Mechanical Sensing

The datasets are related to the findings and results of our investigations of the minimal force thresholds perception in robotic surgical applications. The experimental setup included an indenter-based haptic device acting on the fingertip of a participant and a visual system displays grasping tasks by a surgical grasper. The experiments included the display of a set of presentations in three different modes, namely, visual-alone, haptic-alone, and bimodal (i.e., combined). Sixty participants took part in these experiments and were asked to distinguish between consecutive presentations.

Acoustic measurement data from Multilayer Ceramic Capacitors (MLCCs). Contains preprocessed data from intact and damaged MLCCs for damage detection (classification) purposes.

This dataset is used to develop an algorithm for automatic segmenting the collected signals. When machining a workpiece in a milling process, vibration signals can be recorded by a 3-axis accelerometer, which is attached on the spindle of a CNC milling machine. To segment the recorded signals, a moving window (0.5 sec) is applied to sample the vibration signals and manually labeled the corresponding modes, i.e. dry run or milling, of each window. To verify the algorithm, 3 types of operations are provided and recorded in csv format. 

When producing bolts in a cold forging process, the pressure signals are recorded per cycle of forming a bolt. The dataset is collected from experiments of different failure modes of a forming machine. Two experiments were recorded in csv format for providing four failure modes, including core broken, cavity block, insufficient lubrication, and material out-of-specification, as well as one normal mode. The two experiments were performed in the same machine with different cavities and cores, and saved in Experimental Data for Modeling and Testing.