Accurate Measurement of Reciprocating Kinetic Friction Coefficient Through Automatic Detection of the Running-In

Tribological tests are adopted to estimate the kinetic friction coefficient (COF) of the material of interest according to the American Society for Testing and Materials (ASTM) standard. Typically, for the measurement process, several replicas, as well as a postprocessing data treatment, are necessary to take into account the observed casual variability of the measurand. This article describes a statistical approach aiming to highlight the running-in phase and the most significant time intervals during the steady-state for each test replica. A two-steps procedure based on the adoption of the bootstrap method allows the automatic detection of the running-in time interval and the outlier filtering of the steady-state. Experimental activity has been carried out by performing multiple tests through the ball-on-flat tribometer in order to verify the improvement allowed by the authors’ proposal about the dry friction COF measurement in terms of both accuracy and repeatability.

[1]  P. Blau.  Running-in: Art or engineering? , 1991 .

[2]  A. Ruggiero,et al.  Measurements of the Friction Coefficient: Discussion on the Results in the Framework of the Time Series Analysis , 2018, Advances in Manufacturing Engineering and Materials.

[3]  Alfredo Paolillo,et al.  Outlier Detection for the Evaluation of the Measurement Uncertainty of Environmental Acoustic Noise , 2016, IEEE Transactions on Instrumentation and Measurement.

[4]  Alessandro Ruggiero,et al.  Influence of measurement time on acoustic noise uncertainty , 2017, 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[5]  R. Genevois,et al.  The measure of friction angles for different types of granular material , 2019, Journal of Mountain Science.

[6]  Luca Mari,et al.  Measurement, Models, and Uncertainty , 2012, IEEE Transactions on Instrumentation and Measurement.

[7]  Michele Ciavarella,et al.  Modeling and simulation in tribology across scales: An overview , 2018, Tribology International.

[8]  A. Ruggiero,et al.  Experimental comparison on tribological pairs UHMWPE/TIAL6V4 alloy, UHMWPE/AISI316L austenitic stainless and UHMWPE/AL2O3 ceramic, under dry and lubricated conditions , 2016 .

[9]  Alessandro Ruggiero,et al.  Estimation of the minimum measurement time interval in acoustic noise , 2017 .

[10]  V. Popov,et al.  Handbook of Contact Mechanics , 2019 .

[11]  P. Blau.  The significance and use of the friction coefficient , 2001 .

[12]  Alfredo Paolillo,et al.  A preliminary study on the estimation of the uncertainty of traffic noise measurements , 2016, 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[13]  Alfredo Paolillo,et al.  Towards the evaluation of the measurement uncertainty of environmental acoustic noise , 2015, 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

[14]  Gilles Mauris,et al.  A Review of Relationships Between Possibility and Probability Representations of Uncertainty in Measurement , 2013, IEEE Transactions on Instrumentation and Measurement.

[15]  Peter Blau,et al.  Friction Science and Technology: From Concepts to Applications, Second Edition , 2008 .

[16]  Alessandro Ferrero,et al.  Uncertainty: Only One Mathematical Approach to Its Evaluation and Expression? , 2012, IEEE Transactions on Instrumentation and Measurement.

[17]  J. Klafter,et al.  The nonlinear nature of friction , 2004, Nature.

[18]  Ettore Pennestrì,et al.  Review and comparison of dry friction force models , 2016 .

[19]  Di Sun,et al.  Experimental study on the chaotic attractor evolvement of the friction vibration in a running-in process , 2015 .

[20]  C. Coulomb.  Théorie des machines simples, en ayant égard au frottement de leurs parties et a la roideur des cordages , 1968 .

[21]  Alessandro Ruggiero,et al.  Innovative bootstrap approach for the estimation of minimum measurement time interval in road traffic noise evaluation , 2017 .

[22]  P. Blau.  On the nature of running-in , 2005 .

[23]  Alessandro Ruggiero,et al.  A Statistical Approach for Improving the Accuracy of Dry Friction Coefficient Measurement , 2019, IEEE Transactions on Instrumentation and Measurement.

[24]  Alessandro Ruggiero,et al.  Determining environmental noise measurement uncertainty in the context of the Italian legislative framework , 2016 .

[25]  Alessandro Ruggiero,et al.  Choosing Bootstrap Method for the Estimation of the Uncertainty of Traffic Noise Measurements , 2017, IEEE Transactions on Instrumentation and Measurement.

[26]  Alessandro Ruggiero,et al.  Accurate Estimation of the Environmental Noise Through Sampling Approach: Selection of the Measurement Time , 2018, IEEE Transactions on Instrumentation and Measurement.

[27]  Alessandro Ruggiero,et al.  Accurate measurement of Kinetic Friction Coefficient by using two types of tribometer , 2019, 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[28]  H. Costa,et al.  Effect of debris size on the reciprocating sliding wear of aluminium , 2017 .

[29]  George G. Adams,et al.  Contact mechanics and friction , 2005 .

[30]  Alessandro Ruggiero,et al.  Accurate measurement of dry friction coefficient using reciprocating tribometer , 2018, 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[31]  K. Lu,et al.  Friction mechanism in the running-in stage of copper: From plastic deformation to delamination and oxidation , 2017 .

[32]  Naveen Garg,et al.  Reaffirmation of measurement uncertainty in pressure sensitivity determination of LS2P microphones by reciprocity method , 2014 .

[33]  D. Rigney,et al.  Running-in processes affecting friction and wear , 1982 .

[34]  Edward J. Berger,et al.  Friction modeling for dynamic system simulation , 2002 .

[35]  Yuankai Zhou,et al.  Development of prediction models of running-in attractor , 2018 .

[36]  Hua Zhu.  Evolvement rule of running-in attractor , 2008 .