Multi-Sensor Dataset of Ultrasonic and mmWave for Material Classification

This dataset details a publicly accessible of reflected signals from ultrasonic and millimetre-wave (mmWave) sensors used for material classification. The dataset was gathered using two sensing methods: the URM09 ultrasonic sensor operating at 40 kHz and the DFRobot mmWave C4001 radar sensor operating at 24 GHz. Six common materials were chosen for data collection: wood, plastic, metal, glass, cardboard, and asbestos. Each material was tested at three thicknesses and three sizes (25 cm \u00d7 25 cm, 20 cm \u00d7 20 cm, and 15 cm \u00d7 15 cm). All measurements were taken from a fixed distance of 55 cm in a controlled indoor setting with foam-lined enclosures to reduce external noise. The raw analogue signals were processed with Savitzky-Golay filtering and sliding window techniques. Time-domain features (mean, standard deviation, energy, RMS, kurtosis, skewness) and frequency-domain features (spectral centroid, spectral bandwidth, dominant frequency) were extracted from each sample. For URM09 the dataset includes 1,789 refined data points across all six material-thickness combinations and 565 records for six material-only classification. On the other hand, For C4001 the dataset includes 1,504 refined data points across all five material-thickness combinations and 470 records for five material-only classification.  This dataset supports research in robotics, industrial inspection, non-destructive testing, and sensor fusion applications where contactless material identification is essential.