Real-time chatter detection is crucial for the milling process to maintain the workpiece surface quality and minimize the generation of defective parts. In this study, we propose a new methodology based on the measurement of machine head stock structural vibration. A short-pass lifter was applied to the cepstrum to effectively remove components resulting from spindle rotations and to extract structural vibration modal components of the machine. The vibration modal components include information about the wave propagation from the cutter impact to the head stock. The force excitation from the interactions between the cutter and workpiece induces structural vibrations of the head stock. The vibration magnitude for the rigid body modes was smaller in the chatter state compared to that in the stable state. The opposite variation was observed for the bending modes. The liftered spectrum was used to obtain this dependence of vibration on the cutting states. The one-dimensional convolutional neural network extracted the required features from the liftered spectrum for pattern recognition. The classified features allowed demarcation between the stable and chatter states. The chatter detection efficiency was demonstrated by application to the machining process using different cutting parameters. The classification performance of the proposed method was verified with comparison between different classifiers.

Floor impact noise is a significant social concern to secure a quiescent living space for multi-story building residents in South Korea. The floating floor, consisting of a concrete structure on resilient pads, is a specifically designed system to minimize noise transmission. This floating structure employs polymeric pads as the resilient materials. In this study, we investigated the utilization of helically shaped machining scraps as a resilient material for an alternative approach to floor noise reduction. The dynamic elastic modulus and loss factor of the scrap pads were measured using the vibration test method. The scrap pads exhibited a low dynamic elastic modulus and a high loss factor compared to the polymeric pads. Heavyweight impact sound experiments in an actual building were conducted to evaluate the noise reduction performance. The proposed pads showed excellent performance on the reduction in the structure-borne vibration of the concrete slab and resulting sound generation. The analytical model was used to simulate the response of the floating floor structure, enabling a parametric study to examine the effects of the resilient layer viscoelastic properties. Both experimental and analytical evidence confirmed that the proposed scrap pads contribute to the development of sustainable solutions for the minimization of floor impact noise.

This study presented a quantitative evaluation index related to sound response for diagnosis of middle ear condition. The signal transmission paths for human perception of sound are divided into bone conduction and air conduction, respectively, depending on the path through which vibration and sound are transmitted. The components of auditory system that can affect the sound signal variability include temporal bone, ear canal, eardrum, and middle ear cavity. The specific acoustic impedances were obtained through simple geometric model of the auditory components, and the sound transmission mechanism was implemented through the outer-middle ear circuit model. The frequency range corresponding to the resonance characteristics of each components were calculated. The response difference for the medium of middle ear was confirmed by deriving frequency response function between the input sound and the output sound in the frequency domain through the transfer function method. The reliability of the algorithm was confirmed through the ROC curve, and individual evaluation indexes were derived according to the priority factor between classification accuracy and error rate.

Monitoring the fetal heartbeat is essential for obtaining information about fetal condition during pregnancy. By measuring the maternal uterine contractions, the movement of the fetus, and the fetal heart rate, the state of the fetus is detected and taking appropriate measures prevent fetal health abnormality. The NST method is used to obtain the fetal heart rate. The ECG method is useful to observe heart rhythm. In this study, a system that can detect fetal heartbeat signals using vibration sensors is proposed. For vibration measurement, a band-type device with a sensor was proposed. The band was wrapped around the mother's stomach to sensing signals generated by the mother's body. In order to extract the fetal heartbeat signal, appropriate signal processing for denoising is performed. Processed signal was divided into several IMFs using the CEEMD method. The maternal heartbeat signal and fetal heartbeat signal were separated from the IMF. Through frequency analysis, the characteristics of each signal make clear

Otitis media with effusion (OME) is a common middle ear disease characterized by fluid accumulation without acute infection, leading to conductive hearing loss. Conventional diagnostic tools, such as tympanometry and otoscopy, have limited sensitivity and rely on expert interpretation. This study investigates vibro-acoustic radiation (VAR) as a novel, non-invasive, and objective method for OME detection. VAR signals were obtained from 36 OME patients (43 ears) and 15 normal ears using bone-conduction excitation and stereo microphones, and the frequency response functions were analyzed. OME increases the mechanical loading of the tympanic membrane and ossicular chain, thereby modifying sound transmission across the middle ear. Using a simplified theoretical model, we estimated acoustic parameters of the ear canal, eardrum, and middle ear, including specific acoustic impedance and resonance frequency ranges, to interpret changes in VAR. VAR analysis revealed significantly reduced signal amplitude in the 8–10 kHz range in OME ears compared with normal ears (p < 0.05). A classification algorithm based on these features achieved 86.7% accuracy, 85.0% sensitivity, and 80.0% specificity, with an area under the ROC curve of 0.986. These findings suggest that VAR has strong potential as a non-invasive diagnostic tool for OME, warranting validation in larger clinical studies.

In chatter detection, feature evaluation is an important task to identify mechanical systems and achieve higher classification accuracy. The importance of frequency bands is useful under various operating conditions. In this study, we propose a new methodology to identify the importance of frequency bands based on sub-band attention CNN. The sub-band attention CNN is a structure that combines the sub-band CNN and the attention layer. Unlike conventional CNNs that treat all frequency components with the same filter, the sub-band CNN processes different filters for each band. The attention layer is used to evaluate the importance of each band. The time-varying variance in frequency domain is used to extract chatter characteristics that vary greatly with time and it is used as an input for chatter detection. The useful frequency bands for chatter detection are obtained from the sub-band attention CNN. The importance of the frequency band is analyzed with the frequency response of the mechanical system.