Multilayer nonwoven lining materials made of wool and cotton for clothing and footwear

Five types of multilayer nonwovens for clothing and footwear parts were obtained by the adhesive bonding method. The thickest middle layer of the material consists of evenly laid coarse camel or sheep fibers or of reconstituted cotton fibers from flaps, the upper and lower layers consist of knitwear, and polymer adhesive is located between the layers. The layers are bonded by thermal pressing at a temperature of 150 ± 5°C for 2.0 ± 0.2 min. The microstructure and morphology of fibers, polymer adhesive, and multilayer nonwoven fabric were investigated by FT-IR spectroscopy, SEM, and X-ray phase analysis. The chemical interaction between wool fibers and polymer adhesive, the geometric dimensions and shape of the fibers, the structure and morphology of the cross section of the layers of the material, and the change in the degree of crystallinity of the material have been established. The investigated coarse and thick fibers of camel and sheep wool are more suitable for the production of nonwoven textile material. In the process of thermal exposure, the molten polymer diffuses into the structure of the nonwoven layer and knitted fabric. The diffusion and excellent adhesion of the molten polymer to the fibers ensures the solidity and strength of the composite. The developed design provides high strength of the material as a whole and adhesive strength between layers, high heat-retaining properties, and the use of a mesh adhesive film provides sufficient air and vapor permeability.

Novel process for suppressing orange peel formation in polymer laser sintering through pretreatment with low-power laser irradiation

Purpose This paper aims to describe a new process for suppressing the formation of orange peel, which is a polymer laser sintering (LS) process error. Design/methodology/approach The target for controlling the suppression of orange peel is securing the contact between the molten polymer and the surrounding powder. The authors set the powder bed temperature closer to the melting temperature than that for a typical LS. Alternatively, the authors use a low-power laser to irradiate the powder bed surrounding the parts being built. The surface finish of the built parts was evaluated using a three-dimensional scanner. Findings Both approaches were effective in suppressing orange peel. From the viewpoint of reusability of the used powder, the process that includes low-power laser irradiation is practical. The presence or absence of contact between the surrounding powder and the molten polymer determines whether the orange peel is formed. Research limitations/implications The authors have not tested orange peel suppression for complex shapes. Originality/value The authors have demonstrated a concrete process that can suppress orange peel formation even for powders with low melt-flow rates. Furthermore, a mechanism for the formation/suppression of orange peel based on the experimental results was proposed.

3D-Printing Methods for Crystalline Polyetheretherketone

The article is a literature review on 3D-printing of crystalline polyether ether ketone by the methods of layer-by-layer deposition of molten polymer filament (FDM) and selective laser sintering (SLS). The influence of printing technological modes and material properties (fluidity, morphology, etc.) on the quality of the products is considered.

Fusion of Near-Infrared and Raman Spectroscopy for In-Line Measurement of Component Content of Molten Polymer Blends

Spectral measurement techniques, such as the near-infrared (NIR) and Raman spectroscopy, have been intensively researched. Nevertheless, even today, these techniques are still sparsely applied in industry due to their unpredictable and unstable measurements. This paper put forward two data fusion strategies (low-level and mid-level fusion) for combining the NIR and Raman spectra to generate fusion spectra or fusion characteristics in order to improve the in-line measurement precision of component content of molten polymer blends. Subsequently, the fusion value was applied to modeling. For evaluating the response of different models to data fusion strategy, partial least squares (PLS) regression, artificial neural network (ANN), and extreme learning machine (ELM) were applied to the modeling of four kinds of spectral data (NIR, Raman, low-level fused data, and mid-level fused data). A system simultaneously acquiring in-line NIR and Raman spectra was built, and the polypropylene/polystyrene (PP/PS) blends, which had different grades and covered different compounding percentages of PP, were prepared for use as a case study. The results show that data fusion strategies improve the ANN and ELM model. In particular, mid-level fusion enables the in-line measurement of component content of molten polymer blends to become more accurate and robust.