Influence of (Sub) Structure Development within Rotary Swaged Al–Cu Clad Conductors on Skin Effect during Transfer of Alternating Current

The nature of alternating current transfer via metallic materials is specific, since the current density tends to be inhomogeneous across the cross-section of the conductor and the skin effect tends to occur. However, the influence of this effect on the behaviour of the conductor can be optimized via the design and fabrication procedures. The study presents innovative design of an Al–Cu clad conductor, which is supposed to affect favourably the influence of the skin effect. The clad conductors of various diameters (20 mm, 15 mm, and 10 mm) were fabricated via rotary swaging at room temperature, and their electric characteristics were subsequently examined both experimentally and via numerical simulations. Structure analyses performed to document the effects of the swaging technology on the development of substructure and characteristic structural features were carried out by scanning electron microscopy (electron backscatter diffraction analyses), and transmission electron microscopy. The results showed that the design of the composite has a favourable effect on decreasing the power losses during alternating current transfer and that the substructure development affected favourably the electric resistance of the conductor. The highest electric resistance was measured for the composite conductor with the diameter of 20 mm (1.8% increase compared to electric resistance during transfer of direct current). This value then decreased to 0.6%, and 0.1% after swaging down to the diameters of 15 mm, and 10 mm; the 10 mm composite featured the finest grains, partially restored structure, and texture randomization compared to the 20 mm and 15 mm composites. Manufacturing of the clad composite via rotary swaging imparted advantageous combinations of both the electric and mechanical properties, as swaging also introduced increased microhardness.

Optimization of Foamed Polyurethane/Ground Tire Rubber Composites Manufacturing

The development of the automotive sector and the increasing number of vehicles all over the world poses multiple threats to the environment. One of them, probably not so emphasized as others, is the enormous amount of post-consumer car tires. Due to the potential fire threat, waste tires are considered as dangerous waste, which should not be landfilled, so it is essential to develop efficient methods of their utilization. One of the possibilities is their shredding and application of resulting ground tire rubber (GTR) as filler for polymer composites, which could take advantage of the excellent mechanical performance of car tires. Nevertheless, due to the poor compatibility with majority of polymer matrices, prior to the application, surface of GTR particles should be modified and activated. In the presented work, the introduction of thermo-mechanically modified GTR into flexible foamed polyurethane matrix was analyzed. Isocyanates can be found among the compounds applied during manufacturing of polyurethane foams, which are able to react and generate covalent bonds with the functional groups present on the surface of modified GTR. Such an effect can noticeably enhance the interfacial interactions and boost up the mechanical performance. Nevertheless, it requires the adjustment of formulations used during manufacturing of foams. Therefore, for better understanding of the process foams with varying isocyanate index (from 0.8 to 1.2) were prepared with and without taking into account the possible interactions with functional groups of GTR. For comparison, an unfilled matrix and composite containing deactivated GTR were also prepared.

COMPREHENSIVE PROPERTY DETERMINATION FOR FIBER-REINFORCED POLYMER COMPOSITES IN EXTRUSION DEPOSITION ADDITIVE MANUFACTURING—BAYESIAN VS DETERMINISTIC This work introduces both deterministic and Bayesian methodologies to simultaneously determine the elastic constants of the constituent polymer and the fiber orientation state in a short fiber-reinforced polymer (SFRP) composite based on a small number of experimental measurements of the composite properties. The ability of the Bayesian approach to calibrate uncertainties makes it a promising tool for enabling a probabilistic framework for composites manufacturing digital twins. The two methods that enable the reverse engineering of the orientation of the fibers and the in-situ polymer properties are compared. For the extrusion deposition additive manufacturing (EDAM) process and other SFRP composites processes (e.g. injection molding), extensive characterization efforts are currently required to develop composites manufacturing digital twins. To circumvent the extensive characterization required, Digimat© provides a suite of tools to reverse engineer material properties of SFRPs. However, Digimat© lacks a methodology to inversely determine the fiber orientation state and the constituent polymer properties simultaneously. To that end, this work presents both a deterministic and hierarchical Bayesian approaches to determine the polymer properties and the fiber orientation state simultaneously. The results indicate that both approaches provide a reliable framework for the reverse engineering process. The deterministic approach provides a more rapid, point estimate methodology, whereas the Bayesian approach provides a more comprehensive methodology that includes uncertainties in the reverse engineering process. This work introduces both deterministic and Bayesian methodologies to simultaneously determine the elastic constants of the constituent polymer and the fiber orientation state in a short fiber-reinforced polymer (SFRP) composite based on a small number of experimental measurements of the composite properties. The ability of the Bayesian approach to calibrate uncertainties makes it a promising tool for enabling a probabilistic framework for composites manufacturing digital twins. The two methods that enable the reverse engineering of the orientation of the fibers and the in-situ polymer properties are compared. For the extrusion deposition additive manufacturing (EDAM) process and other SFRP composites processes (e.g. injection molding), extensive characterization efforts are currently required to develop composites manufacturing digital twins. To circumvent the extensive characterization required, Digimat© provides a suite of tools to reverse engineer material properties of SFRPs. However, Digimat© lacks a methodology to inversely determine the fiber orientation state and the constituent polymer properties simultaneously. To that end, this work presents both a deterministic and hierarchical Bayesian approaches to determine the polymer properties and the fiber orientation state simultaneously. The results indicate that both approaches provide a reliable framework for the reverse engineering process. The deterministic approach provides a more rapid, point estimate methodology, whereas the Bayesian approach provides a more comprehensive methodology that includes uncertainties in the reverse engineering process.

This work introduces both deterministic and Bayesian methodologies to simultaneously determine the elastic constants of the constituent polymer and the fiber orientation state in a short fiber-reinforced polymer (SFRP) composite based on a small number of experimental measurements of the composite properties. The ability of the Bayesian approach to calibrate uncertainties makes it a promising tool for enabling a probabilistic framework for composites manufacturing digital twins. The two methods that enable the reverse engineering of the orientation of the fibers and the in-situ polymer properties are compared. For the extrusion deposition additive manufacturing (EDAM) process and other SFRP composites processes (e.g. injection molding), extensive characterization efforts are currently required to develop composites manufacturing digital twins. To circumvent the extensive characterization required, Digimat© provides a suite of tools to reverse engineer material properties of SFRPs. However, Digimat© lacks a methodology to inversely determine the fiber orientation state and the constituent polymer properties simultaneously. To that end, this work presents both a deterministic and hierarchical Bayesian approaches to determine the polymer properties and the fiber orientation state simultaneously. The results indicate that both approaches provide a reliable framework for the reverse engineering process. The deterministic approach provides a more rapid, point estimate methodology, whereas the Bayesian approach provides a more comprehensive methodology that includes uncertainties in the reverse engineering process.

A Generic Cost Estimating Approach for a Composite Manufacturing Process Assessment

Cost estimation helps build confidence in the feasibility of the development of novel manufacturing processes. This paper focuses on the exploration of the cost estimation for novel manufacturing processes for decision support. One of the aspects of estimation is building credibility around the analysis, especially, in the early stages of planning. Cost estimating guidelines provide a good overview of the cost estimation steps but there is a requirement for guidelines for cost estimation model development. Through building on an understanding of the cost estimation principles, as well as cost estimation modelling, a high-level generic approach for process cost estimation is proposed. Further, a demonstration of a cost estimation modelling approach used for composites manufacturing in the automotive sector is provided, outlining the steps in cost estimation model development.

Selective Laser Melting of Ti6Al4V-2%Hydroxyapatite Composites: Manufacturing Behavior and Microstructure Evolution

In this work, selective laser melting of Ti6Al4V (Ti64) and 2 wt.% hydroxyapatite (HA) composites was performed with the purpose of osseointegration enhancement and biological fixation between implants and bone tissue. The microstructural evolution and mechanical properties were analyzed by using X-ray diffraction (XRD), optical microscopy (OM), a scanning electron microscope (SEM) equipped with (EDX) and (EBSD) systems, microhardness, nanoindentation, and tensile testing. The results showed that the Ti64-2%HA composite components exhibited complicated manufacturing behavior, which could be correlated with the decomposition of HA. The microstructure was found to mainly consist of α Ti with a small amount of HA distributed along grain boundaries. Furthermore, the interaction between Ti64 and HA leading to the formation of Ti3P, TixO, P, and CaTiO3 phases, resulted in poor tensile properties, as compared to pure Ti64 components. Conversely, the tensile properties of SLM Ti64-2%HA composite components were significantly higher than human bone reported previously in the literature.

Classification of Textile Polymer Composites: Recent Trends and Challenges

Polymer based textile composites have gained much attention in recent years and gradually transformed the growth of industries especially automobiles, construction, aerospace and composites. The inclusion of natural polymeric fibres as reinforcement in carbon fibre reinforced composites manufacturing delineates an economic way, enhances their surface, structural and mechanical properties by providing better bonding conditions. Almost all textile-based products are associated with quality, price and consumer’s satisfaction. Therefore, classification of textiles products and fibre reinforced polymer composites is a challenging task. This paper focuses on the classification of various problems in textile processes and fibre reinforced polymer composites by artificial neural networks, genetic algorithm and fuzzy logic. Moreover, their limitations associated with state-of-the-art processes and some relatively new and sequential classification methods are also proposed and discussed in detail in this paper.