Carpet manufacturers certify their products for end-use applications by evaluating the wear behavior of their carpets in mechanical experiments. Currently, this process is performed by visual inspection, suffering from subjective gathers that limit reliability. To automate this process, we propose the use of image processing techniques, specifically of local binary pattern (LBP) statistics. Such statistics are tolerant against illumination changes, can be easily implemented, and perform well when combined with a symmetrized adaptation of the Kullback—Leibler divergence. As a main innovation, we extend the existing rotationally invariant LBPs by including ‘mirror’ and ‘complement’ invariants. We show an accurately improved and more reliable estimation of the degree of wear in worn carpets. The evaluation is performed on four digital reference scales, each containing eight pairs of images comparing transitional degrees of wear to the original appearance. Additionally, the texture changes due to distortions of the pile yarn tufts are enhanced by choosing a suitable scale factor per reference. We validate the findings using six physical reference scales, each containing four pairs of images. In both references, linear correlations of over 0.89 are demonstrated between the degrees of wear and extracted features from the images. These findings justify the use of the proposed LBP extensions in a first approach towards an automated low-cost inspection system for carpet wear at low computation cost.

This paper deals with the three-dimensional computer simulation of the weft insertion process as a possible solution to avoid costly weaving trials when researching this process. A three-dimensional mathematical model of the yarn was developed, in which the behavior of the yarn was described by the Second Law of Newton. For this mathematical model, a second order differential equation needed to be solved. To this end, an explicit integration using Euler’s method was chosen. In order to validate the model, high-speed camera recordings were performed on a test stand and compared with simulations of the same test stand. A very good resemblance was found between simulation and camera recordings. This is the first time that a three-dimensional model for the weft insertion on air-jet looms has been successfully tested.

The main nozzle of an air jet loom plays an essential role in the weft insertion process. This role involves sucking the weft yarn from the prewinder and launching it into the reed. Simulating the dynamic behavior of the weft yarn inside the main nozzle involves fluid–structure interaction (FSI). In this work, one-way and two-way FSI simulations of air flow–yarn interaction inside a main nozzle have been performed. A three-dimensional model of the flexible weft yarn, consisting of a chain of line segments, and a two-dimensional axisymmetric model of the supersonic flow have been developed and coupled to perform these simulations. The results of the simulations are compared quantitatively and qualitatively with experimental results. Good agreement has been found between the results of the two-way FSI simulations and the experiment. The coupled fluid and structure models provide an effective numerical tool to optimize the geometry of the main nozzle based on the calculated motion and speed of the weft yarn.

In this paper we present a novel 3D scanner to capture the texture characteristics of worn carpets into images of the depth. We first compare our proposed scanner to a Metris scanner previously attempted for this application. Then, we scan the surface of samples from the standard EN1471 using our proposed scanner. We found that our proposed scanner offers additional benefits because it has been specifically designed for carpets, performing faster, cheaper, better and also a lot more suitable for darker carpets. The results of this approach give optimistic expectations in the automation of the label assessment dealing with multiple types of carpets.

No abstract