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• W2784445644 abstract "In recent years the consumption of polymer based composites in many engineeringfields where friction and wear are critical issues has increased enormously. Satisfyingthe growing industrial needs can be successful only if the costly, labor-intensive andtime-consuming cycle of manufacturing, followed by testing, and additionally followedby further trial-and-error compounding is reduced or even avoided. Therefore, theobjective is to get in advance as much fundamental understanding as possible of theinteraction between various composite components and that of the composite againstits counterface. Sliding wear of polymers and polymer composites involves verycomplex and highly nonlinear processes. Consequently, to develop analytical modelsfor the simulation of the sliding wear behavior of these materials is extremely difficultor even impossible. It necessitates simplifying hypotheses and thus compromisingaccuracy. An alternative way, discussed in this work, is an artificial neural networkbased modeling. The principal benefit of artificial neural networks (ANNs) is their abilityto learn patterns through a training experience from experimentally generated datausing self-organizing capabilities.Initially, the potential of using ANNs for the prediction of friction and wear propertiesof polymers and polymer composites was explored using already published frictionand wear data of 101 independent fretting wear tests of polyamide 46 (PA 46) composites.For comparison, ANNs were also applied to model the mechanical propertiesof polymer composites using a commercial data bank of 93 pairs of independent Izodimpact, tension and bending tests of polyamide 66 (PA 66) composites. Differentstages in the development of ANN models such as selection of optimum networkconfiguration, multi-dimensional modeling, training and testing of the network wereaddressed at length. The results of neural network predictions appeared viable andvery promising for their application in the field of tribology.A case example was subsequently presented to model the sliding friction and wearproperties of polymer composites by using newly measured datasets of polyphenylenesulfide (PPS) matrix composites. The composites were prepared by twinscrewextrusion and injection molding. The dataset investigated was generated frompin-on-disc testing in dry sliding conditions under various contact pressures and sliding speeds. Initially the focus was placed on exploring the possible synergistic effectsbetween traditional reinforcements and particulate fillers, with special emphasis onsub-micro TiO2 particles (300 nm average diameter) and short carbon fibers (SCFs).Subsequently, the lubricating contributions of graphite (Gr) and polytetrafluoroethylene(PTFE) in these multiphase materials were also studied. ANNs were trainedusing a conjugate gradient with Powell/Beale restarts (CGB) algorithm as well as avariable learning rate backpropagation (GDX) algorithm in order to learn compositionpropertyrelationships between the inputs and outputs of the system. Likewise, theinfluence of the operating parameters (contact pressure (p) and sliding speed (v))was also examined. The incorporation of short carbon fibers and sub-micro TiO2particles resulted in both a lower friction and a great improvement in the wear resistanceof the PPS composites within the low and medium pv-range. The mechanicalcharacterization and surface analysis after wear testing revealed that this beneficialtribological performance could be explained by the following phenomena: (i)enhanced mechanical properties through the inclusion of short carbon fibers, (ii)favorable protection of the short carbon fibers by the sub-micro particles diminishingfiber breakage and removal, (iii) self-repairing effects with the sub-micro particles, (iv)formation of quasi-spherical transfer particles free to roll at the tribological contact.Still, in the high pv-range stick-slip sliding motion was observed with these hybridmaterials. The adverse stick-slip behavior could be effectively eliminated through theadditional inclusion of solid lubricant reservoirs (Gr and PTFE), analogous to thelubricants used in real ball bearings. Likewise, solid lubricants improved the wear resistanceof the multiphase system PPS/SCF/TiO2 in the high pv-range (≥ 9 MPa·m/s).Yet, their positive effect, especially that of graphite, was limited up to certain volumefraction and loading conditions. The optimum results were obtained by blendingcomparatively low amounts of Gr and PTFE (≈ 5 vol.% from each additive). An introductionof softer sub-micro particles did not bring the desired ball bearing effect andfiber protection. The ANN prediction profiles for PPS tribo-compounds exhibited verygood or even perfect agreement with the measured results demonstrating that thetarget of achieving a well trained network was reached. The results of employing avalidation test dataset indicated that the trained neural network acquired enoughgeneralization capability to extend what it has learned about the training patterns todata that it has not seen before from the same knowledge domain. Optimal brain surgeon (OBS) algorithm was employed to perform pruning of the networktopology by eliminating non-useful weights and bias in order to determine if theperformance of the pruned network was better than the fully-connected network.Pruning resulted in accuracy gains over the fully-connected network, but inducedhigher computational cost in coding the data in the required format. Within an importanceanalysis, the sensitivity of the network response variable (frictional coefficientor specific wear rate) to characteristic mechanical and thermo-mechanical input variableswas examined. The goal was to study the relationships between the diverseinput variables and the characteristic tribological parameters for a better understandingof the sliding wear process with these materials. Finally, it was demonstrated thatthe well-trained networks might be applied for visualization what will happen if a certainfiller is introduced into a composite, or what the impacts of the testing conditionson the frictional coefficient and specific wear rate are. In this way, they might be ahelpful tool for design engineers and materials experts to explore materials and tomake reasoned selection and substitution decisions early in the design phase, whenthey incur least cost." @default.
• W2784445644 created "2018-02-02" @default.
• W2784445644 creator A5008452031 @default.
• W2784445644 date "2010-03-08" @default.
• W2784445644 modified "2023-09-23" @default.
• W2784445644 title "Sliding Friction and Wear of Polyphenylene Sulfide Matrix Composites: Experimental and Artificial Neural Network Approach" @default.
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