查看更多>>摘要：For training artificial neural network(ANN),big data either generated by machine or measured from experiments are used as input to"learn"the unspecified functions defining the ANN.The experimental data are fed directly into the optimizer allowing training to be performed according to a predefined loss function.To predict sliding friction and wear at mixed lubrication conditions,in this study a specific ANN structure was so designed that deep learning algorithms and data-driven optimization models can be used.Experimental ball-on-plate friction and wear data were analyzed using the specific training procedure to optimize the weights and biases incorporated into the neural layers of the ANN,and only two independent experimental data sets were used during the ANN optimization procedure.After the training procedure,the ANN is capable to predict the contact and hydrodynamic pressure by adapting the output data according to the tribological condition implemented in the optimization algorithm.
查看更多>>摘要：H62 brass material is one of the important materials in the process of electrical energy transmission and signal transmission,and has excellent performance in all aspects.Since the wear behavior of electrical contact pairs is particularly complex when they are in service,we evaluated the effects of load,sliding velocity,displacement amplitude,current intensity,and surface roughness on the changes in contact resistance.Machine learning(ML)algorithms were used to predict the electrical contact performance of different factors after wear to determine the correlation between different factors and contact resistance.Random forest(RF),support vector regression(SVR)and BP neural network(BPNN)algorithms were used to establish RF,SVR and BPNN models,respectively,and the experimental data were trained and tested.It was proved that BP neural network model could better predict the stable mean resistance of H62 brass alloy after wear.Characteristic analysis shows that the load and current have great influence on the predicted electrical contact properties.The wear behavior of electrical contacts is influenced by factors such as load,sliding speed,displacement amplitude,current intensity,and surface roughness during operation.Machine learning algorithms can predict the electrical contact performance after wear caused by these factors.Experimental results indicate that an increase in load,current,and surface roughness leads to a decrease in stable mean resistance,while an increase in displacement amplitude and frequency results in an increase in stable mean resistance,leading to a decline in electrical contact performance.To reduce testing time and costs and quickly obtain the electrical contact performance of H62 brass alloy after wear caused by different factors,three algorithms(random forest(RF),support vector regression(SVR),and BP neural network(BPNN))were used to train and test experimental results,resulting in a machine learning model suitable for predicting the stable mean resistance of H62 brass alloy after wear.The prediction results showed that the BPNN model performed better in predicting the electrical contact performance compared to the RF and SVR models.
查看更多>>摘要：This study introduces a method to predict the remaining useful life(RUL)of plain bearings operating under stationary,wear-critical conditions.In this method,the transient wear data of a coupled elastohydrodynamic lubrication(mixed-EHL)and wear simulation approach is used to parametrize a statistical,linear degradation model.The method incorporates Bayesian inference to update the linear degradation model throughout the runtime and thereby consider the transient,system-dependent wear progression within the RUL prediction.A case study is used to show the suitability of the proposed method.The results show that the method can be applied to three distinct types of post-wearing-in behavior:wearing-in with subsequent hydrodynamic,stationary wear,and progressive wear operation.While hydrodynamic operation leads to an infinite lifetime,the method is successfully applied to predict RUL in cases with stationary and progressive wear.
查看更多>>摘要：Numerically generating synthetic surface topography that closely resembles the features and characteristics of experimental surface topography measurements reduces the need to perform these intricate and costly measurements.However,existing algorithms to numerically generated surface topography are not well-suited to create the specific characteristics and geometric features of as-built surfaces that result from laser powder bed fusion(LPBF),such as partially melted metal particles,porosity,laser scan lines,and balling.Thus,we present a method to generate synthetic as-built LPBF surface topography maps using a progressively growing generative adversarial network.We qualitatively and quantitatively demonstrate good agreement between synthetic and experimental as-built LPBF surface topography maps using areal and deterministic surface topography parameters,radially averaged power spectral density,and material ratio curves.The ability to accurately generate synthetic as-built LPBF surface topography maps reduces the experimental burden of performing a large number of surface topography measurements.Furthermore,it facilitates combining experimental measurements with synthetic surface topography maps to create large data-sets that facilitate,e.g.relating as-built surface topography to LPBF process parameters,or implementing digital surface twins to monitor complex end-use LPBF parts,amongst other applications.
Robert GUTIERREZTianshi FANGRobert MAINWARINGTom REDDYHOFF...
1299-1321页
查看更多>>摘要：It is increasingly important to monitor sliding interfaces within machines,since this is where both energy is lost,and failures occur.Acoustic emission(AE)techniques offer a way to monitor contacts remotely without requiring transparent or electrically conductive materials.However,acoustic data from sliding contacts is notoriously complex and difficult to interpret.Herein,we simultaneously measure coefficient of friction(with a conventional force transducer)and acoustic emission(with a piezoelectric sensor and high acquisition rate digitizer)produced by a steel‒steel rubbing contact.Acquired data is then used to train machine learning(ML)algorithms(e.g.,Gaussian process regression(GPR)and support vector machine(SVM))to correlated acoustic emission with friction.ML training requires the dense AE data to first be reduced in size and a range of processing techniques are assessed for this(e.g.,down-sampling,averaging,fast Fourier transforms(FFTs),histograms).Next,fresh,unseen AE data is given to the trained model and the resulting friction predictions are compared with the directly measured friction.There is excellent agreement between the measured and predicted friction when the GPR model is used on AE histogram data,with root mean square(RMS)errors as low as 0.03 and Pearson correlation coefficients reaching 0.8.Moreover,predictions remain accurate despite changes in test conditions such as normal load,reciprocating frequency,and stroke length.This paves the way for remote,acoustic measurements of friction in inaccessible locations within machinery to increase mechanical efficiency and avoid costly failure/needless maintenance.
查看更多>>摘要：The tribological properties of self-lubricating composites are influenced by many variables and complex mechanisms.Data-driven methods,including machine learning(ML)algorithms,can yield a better comprehensive understanding of complex problems under the influence of multiple parameters,typically for how tribological performances and material properties correlate.Correlation of friction coefficients and wear rates of copper/aluminum-graphite(Cu/Al-graphite)self-lubricating composites with their inherent material properties(composition,lubricant content,particle size,processing process,and interfacial bonding strength)and the variables related to the testing method(normal load,sliding speed,and sliding distance)were analyzed using traditional approaches,followed by modeling and prediction of tribological properties through five different ML algorithms,namely support vector machine(SVM),K-Nearest neighbor(KNN),random forest(RF),eXtreme gradient boosting(XGBoost),and least-squares boosting(LSBoost),based on the tribology experimental data.Results demonstrated that ML models could satisfactorily predict friction coefficient and wear rate from the material properties and testing method variables data.Herein,the LSBoost model based on the integrated learning algorithm presented the best prediction performance for friction coefficients and wear rates,with R2 of 0.9219 and 0.9243,respectively.Feature importance analysis also revealed that the content of graphite and the hardness of the matrix have the greatest influence on the friction coefficients,and the normal load,the content of graphite,and the hardness of the matrix influence the wear rates the most.
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