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Tribology, the science of rubbing surfaces, is involved at micro and macro level in several aspects of life and technology. A finer understanding of this is becoming essential as the things are getting smaller, or where nano or bionic designs are involved; besides the domain of regular size or mega machines. "Inventi Impact: Tribology" is a multidisciplinary journal and aims to engage the researchers not only from engineering and technology, but from the life sciences as well.
Applying the ultrasonic machining in gear honing can improve honing speed, reduce cutting force, and avoid blocking. *ere are
two problems leading to the decrease of calculation accuracy in the traditional nonresonant theory of the ultrasonic gear honing.
One is that one-dimensional longitudinal vibration theory and two-dimensional theory cannot reflect the vibration characteristics
of ultrasonic horn and gear comprehensively. And, the other one is that the difference of the analysis dimension between the two
theories leads to mismatch of the coupling condition dimension between ultrasonic horn and gear. A free vibration analysis
through Chebyshev–Ritz method based on three-dimensional elasticity theory was presented to analyze the eigenfrequencies of
the horn-gear system in ultrasonic gear honing. In the method, the model of the horn-gear system was divided into four parts: a
solid circular plate, an annular plate, a solid cylinder, and a cone with hole. *e eigenvalue equations were derived by using
displacement coupling condition between each part under completely free boundary condition. It was found that the eigenfrequencies
were highly convergent through convergence study. *e hammering method for a modal experiment was used to test
the horn-gear systems’ eigenfrequencies. And, the finite element method was also applied to solve the eigenfrequencies. *rough a
comparative analysis of the frequencies obtained by these three methods, it showed that the results achieved by the Chebyshev–
Ritz method were close to those obtained from the experiment and finite element method. *us, it was feasible to use the
Chebyshev–Ritz method to solve the eigenfrequencies of the horn-gear system in ultrasonic gear honing....
When aluminum or its alloys are melted, considerable amounts of dross are produced. The alloy type and the method used in the production of aluminum products play an important role in the amount of dross that will result as a byproduct. The current needs of the Al industry as well as economic and environmental factors demand the recovery of the pure material that is lost during dross removal by simple and efficient methods that can be applied within the foundry. Most cases of Al recovery employ methods of dross compression at high temperatures. This investigation attempts to develop a mathematical model to characterize the efficiency of the recovery process that can be implemented for any dross collection method or even compression device, facilitating the direct comparison of recovery methods....
We have developed a novel instrument combining a glide tester with anAtomic ForceMicroscope (AFM) for hard disk drive (HDD)\r\nmedia defect test and analysis. The sample stays on the same test spindle during both glide test and AFM imaging without losing\r\nthe relevant coordinates. This enables an in situ evaluation with the high-resolution AFM of the defects detected by the glide test.\r\nThe ability for the immediate follow-on AFM analysis solves the problem of relocating the defects quickly and accurately in the\r\ncurrent workflow. The tool is furnished with other functions such as scribing, optical imaging, and head burnishing. Typical data\r\ngenerated from the tool are shown at the end of the paper. It is further demonstrated that novel experiments can be carried out on\r\nthe platform by taking advantage of the correlative capabilities of the tool....
The challenges in designing future head disk interface (HDI) demand efficient theoretical modeling tools with flexibility in\r\ninvestigating various combinations of perfluoropolyether (PFPE) and carbon overcoat (COC) materials. For broad range of time\r\nand length scales, we developedmultiscale/multiphysical modeling approach, which can bring paradigm-shifting improvements in\r\nadvanced HDI design. In this paper, we introduce our multiscale modeling methodology with an effective strategic framework for\r\nthe HDI system. Our multiscale methodology in this paper adopts a bottom to top approach beginning with the high-resolution\r\nmodeling, which describes the intramolecular/intermolecular PFPE-COC degrees of freedomgoverning the functional oligomeric\r\nmolecular conformations on the carbon surfaces. By introducing methodology for integrating atomistic/molecular/mesoscale levels\r\nvia coarse-graining procedures, we investigated static and dynamic properties of PFPE-COC combinations with variousmolecular\r\narchitectures. By bridging the atomistic and molecular scales, we are able to systematically incorporate first-principle physics into\r\nmolecular models, thereby demonstrating a pathway for designing materials based on molecular architecture. We also discussed\r\nfuture materials (e.g., graphene for COC, star-like PFPEs) and systems (e.g., heat-assisted magnetic recording (HAMR)) with\r\nhigher scale modeling methodology, which enables the incorporation of molecular/mesoscale information into the continuum\r\nscale models....
Airborne particulate emissions originating from the wear of pads and rotors of disc brakes contribute up to 50% of the total road\nemissions in Europe. The wear process that takes place on a mesoscopic length scale in the contact interfaces between the pads\nand rotors can be explained by the creation and destruction of contact plateaus. Due to this complex contact situation, it is hard to\npredict how changes in the wear and material parameters of the pad friction material will affect the friction and wear emissions.\nThis paper reports on an investigation of the effect of different parameters of the pad friction material on the coefficient of friction\nand wear emissions. A full factorial design is developed using a simplified version of a previously developed cellular automaton\napproach to investigate the effect of four factors on the coefficient of friction and wear emission. The simulated result indicates\nthat a stable third body, a high specific wear, and a relatively high amount of metal fibres yield a high and stable mean coefficient\nof friction, while a stable third body, a low specific wear, a stable resin, and a relatively high amount of metal fibres give low wear\nemissions....
When an elastic body of revolution rolls tractively over another, the period from commencement of rolling until gross rolling\r\nensues is termed the prerolling regime. The resultant tractions in this regime are characterized by rate-independent hysteresis behavior\r\nwith nonlocal memory in function of the traversed displacement. This paper is dedicated to the theoretical characterization\r\nof traction during prerolling. Firstly, a theory is developed to calculate the traction field during prerolling in function of the instantaneous\r\nrolling displacement, the imposed longitudinal, lateral and spin creepages, and the elastic contact parameters. Secondly,\r\nthe theory is implemented in a numerical scheme to calculate the resulting traction forces and moments on the tractive rolling of a\r\nball. Thirdly, the basic hysteresis characteristics are systematically established by means of influence-parameters simulations using\r\ndimensionless forms of the problem parameters. The results obtained are consistent with the limiting cases available in literature\r\nand they confirm experimental prerolling hysteresis observations. Furthermore, in a second paper, this theory is validated experimentally\r\nfor the case of V-grooved track....
The molecular models of nitrile–butadiene rubber (NBR) with varied contents of acrylonitrile
(ACN) were developed and investigated to provide an understanding of the enhancement
mechanisms of ACN. The investigation was conducted using molecular dynamics (MD) simulations
to calculate and predict the mechanical and tribological properties of NBR through the constant strain
method and the shearing model. The MD simulation results showed that the mechanical properties
of NBR showed an increasing trend until the content of ACN reached 40%. The mechanism to
enhance the strength of the rubber by ACN was investigated and analyzed by assessing the binding
energy, radius of gyration, mean square displacement, and free volume. The abrasion rate (AR)
of NBR was calculated using Fe-NBR-Fe models during the friction processes. The wear results
of atomistic simulations indicated that the NBR with 40% ACN content had the best tribological
properties due to the synergy among appropriate polarity, rigidity, and chain length of the NBR
molecules. In addition, the random forest regression model of predicted AR, based on the dataset of
feature parameters extracted by the MD models, was developed to obtain the variable importance for
identifying the highly correlated parameters of AR. The torsion–bend–bend energy was obtained and
used to successfully predict the AR trend on the new NBR models with other acrylonitrile contents....
According to the performance degradation problem of feature extraction from higher-order statistics in the context of alphastable\nnoise, a new feature extraction method is proposed. Firstly, the nonstationary vibration signal of rolling bearings is\ndecomposed into several product functions by LMD to realize signal stability. ,en, the distribution properties of product\nfunctions in the time domain are discussed by the comparison of heavy tails and characteristic exponent estimation. Fractional\nlower-order p-function optimization is obtained by the calculation of the distance ratio based on K-means algorithms. Finally, a\nfault feature dataset is established by the optimal FLOS and lower-dimensional mapping matrix of covariation to accurately and\nintuitively describe various bearing faults. Since the alpha-stable noise is effectively suppressed and state described precisely, the\npresented method has shown better performance than the traditional methods in bearing experiments via fractional lower-order\nfeature extraction....
To improve the efficiency of geared transmissions, prediction models are required. Literature provides only simplified models\nthat often do not take into account the influence of many parameters on the power losses. Recently some works based on CFD\nsimulations have been presented. The drawback of this technique is the time demand needed for the computation. In this work\na less time-consuming numerical calculation method based on some specific mesh-handling techniques was extensively applied.\nWith this approach the windage phenomena were simulated and compared with experimental data in terms of power loss. The\ncomparison shows the capability of the numerical approach to capture the phenomena that can be observed experimentally. The\npowerful capabilities of this approach in terms of both prediction accuracy and computational effort efficiency make it a potential\ntool for an advanced design of gearboxes as well as a powerful tool for further comprehension of the physics behind the gearbox\nlubrication....
A novel method which is a combination of wavelet packet transform (WPT),\r\nuninformative variable elimination by partial least squares (UVE-PLS) and simulated\r\nannealing (SA) to extract best variance information among different varieties of lubricants\r\nis presented. A total of 180 samples (60 for each variety) were characterized on the basis of\r\nvisible and short-wave infrared spectroscopy (VIS-SWNIR), and 90 samples (30 for each\r\nvariety) were randomly selected for the calibration set, whereas, the remaining 90 samples\r\n(30 for each variety) were used for the validation set. The spectral data was split into\r\ndifferent frequency bands by WPT, and different frequency bands were obtained. SA was\r\nemployed to look for the best variance band (BVB) among different varieties of lubricants.\r\nIn order to improve prediction precision further, BVB was processed by UVE-PLS and the\r\noptimal cutoff threshold of UVE was found by SA. Finally, five variables were mined, and\r\nwere set as inputs for a least square-support vector machine (LS-SVM) to build the\r\nrecognition model. An optimal model with a correlation coefficient (R) of 0.9850 and root\r\nmean square error of prediction (RMSEP) of 0.0827 was obtained. The overall results\r\nindicated that the method of combining WPT, UVE-PLS and SA was a powerful way to\r\nselect diagnostic information for discrimination among different varieties of lubricating oil,\r\nfurthermore, a more parsimonious and efficient LS-SVM model could be obtained...
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