Micro-mechanical predictive modelling as an aid to CAD based analysis of composite sporting equipment

The sport and leisure industry in New Zealand (NZ) has the potential to become a major user of composite materials. Given the size of NZ industry, design and manufacturing strategies based on virtual engineering should be developed to suit NZ requirements. Virtual methods use computer aided engineering capabilities to find faults, explore alternatives and optimise product performance before detailed design or prototyping. When doing computer aided simulation the required mechanical properties of individual reinforcement and matrix components are well documented. However, the mechanical properties of composite materials are not as simple to obtain. Micro-mechanical modelling could therefore be used to aid the design and development of composite equipment, where mechanical properties are unknown. In this study, solids modelling was used to produce an analog model of a composite, and it was found that it lead to reductions in file size and simulation time. Representing a composite with an analog model implies that the behavioural characteristics are modelled, but not the physical characteristics of the individual components. Three micro-mechanical models were developed to predict the flexural modulus of composite materials, based on perfect, partial and no adhesion. It was found that the partial adhesion model was both practical and consistently accurate. The partial adhesion model accounted for adhesion between components by considering an 'effective shear value' at the interface. Validation of the models was done by flexural testing injection moulded samples of glass, wood and carbon fibre reinforced polyethylene. It was shown that the adhesion coefficient range was 0.1 for carbon fibre, 0.5 for glass fibre and 0.9 for the wood fibre composites. It was concluded that the adhesion coefficient is crucial and it is recommended that further work is done to validate effective shear values by empirical means. The predicted flexural modulus values were used to enable finite element simulation of modelled analog beams as well as commercial kayak paddles. It was determined that accurate simulation is possible for composite equipment using the partial adhesion model

Modeling and simulation in tribology across scales: An overview

This review summarizes recent advances in the area of tribology based on the outcome of a Lorentz Center workshop surveying various physical, chemical and mechanical phenomena across scales. Among the main themes discussed were those of rough surface representations, the breakdown of continuum theories at the nano- and micro-scales, as well as multiscale and multiphysics aspects for analytical and computational models relevant to applications spanning a variety of sectors, from automotive to biotribology and nanotechnology. Significant effort is still required to account for complementary nonlinear effects of plasticity, adhesion, friction, wear, lubrication and surface chemistry in tribological models. For each topic, we propose some research directions

Numerical and Experimental Study on the Friction of Complex Surfaces

Whenever two bodies are in contact due to a normal load and one is sliding against the other, a tangential force arises, as opposed to the motion. This force is called friction force and involves different mechanisms, such as asperity interactions, energy dissipation, chemical and physical alterations of the surface topography and wear. The friction coefficient is defined as the ratio between the friction force and the applied normal load. Despite this apparently simple definition, friction appears to be a very complex phenomenon, which also involves several aspects at both the micro- and nano-scale, including adhesion and phase transformation. Moreover, it plays a key role in a variety of systems, and must be either enhanced (e.g. for locomotion) or minimized (e.g. in bearings), depending on the application. Considering friction as a multiscale problem, an analytical model has been proposed, starting from the literature, to describe friction in the presence of anisotropy, adhesion and wear between surfaces with hierarchical structures, e.g. self-similar. This model has been implemented in a MATLAB code for the design of the tribological properties of hierarchical surfaces and has been applied to study the ice friction, comparing analytical predictions with experimental tests. Furthermore, particular isotropic or anisotropic surface morphologies (e.g., microholes of different shapes and sizes) has been investigated for their influence to the static and dynamic friction coefficients with respect to a flat counterpart. In particular, it has been proved that the presence of grooves on surfaces could decrease the friction coefficients and thus reduce wear and energy dissipation. Experimental tests were performed with a setup realized ad hoc and the results were compared with full numerical simulations. If patterned surfaces showed that they can reduce sliding friction, other applications could require an increase in energy dissipation, e.g. to enhance the toughness of microfibers. Specifically, the applied method consists of introducing sliding frictional elements (sliding knots) in biological (silkworm silk, natural or degummed) and synthetic fibres, reproducing the concept of molecules, where the sacrificial bonds provide higher toughness to the molecular backbone, with a hidden length, which occurs after their breakage. A variety of slip knot topologies with different unfastening mechanisms have been investigated, including even complex knots usually adopted in the textile industry. The knots were made by manipulation of fibres with tweezers and the resulting knotted fibres were characterized through nanotensile tests to obtain their stress-strain curve until failure. The presence of sliding knots strongly increases the dissipated energy per unit mass, without compromising the structural integrity of the fibre itself

Triboinformatic Approaches for Surface Characterization: Tribological and Wetting Properties

Tribology is the study of surface roughness, adhesion, friction, wear, and lubrication of interacting solid surfaces in relative motion. In addition, wetting properties are very important for surface characterization. The combination of Tribology with Machine Learning (ML) and other data-centric methods is often called Triboinformatics. In this dissertation, triboinformatic methods are applied to the study of Aluminum (Al) composites, antimicrobial, and water-repellent metallic surfaces, and organic coatings.Al and its alloys are often preferred materials for aerospace and automotive applications due to their lightweight, high strength, corrosion resistance, and other desired material properties. However, Al exhibits high friction and wear rates along with a tendency to seize under dry sliding or poor lubricating conditions. Graphite and graphene particle-reinforced Al metal matrix composites (MMCs) exhibit self-lubricating properties and they can be potential alternatives for Al alloys in dry or starved lubrication conditions. In this dissertation, artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and hybrid ensemble algorithm-based ML models have been developed to correlate the dry friction and wear of aluminum alloys, Al-graphite, and Al-graphene MMCs with material properties, the composition of alloys and MMCs, and tribological parameters. ML analysis reveals that the hardness, sliding distance, and tensile strength of the alloys influences the COF most significantly. On the other hand, the normal load, sliding speed, and hardness were the most influential parameters in predicting wear rate. The graphite content is the most significant parameter for friction and wear prediction in Al-graphite MMCs. For Al-graphene MMCs, the normal load, graphene content, and hardness are identified as the most influential parameters for COF prediction, while the graphene content, load, and hardness have the greatest influence on the wear rate. The ANN, KNN, SVM, RF, and GBM, as well as hybrid regression models (RF-GBM), with the principal component analysis (PCA) descriptors for COF and wear rate were also developed for Al-graphite MMCs in liquid-lubricated conditions. The hybrid RF-GBM models have exhibited the best predictive performance for COF and wear rate. Lubrication condition, lubricant viscosity, and applied load are identified as the most important variables for predicting wear rate and COF, and the transition from dry to lubricated friction and wear is studied. The micro- and nanoscale roughness of zinc (Zn) oxide-coated stainless steel and sonochemically treated brass (Cu Zn alloy) samples are studied using the atomic force microscopy (AFM) images to obtain the roughness parameters (standard deviation of the profile height, correlation length, the extreme point location, persistence diagrams, and barcodes). A new method of the calculation of roughness parameters involving correlation lengths, extremum point distribution, persistence diagrams, and barcodes are developed for studying the roughness patterns and anisotropic distributions inherent in coated surfaces. The analysis of the 3×3, 4×4, and 5×5 sub-matrices or patches has revealed the anisotropic nature of the roughness profile at the nanoscale. The scale dependency of the roughness features is explained by the persistence diagrams and barcodes. Solid surfaces with water-repellent, antimicrobial, and anticorrosive properties are desired for many practical applications. TiO2/ZnO phosphate and Polymethyl Hydrogen Siloxane (PMHS) based 2-layer antimicrobial and anticorrosive coatings are synthesized and applied to steel, ceramic, and concrete substrates. Surfaces with these coatings possess complex topographies and roughness patterns, which cannot be characterized completely by the traditional analysis. Correlations between surface roughness, coefficient of friction (COF), and water contact angle for these surfaces are obtained. The hydrophobic modification in anticorrosive coatings does not make the coated surfaces slippery and retained adequate friction for transportation application. The dissertation demonstrates that Triboinformatic approaches can be successfully implemented in surface science, and tribology and they can generate novel insights into structure-property relationships in various classes of materials

Compressive strength of delaminated aerospace composites

An efficient analytical model is described which predicts the value of compressive strain below which buckle-driven propagation of delaminations in aerospace composites will not occur. An extension of this efficient strip model which accounts for propagation transverse to the direction of applied compression is derived. In order to provide validation for the strip model a number of laminates were artificially delaminated producing a range of thin anisotropic sub-laminates made up of 0°, ±45° and 90° plies that displayed varied buckling and delamination propagation phenomena. These laminates were subsequently subject to experimental compression testing and nonlinear finite element analysis (FEA) using cohesive elements. Comparison of strip model results with those from experiments indicates that the model can conservatively predict the strain at which propagation occurs to within 10 per cent of experimental values provided (i) the thin-film assumption made in the modelling methodology holds and (ii) full elastic coupling effects do not play a significant role in the post-buckling of the sub-laminate. With such provision, the model was more accurate and produced fewer non-conservative results than FEA. The accuracy and efficiency of the model make it well suited to application in optimum ply-stacking algorithms to maximize laminate strength.</jats:p