The use of tilting pad journal bearings (TPJBs) has increased in the recent past due to their stabilizing effects on the rotor bearing system. However, in this paper two mechanisms capable of producing instabilities in terms of subharmonic and chaotic motions are suggested. The first one is that of a centrally loaded pad with rotor unbalance excitations. The second one represents a concentric rotor (or a vertical rotor) acted upon by centering sprigs and large unbalance excitations. Extensive numerical experimentation shows, for certain parameters, subharmonic, quasi-periodic, and chaotic motions. The pad state space trajectory, in many cases, resembles that of the two-well potential case as in Duffing’s oscillator. Time trajectories, Poincare maps, fast Fourier transform (FFT) plots, and the max Lyapunov exponent are utilized to examine the periodicity (order) of the nonsynchronous rotor orbits and pad trajectories. The TPJB problem belongs to a family of nonlinear rotor-dynamical phenomena that are potentially of a considerable value as diagnostic tools in assessing rotating machinery condition monitoring.
In metal-cutting processes, the interaction between the tool and workpiece is highly nonlinear and is very sensitive to small variations in the process parameters. This causes difficulties in controlling and predicting the resulting surface finish quality of the machined surface. In this work, vibration signals along the major cutting force direction in the turning process are measured at different combinations of cutting speeds, feeds, and depths of cut using a piezoelectric accelerometer. The signals are processed to extract features in the time and frequency domains. These include statistical quantities, Fast Fourier spectral signatures, and various wavelet analysis extracts. Various feature selection methods are applied to the extracted features for dimensionality reduction, followed by applying several outlier-resistant unsupervised clustering algorithms on the reduced feature set. The objective is to ascertain if partitions created by the clustering algorithms correspond to experimentally obtained surface roughness data for specific combinations of cutting conditions. We find 75% accuracy in predicting surface finish from the Noise Clustering Fuzzy C-Means (NC-FCM) and the Density-Based Spatial Clustering Applications with Noise (DBSCAN) algorithms, and upwards of 80% accuracy in identifying outliers. In general, wrapper methods used for feature selection had better partitioning efficacy than filter methods for feature selection. These results are useful when considering real-time steel turning process monitoring systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.