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.

As the demand for precision in the manufacturing industry grows, Digital Twin (DT) technology is gaining attention for its potential to enhance equipment performance and process reliability. However, existing research has primarily focused on specific stages of design or operation, leaving a gap in the literature concerning DT models that can be utilized throughout the entire equipment lifecycle. To address this gap, this study proposes a method for developing a DT that employs a consistent Finite Element (FE) model across all phases of the equipment lifecycle. We utilized actual measurement data to ensure high fidelity in the FE model of previous-generation equipment, which we refer to as the Pre-DT. This Pre-DT was instrumental in improving design during the new equipment development phase. Additionally, the DT model was implemented to predict equipment status in real time using the Reduced-Order Model (ROM) method, functioning as a virtual sensor during operation. This approach was applied to the equipment development process, aligned with the asset lifecycle concept of RAMI 4.0, and was tested on an MLCC cutting equipment to validate its effectiveness.

Wearable sensors are susceptible to degradation from physical wear, moisture, and desiccation, which can result in signal attenuation and unreliable data. This pilot study, conducted in a controlled single-participant setting, introduces a framework to quantify and characterize sensor degradation while restoring corrupted electromyography (EMG) signals. Four types of sensors—polyethylene terephthalate film, parylene film, 3M bioelectrode pads, and microneedle patches—were affixed to the left forearm in a three-electrode EMG configuration. Impedance at 100 Hz was monitored as an indicator of sensor aging, while a one-dimensional convolutional autoencoder was employed to reconstruct degraded EMG signals using a loss function that incorporated both time-domain and frequency-domain error terms. The reconstruction loss showed a correlation with impedance changes, providing a practical metric for assessing sensor health. These findings highlight the feasibility of real-time signal recovery and its potential to extend the lifespan of sensors.

Carbon nanotubes (CNTs) are popular in strain sensors due to their exceptional electrical conductivity, flexibility, and sensitivity to deformation. In this study, a high-sensitivity strain sensor was fabricated by spray-coating CNT ink onto various paper substrates, with “lint-free paper” identified as the optimal choice. A total of 10 spray cycles ensured a reliable conductive coating. To enhance durability and broaden application potential, a PET protective layer was incorporated. The sensor's performance was assessed through bending tests using a push-pull gauge across a strain range of 0-2%. The lintfree paper-based sensor exhibited a consistent response up to 1.4% strain. The measured gauge factors (GF) were 121.370 in the 0-0.3% range, 70.999 in the 0.3-0.8% range, and 20.935 in the 0.8-1.4% range. A precise response was also noted when adjusting the bending angle in 1° increments, particularly within the 0-20° range. Additionally, the sensor was tested on the human wrist, confirming its viability for wearable applications. These findings indicate that the lint-free paper-based CNT strain sensor offers high sensitivity and measurement precision within narrow strain ranges. Its lightweight structure and flexible design suggest strong potential for practical use in areas such as sports monitoring and human motion detection.

This study details the development of an ultra-precision air-bearing stage that integrates real-time motion error measurement and compensation features. The motion errors addressed include horizontal and vertical straightness errors, as well as roll, pitch, and yaw errors. These errors are measured by an embedded system that incorporates five capacitive sensors and a reference mirror within the stage. A key advantage of this stage is its capability to perform real-time compensation using the internal measurement system and on-stage pneumatic regulators, eliminating the need for external measurement and compensation devices. Experimental results show a significant reduction in motion errors, with horizontal and vertical straightness errors decreasing from 3.09 and 1.95 μm to 0.29 and 0.25 μm, respectively. Additionally, roll, pitch, and yaw errors were reduced from 3.18, 3.45, and 4.93 arcsec to 0.35, 0.41, and 0.49 arcsec, respectively. These results clearly demonstrate the effectiveness of the proposed approach.

Recently, flexible pressure sensors featuring enhanced sensitivity and durability through nano/micro additive manufacturing have been employed in various fields, including medical monitoring, E-skin technology, and soft robotics. This study focuses on the fabrication and verification of an interdigitated electrode (IDE) based flexible pressure sensor that incorporates microstructures, utilizing a direct patterning-based additive process. The IDE-patterned sample was designed with a total size of 7.95 × 10 mm2, a line width of 150 µm, a spacing of 200 µm, and a probe pad measuring 1.25 × 2 mm2. It was fabricated using AgNP ink on a primed 100 µm thick polyethylene naphthalate (PEN) substrate. The electrode layer was subsequently covered with a sensing layer made of a MWCNT/Ecoflex composite material, resulting in the final pressure sensor sample. Measurements indicated that the sensor exhibited good sensitivity and response speed, and it was confirmed that further improvements in sensitivity could be achieved by optimizing the size, spacing, and height of the microstructures. Building on the flexible pressure sensor structure developed in this study, we plan to pursue future research aimed at fabricating array sensors with integrated circuits and exploring their applicability in wearable devices for pressure sensing and control functions.

In this study, we fabricated and investigated the polymer-based cylindrical flow sensor for two-dimensional (2D) detection. The flow sensor was the drag force type flowmeter which was fabricated with ecoflex. It had CNT/PDMS as the piezoresistive material and a cylindrical shape to measure the 2D flow. It also had impact resistance and ease of fabrication due to its polymer-based sensor. At first, two piezoresistive parts were applied to evaluate detection properties. Forces from various direction were applied. Results showed its potential as a sensing device. Following this, the final flow sensor was fabricated with four piezoresistive parts and its sensitivity was measured in the air flow from 0 to 30 m/s. Resistance changes were measured while rotating the sensor. Outputs showed a form of sine waves. Data were repeatedly collected under various conditions. The direction and air flow rate were then determined. To check physical impact resistance, a sudden high air flow rate with 100m/s was applied to the sensor and a stable output was obtained. These results suggest that such ecoflex-based cylindrical flow sensor can be used as a 2D flow rate sensor.

This paper extensively explores and analyzes the latest research trends in Ionic Polymer-Metal Composites (IPMC) sensors. IPMC sensors are known for their flexibility, lightness, and high responsiveness. They show great promise across different fields. They can respond sensitively to various stimuli such as mechanical deformation, humidity, and pressure, making them ideal for bio-responsive detection, health monitoring, and energy harvesting. This paper introduces actuation and sensing mechanisms of IPMCs, discusses their manufacturing processes, and explores how these processes can influence the responsiveness and stability of sensors. Moreover, through case studies of IPMC-based research that can perform self-sensing functions, it presents possibilities brought by the integration of sensors and actuators. This paper emphasizes the potential for research and development of IPMC sensors to expand into various industrial fields and explores ways to continuously improve the accuracy and reliability of sensors. IPMC-based sensors are expected to play a significant role in advancing medical devices and wearable technologies, thereby facilitating innovation in the field.

Gas sensors are crucial devices in various fields such as industrial safety, environmental monitoring, and gas infrastructure. Designed to have high-sensitivity, stability, and reliability, gas sensors are often required to be cost-effective with quick response and compactness. To meet diverse needs, we developed two types of gas sensors based on volumetric and manometric analyses. These sensors could operate by measuring gas volume and pressure changes, respectively, based on emitted gas. These sensors are capable of determining gas transport parameters such as gas uptake, solubility, and diffusivity for gas-charged polymers in a high-pressure environment. These sensors can provide rapid responses within one-second. They can measure gas concentration ranging from 0.01 wt·ppm to 1,500 wt·ppm with adjustable sensitivity and measurement ranges. As a result, such sensor system can be used to facilitate real time detection and analysis of gas transport properties in pure gases including H₂, He, N₂, O₂, and Ar.

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.

In this study, a highly sensitive analysis device for hydrogen sulfide that could be used quickly and easily on site was developed using a colorimetric paper sensor. To optimize analysis conditions, tests were performed for each function. Performances of the method using laboratory equipment and tools and the method using the developed device for hydrogen sulfide analysis were compared. The trend line of changes in parameter b of the image acquired by the on-site analytical device for hydrogen sulfide was calculated as y = 0.517x - 0.141 with a coefficient of determination (R2) of 0.9874. It was comparable to the method performed at the laboratory level, showing an excellent linearity. Using the calculated trend line as a calibration curve, the detection limit and quantification limit were found to be 2.386 μM and 7.952 μM, respectively. A reproducibility test showed a relative standard deviation of 5.7%, indicating a low dispersion of results.

This study reports an autonomous fine dust source tracking system of a water spray robot for high-rise building demolition. The core function of this system is performing a self-controlled fine dust tracking of the endpoint of the excavator, which is the fine dust generation point. The water spray robot has a lift with a parallelogram-shaped linkage to lift the water spray drum to 10 m from the ground. The sensor network system is connected to the robot and the excavator to calculate the relative position of the water spray drum and excavator endpoint using forward kinematics. RTK-GPS is attached to the robot and the excavator to calculate the relative distance. By sensor network, forward kinematics, and RTK-GPS, the water spray robot can autonomously track fine dust generation point and spray water to the endpoint of the excavator. The experiment was conducted to confirm the accuracy of kinematics calculation and tracking performance of the robot. The first experiment showed that the calculation result of forward kinematics was accurate enough to fulfill tracking operations. The second experiment showed that the tracking accuracy was precise enough, meaning that the robot could autonomously track fine dust generation point.

Recently, the estimation of joint kinetics such as joint force and moment using wearable inertial sensors has received great attention in biomechanics. Generally, the joint force and moment are calculated though inverse dynamics using segment kinematic data, ground reaction force, and moment. However, this approach has problems such as estimation error of kinematic data and soft tissue artifacts, which can lead to inaccuracy of joint forces and moments in inverse dynamics. This study aimed to apply a recurrent neural network (RNN) instead of inverse dynamics to joint force and moment estimation. The proposed RNN could receive signals from inertial sensors and force plate as input vector and output lower extremity joints forces and moments. As the proposed method does not depend on inverse dynamics, it is independent of the inaccuracy problem of the conventional method. Experimental results showed that the estimation performance of hip joint moment of the proposed RNN was improved by 66.4% compared to that of the inverse dynamics-based method.

The purpose of this study was to check the position classification prediction rate based on the movement data of field hockey players using the random forest algorithm. In order to achieve the purpose of this study, movement data were collected using wearable devices in 15 practice matches. The collected information was then analyzed using the Random Forest algorithm, one of the ensemble techniques, with Python, a high-level, general-purpose programming language. As a result of this study, first, the position classification prediction rate was 52.4±3.3% when data measured by GPS sensors were used. Second, when using the data measured by an inertial measurement unit (IMU) sensor, the position classification prediction rate was 50.8±2.4%. Third, when both Global Positioning System (GPS) and IMU data were used, the position classification prediction rate was 55.6±2.0%. As a result of the study, it showed that the prediction rate was the highest when both GPS and IMU data were used.