In this study, we proposed a methodology for predicting tool wear in the turning process using the SVR model. This model maintains stable performance even in small-scale data environments and demonstrates robust characteristics against outliers. We detected changes in machining performance caused by tool wear through an AE sensor and accelerometer. Features were extracted from the acquired sensor signals and utilized in the machine learning model. Prior to training, the extracted features underwent a preliminary screening process based on distance correlation. By optimizing the feature combination using the RFECV algorithm, we achieved a prediction accuracy of R² = 0.95. The analysis revealed that key features influencing the tool wear prediction model included several significant variables. Additionally, we found that evaluating feature importance allowed for more efficient model improvement. Overall, when developing a tool wear prediction model for cutting, it is crucial to utilize various sensor signals, extract features in both the time and frequency domains, and optimize the combination of those features.

In this study, a module combining various types of sensors was developed to increase search efficiency inside collapsed buildings. It was designed to be less than 70 mm in diameter so that it can be put into narrow spaces, and is equipped with a small & high-performance processor to process multiple sensor data. To increase sensor data processing efficiency, multi thread based software was configured, and the images were combined and transmitted to ensure time synchronization of multi-channel video data. A human detection function based on sound source detection using two microphones was implemented. The developed multi-sensor module was tested for operation by mounting it on a snake-type robot in a test bed simulating a disaster site. It was confirmed that the visible range of the robot to which the multi-sensor module was applied was expanded, and the ability to detect human and low-light human detect was secured.