SummaryThis study constructs a neural network (NN) model to quantify adhesion from atomic force microscopy (AFM) data. AFM data contain five-point force-distance values. A total of 760 observations are used to build NN model. To train the network, AFM tip-sample distance data, percentage of lime, type and percentage of polymer and asphalt chemical functional groups are given as inputs and AFM force as an output. To select the NN architecture, one and two hidden layers with varying neurons are tried with 10 input nodes in the input layer and 5 output nodes in the output layer. Two hidden layers with 9 and 17 nodes in the first and second layer, respectively, show the best performance. A 10-9-17-5 NN is selected as the final structure of the NN model. Test results for the trained model show good prediction ability. The model is further applied to evaluate the effect of five different percentages of lime on the adhesion of asphalt. Results show that increase in the percentage of lime is very effective at reducing moisture damage in a styrene butadiene polymer modified asphalt sample. However, increase in lime percentage above 1.5% does not help reduce moisture damage in the styrene butadiene styrene polymer modified sample.
Nanoindentation of thin film-thick substrate system is a commonly employed tool to measure the mechanical properties of materials. Finite Element Method (FEM) simulation of nanoindentation experiment can overcome the expense and limitations of sophisticated test procedure. This study focused on the FEM simulation of nanoindentation test in ABAQUS environment to check the effects of film-substrate material properties and geometry. The indentation process in concern involves a two dimensional axisymmetric model where a thin film is placed above a substrate and indented by a rigid indenter for a specific friction condition. Modulus of elasticity and hardness of thin film has been calculated from analysis results using empirical relationship. For this study, two types of thin film properties i.e. elastic-perfectly plastic and elasto-plastic with specific strain hardening condition are taken for consideration. Firstly, different elastic substrate materials have been used under elastic-perfectly plastic thin film to observe the substrate strength effects. The analysis has been conducted for four different indentation depths to incorporate the influence of depth of penetration also. Secondly, similar analysis was performed for strain-hardening film material for all substrate strength to compare the behavior with perfectly plastic case. Finally, thickness of substrate layer has also been varied to observe the effect of substrate thickness under nanoindentation test. The simulation result shows that substrate strength effect is pronounced on film modulus determination whereas hardness is not significantly sensitive to this effect. Substrate modulus with magnitude smaller or near film modulus can predict reasonable value of film modulus whereas high strength substrate modulus i.e. rigid body as a substrate produces extremely high film modulus. Indentation derived film hardness affects significantly the elastic modulus due to incorporation of strain hardening in thin film properties. In addition, calculated film properties increase with the increment of indentation depth but show negligible change due to the variation of substrate thickness.
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