The orientation and dispersion of nanoparticles can greatly influence the conductivity and mechanical properties of nanocomposites. In this study, the Polypropylene/ Carbon Nanotubes (PP/CNTs) nanocomposites were produced using three different molding methods, i.e., compression molding (CM), conventional injection molding (IM), and interval injection molding (IntM). Various CNTs content and shear conditions give CNTs different dispersion and orientation states. Then, three electrical percolation thresholds (4 wt.% CM, 6 wt.% IM, and 9 wt.% IntM) were obtained by various CNTs dispersion and orientations. Agglomerate dispersion (Adis), agglomerate orientation (Aori), and molecular orientation (Mori) are used to quantify the CNTs dispersion and orientation degree. IntM uses high shear to break the agglomerates and promote the Aori, Mori, and Adis. Large Aori and Mori can create a path along the flow direction, which lead to an electrical anisotropy of nearly six orders of magnitude in the flow and transverse direction. On the other hand, when CM and IM samples already build the conductive network, IntM can triple the Adis and destroy the network. Moreover, mechanical properties are also been discussed, such as the increase in tensile strength with Aori and Mori but showing independence with Adis. This paper proves that the high dispersion of CNTs agglomerate goes against forming a conductivity network. At the same time, the increased orientation of CNTs causes the electric current to flow only in the orientation direction. It helps to prepare PP/CNTs nanocomposites on demand by understanding the influence of CNTs dispersion and orientation on mechanical and electrical properties.

Aiming at the difficulty of temperature control in the laser cladding process of high-temperature nickel-based alloys, the influence of cladding parameters on the temperature of the molten pool, and the quality of the cladding layer were explored. Firstly, through the analysis of the finite element method, the Inconel718 single-pass cladding model was established on the surface of 45 steel by using parametric language and life–death element technology, the influence of different laser power and scanning speed on the temperature of the molten pool center was explored, and reasonable process parameters scope were selected. Secondly, taking the cladding parameters as independent variables, and taking the dilution rate and forming coefficient of the cladding layer as the response variables, using BBD (Box–Behnken Design) to design experiments the response surface analysis method was used to establish the regression prediction model of the cladding process parameters and response indicators, and the genetic algorithm was used to carry out multi-objective optimization to obtain the best results. The optimal parameter combination is a laser power of 1756 W, a scanning speed of 19.43 mm/s, and a powder feeding rate of 19.878 g/min. Finally, a multi-pass lap joint experiment was carried out with the optimal parameters, and it was found that the cladding layer has a dense and fine structure and a good metallurgical bond with the matrix, which can effectively guide the actual production.

This article introduces a one-step extrusion-based fused deposition modeling (FDM) approach for the challenging separation of polypropylene (PP) and polyethylene terephthalate (PET) during recycling. A shear screw printer (SSP) with shear elements was designed, and it was compared to a conventional single-screw printer (CSP) to investigate the differences in print stability, degradation levels, tensile performance, molecular orientation, and crystallization when preparing recycled PP and recycled PET blends. Although the retention effect of the SSP screw slightly increases the degradation of the blended rPP/rPET, the strong shear (2.6 × 10⁴ s⁻¹) applied near the extrusion exit improves the blending efficiency. The SSP also enhances molecular orientation, modulus of the parts, and reduces performance fluctuations. Additionally, the SSP has the potential to simplify the recycling process, enabling the transformation of blended recycled materials into products with just one melt process.

Fused Deposition Modeling (FDM), a widely-utilized additive manufacturing (AM) technology, has found significant favor among automotive manufacturers. Polypropylene (PP) compound is extensively employed in the production of automotive parts due to its superior mechanical properties and formability. However, aiming at the problem of poor dimensional accuracy of pure PP parts, the quality of products can be enhanced by optimizing the combination of processing parameters. In this paper, the dimensional accuracy of 3D-printed components made from pure PP material is investigated. Key influencing factors such as infill percentage, infill pattern, layer thickness, and extrusion temperature are considered. To gain a deeper understanding, fluid simulation is conducted, and mathematical models are established to correlate processing parameters with dimensional accuracy. Furthermore, the Taguchi's experiments are designed and the experimental data are subjected to rigorous Signal-to-Noise ratio and ANOVA analyses. Within the experimental range, the lower extrusion temperature, infill percentage and layer thickness yield the best dimensional accuracy. Considering the influence factors of X, Y and Z directions, the optimal processing parameters for PP prints using screw extrusion 3D printers are determined as follows: an extrusion temperature of 210 °C, an infill percentage of 40 %, a layer thickness of 0.3 mm, and a concentric circle infill pattern. This study provides reference value for the subsequent improvement of the dimensional accuracy of the printed parts.

Shear and thermal processing can greatly influence nanoparticles’ orientation and dispersion, affecting the nanocomposites’ conductivity and mechanical properties. The synergistic effects of shear flow and Carbon nanotubes (CNTs) nucleating ability on the crystallization mechanisms have been proven. In this study, Polylactic acid/Carbon nanotubes (PLA/CNTs) nanocomposites were produced by three different molding methods: compression molding (CM), conventional injection molding (IM), and interval injection molding (IntM). Solid annealing at 80 °C for 4 h and pre-melt annealing at 120 °C for 3 h was applied to research the CNTs’ nucleation effect and the crystallized volume exclusion effect on the electrical conductivity and mechanical properties. The volume exclusion effect only significantly impacts the oriented CNTs, causing the conductivity along the transverse direction to rise by about seven orders of magnitude. In addition, the tensile modulus of the nanocomposites decreases with the increased crystallinity, while the tensile strength and modulus decrease.