Evaluation of Clustering Techniques to Predict Surface Roughness during Turning of Stainless-Steel Using Vibration Signals

• Published in: Materials Vol. 14; no. 17; p. 5050
• Main Authors: Abu-Mahfouz, Issam, Banerjee, Amit, Rahman, Esfakur
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 03.09.2021; MDPI
• Subjects: Accelerometers; Algorithms; Approximation; Clustering; Cutting force; Cutting parameters; Cutting speed; Cutting tools; Data analysis; Decomposition; Design of experiments; Feature extraction; Feature selection; Fourier transforms; Genetic algorithms; Machine tools; Noise prediction; Outliers (statistics); Parameter sensitivity; Piezoelectricity; prediction; Predictive control; Principal components analysis; Process parameters; Signal processing; Spectral signatures; Stainless steel; Stainless steels; Surface finish; Surface roughness; Taguchi methods; turning; Turning (machining); Vibration; Wavelet analysis; Wavelet transforms; Workpieces; United States > US
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma14175050

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.