapping technique, augmented by grid smoothing and segmentation methods, to construct the wrapping model of the foot. By analyzing the customized socks, this study introduces fit and style indicators to assess the generated wrapping model. Based on these metrics, an evaluation function for the simulated annealing algorithm is constructed, enabling automated adjustment of alpha wrapping algorithm parameters to create customized sock models that meet predefined criteria. The system subsequently simulates the knitting process by iteratively sampling the model’s surface, treating sampling points as coils to progressively generate knitting paths aligned with the model’s geometric attributes. These paths are then converted into machine-knitting process parameters. Finally, the process parameters were input into a sock machine to fabricate a knitted sock. A comparison of the sample’s dimensions with the foot model revealed an error margin below 6.5%. In addition, density changes between the flat state and the wearing state of the knitted sock averaged under 7.7%, affirming the method’s validity.