In machining operations, dynamometers are typically used to directly measure the forces acting on cutting tools. However, their high cost and complex setup restrict their use to laboratory environments, making them unsuitable for real-time monitoring in general production settings. To overcome this limitation, this study proposes an autoencoder-based learning model for estimating cutting forces using only spindle vibration signals acquired during milling. The model features a deep neural network (DNN) that takes processed spindle vibration signals as input and predicts latent features derived from cutting force signals through an autoencoder. These predicted latent features are then fed into a pretrained decoder to reconstruct the corresponding cutting force signals. To enhance the model's accuracy and robustness, the raw vibration signals sampled at 20 kHz were filtered with a bandpass filter that spans the effective frequency range of 20–2500 Hz, effectively removing irrelevant noise. For validation, an accelerometer was mounted on the spindle head of a milling machine, and vibration data were collected during cutting. The estimated cutting forces were compared to ground truth measurements obtained from a dynamometer. The model achieved a Pearson correlation coefficient of 0.943, demonstrating that reliable cutting force estimation is achievable using only low-cost vibration sensors.
This paper presents a method for the real-time detection of pipeline leaks using flexible Acoustic Emission (AE) sensors. The signals gathered from the AE sensor are transformed into RGB images through the application of Mel-spectrogram and color coding. These converted images serve as input for a Convolutional Neural Network (CNN) based on ResNet18. With this approach, both the presence and intensity of leaks in a pipeline can be identified using the AE sensor. The effectiveness of the proposed method was validated through data collected from a testbed featuring a galvanized pipe.
The importance of cutting forces in machining has been emphasized for monitoring and optimizing cutting conditions, leading to various method to detecting cutting forces researched. Cutting forces can be directly measured using dynamometer or indirectly estimated using AE sensors and accelerometers, etc. However, these external sensors demand high costs and have accuracy limitations due to environment issues. To compensate for these drawbacks, utilizing internal signals of machine tool has been developed. Among these, using internal electrical signals of machine tool is representative. In commercial machine tools, cutting forces are often estimated through current measurements. However, due to the characteristics of the spindle motor, electrical properties such as slip, power factor, and efficiency vary with the load, resulting in relatively lower accuracy. This study introduces current-based method considering characteristics of motor and power-based method for estimating cutting forces and compare accuracy of those methods with the measurements from dynamometer respectively.
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Autoencoder-based Milling Cutting Force Monitoring by Spindle Vibration Signal Detection Je-Doo Ryu, Jung-Min Lee, Sung-Ryul Kim, Min Cheol Lee Journal of the Korean Society for Precision Engineering.2026; 43(1): 47. CrossRef
In mechanical braking systems, there are hot spots on the surface of a braking disc due to thermal deformation with a high thermal gradient. Controlling such hot spots is important for extending the life of a braking disc. In this study, surface temperatures of railway brake discs were monitored using infrared (IR) thermal imaging technique. A highspeed infrared camera with a maximum speed of 380 Hz was used to monitor surface temperature changes of the braking disc. Braking tests were performed with a full-scale dynamometer. During the braking test, the surface temperature change of the braking disc were monitored using a high-speed infrared camera. Hot spots and thermal damage observed on the surface of railway brake discs during braking tests were quantitatively analyzed using infrared thermographic images. Results revealed that monitoring disc surface temperature using IR thermographic technique can be a new method for predicting surface temperature changes without installing a thermocouple inside the disc.
Elderly monitoring systems are gaining significant attention in our increasingly aging society. Existing monitoring systems, which utilize RGB and infrared cameras, often encounter errors when recognizing human-like objects, photos, and videos as actual humans. Additionally, privacy concerns arise due to this issue. However, these challenges can potentially be overcome by employing thermal images. Thus, our study aimed to investigate the feasibility of identifying and categorizing human postures depicted in thermal images using deep learning models and algorithms. To conduct our experiment, we developed a system that utilizes a thermal pose algorithm and a convolutional neural network. As a result, we achieved an average accuracy of 88.3%, with the highest accuracy reaching 91.2%.
Belt-pulley looseness is a crucial factor in ensuring the safe operation of machinery used in industrial applications, such as compressors and fans. Traditionally, belt looseness has been inspected using contact-based current and vibration sensors. However, these methods are time-consuming and require manual attachment of the sensors. In order to overcome the limitations of these traditional methods, we propose a remote diagnosis method for detecting belt looseness using a smartphone. By utilizing a four-mirror system, the smartphone can construct a stereo system that enables 3D reconstruction of the object. This allows us to reconstruct the 3D trajectory of the belt and diagnose the level of looseness. To further enhance the accuracy of our proposed system, we have developed a calibration algorithm specifically designed for the four-mirror system. In our actual experiment, we successfully diagnosed four levels of belt looseness. As the level of looseness increased, we observed a curved shape in the 3D trajectory of the belt, along with noticeable quantitative differences. To quantitatively analyze these differences, we introduced a measure called the residual, which reflects the curvilinearity of the 3D trajectory. Our findings confirmed a significant correlation between the residual and the level of belt looseness.
Damage to the units related to driving and running of the railway vehicle may cause an inevitable accident due to defects and malfunctions in operation. In order to prevent such an accident, a non-destructive diagnostic technology that detects the damage is required. Previous researchers have researched and developed a monitoring system of the infrared thermography method to diagnose the condition of the railway vehicle driving and driving units. A system for monitoring running of the railway vehicle and temperature condition of the drive unit at a vehicle speed of 30 to 100 km/h was constructed, and a study on its applicability was conducted. In this study, a system for diagnosing an abnormal condition of the driving and running units while the vehicle is running with an infrared thermography diagnostic system was installed in the depot and operation route, and evaluation of the abnormal condition of the driving and running units was performed. The results show that the diagnosis system using infrared thermography can be used to identify abnormal conditions in the driving and running units of a railway vehicle. The diagnosis system can effectively inspect the normal and abnormal conditions in operation of a railway vehicle.
Currently digital transformation has a huge impact on human lives. Digital transformation does not just mean a transformation of a (non-) physical element to a digitally identifiable element. It focuses on the utilization of digital technology for transforming (improving) procedures or routines of business and operation. The manufacturing industry has been adopting the most recent digital technology, and lots of digital data are being created. To utilize the stored data, data analysis is essential. Because the manufacturing data is created in a different format at every manufacturing step, the integration of the data is always the bottleneck of the data analysis. Querying of the right data at the proper time is fundamental for high-level data analysis. The digital thread is introduced to provide the inter-reference of digital data based on a context. This paper proposes a digital thread framework for the machining process. The context of the proposed framework consists of the questions of how the product will be machined, how it is (was) being produced, and how it was made. A prototype software was developed to verify the proposed framework by implementing the creating, storing, and querying modules for simulation, monitoring, and inspection data.
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A Review of Intelligent Machining Process in CNC Machine Tool Systems Joo Sung Yoon, Il-ha Park, Dong Yoon Lee International Journal of Precision Engineering and Manufacturing.2025; 26(9): 2243. CrossRef
Among the monitoring technologies in the metal-cutting process, tool wear is the most critical monitoring factor in real machining sites. Extensive studies have been conducted to monitor equipment breakdown in real-time. For example, tool wear prediction studies using cutting force signals and deducting force coefficient values from the cutting process. However, due to many limitations, those wearable monitoring technologies have not been directly adopted in the field. This paper proposes a novel tool wear predictor using the cutting force coefficient with various cutting tools, and its validity evaluates through cutting tests. Tool wear prediction from the cutting force coefficient should conduct in real-time for adoption in real machining sites. Therefore, a real-time calculation algorithm of the cutting force coefficient and a tool wear estimation method proposes, and they compare with actual tool wear in cutting experiments for validation. Validation cutting tests are conducted with carbon steel and titanium, the most commonly used materials in real cutting sites. In future work, validation will be conducted with different materials and cutting tools, considering the application in real machining sites.
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A Review of Intelligent Machining Process in CNC Machine Tool Systems Joo Sung Yoon, Il-ha Park, Dong Yoon Lee International Journal of Precision Engineering and Manufacturing.2025; 26(9): 2243. CrossRef
Recently, large-scale accidents caused by minor damage from fatigue failure and impact on structures have been frequently reported. Therefore, a real-time damage monitoring system of structures is considered to be one of the most important technologies to ensure safety in various types of research. The piezoelectric sensor, which has an advantage of converting deformation of a structure into an electrical signal without using an additional power source, has been reported as one of the most suitable methods for real-time monitoring systems. This review aims to describe the structural monitoring system utilizing piezoelectric paint sensors. First, we present the concept of a piezoelectric paint sensor with the advantages of flexibility and piezoelectric performance. Then, factors affecting the performance of the piezoelectric paint sensor are introduced. Finally, an overview of piezoelectric paint sensors for structural monitoring, such as vibration detection and impact monitoring, are provided. The state-of-the-art of the application of the piezoelectric sensor is also introduced, providing feasibility in industrial fields.
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Evaluation of MWCNT/PU sponge-based triboelectric nanogenerator for harvesting mechanical energy Insik Jo, Byungchul Kim, Hyungsik Won, SunHee Kim, Kyungwho Choi, Dukhyun Choi Functional Composites and Structures.2025; 7(3): 035010. CrossRef
Spectroscopic sensors have been used in the field of optics and chemical analysis, and the need for remote gas monitoring in a portable form has increased. Hence, it is essential to design, manufacture, and develop a moving mirror, which that can generate the optical path difference for spectroscopic sensor. It is important to verify whether it can satisfy design requirements for the actual spectroscopic interferometer application by evaluating its performance. In this paper, a moving mirror assembly with high-speed transfer capability for a portable spectroscopic sensor is fabricated and tested. For application to a portable spectrometer, design requirements, such as moving distance, speed, and stiffness, are set and the mechanical structure, including the voice coil motor and elastic guide, satisfying these requirements is proposed. Through finite element analysis, performance of the proposed moving mirror assembly is predicted. By testing the performance after fabrication, it is verified that the proposed mirror is capable of linear movement with travel distance of several millimeters and moving speed of tens of Hz. Optical testing result shows that the proposed moving mirror can generate linear motion with a tilting error below 10 arcsecond and can be applied to the actual spectroscopic interferometer in future.
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Simulation Study on Line-of-sight Stabilization Controller Design for Portable Optical Systems Jae Woo Jung, Sang Won Jung, Jae Hyun Kim, Seonbin Lim, Youngjin Park, Onemook Kim, Jaehyun Lim, Jae Ho Jin, No-Cheol Park, Jun Young Yoon Journal of the Korean Society for Precision Engineering.2025; 42(2): 175. CrossRef
Power Consumption Analysis and Optimal Operation Method of Wireless Multi-sensor Module Hyun Sik Son, Duck-Keun Kim, Kwang Eun Ko, Seung-Hwan Yang Journal of the Korean Society for Precision Engineering.2025; 42(10): 843. CrossRef
Structural Design of Fast Steering Mirror with Reluctance Actuator Onemook Kim, Seonbin Lim, Jaewoo Jung, Sangwon Jung, Jaehyun Kim, Bomin Kang, Junyoung Yoon, Seounghan Lee, Byoungju Lee, Yonghoon Lee, Hyeongrae Kim, No-cheol Park Transactions of the Korean Society for Noise and Vibration Engineering.2024; 34(3): 330. CrossRef
Design optimization of a flexure spring used in small-sized ultra-precise optical instrument Jin Kim, Dong-Chan Lee, Ho-Sang Kim Heliyon.2023; 9(12): e22560. CrossRef
Development of piezoelectric fast steering mirror with tilt error compensation for portable spectroscopic sensor Ho-Sang Kim, Kyoung Don Lee, Chan-Hee Lee, Won-Gi Lee Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture.2023; 237(12): 1847. CrossRef
Social interest in the 4th industry, intelligent factories, and smart manufacturing is continually growing along with the core technologies like big data and artificial intelligence, which can generate meaningful information by collecting and accumulating sensor data. Demand for industrial automation equipment is increasing worldwide due to the efforts needed to modernize manufacturing facilities, reduce automation and cycle time, and improve quality. Currently, the majority of research is focused on the development of automation facilities and improving productivity. The research on the contents of real-time data considering the characteristics of the cutting machine plasma machine is insufficient. In this study, based on the current data measured according to cutting current and cutting speed, a reference value for cutting quality is presented and the optimal process parameter has been selected. A model for predicting cutting quality by introducing the Mahalanobis Distance Method is presented. An attempt has been made to derive selection and optimal cutting process variables. Based on the predictive model, threshold values were specified and used in real-time data to consider the correlations between multivariate variables and evaluate the degree of scattering around the average of specific values of each variable. Also, process parameters suitable for surface roughness were calculated.
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A quantitative diagnostic method of feature coordination for machine learning model with massive data from rotary machine Yoonjae Lee, Byeonghui Park, Minho Jo, Jongsu Lee, Changwoo Lee Expert Systems with Applications.2023; 214: 119117. CrossRef
Silicon nanoparticles: fabrication, characterization, application and perspectives Taeyeong Kim, Jungchul Lee Micro and Nano Systems Letters.2023;[Epub] CrossRef
Feature selection algorithm based on density and distance for fault diagnosis applied to a roll-to-roll manufacturing system Hyogeun Oh, Yoonjae Lee, Jongsu Lee, Changbeom Joo, Changwoo Lee Journal of Computational Design and Engineering.2022; 9(2): 805. CrossRef
Impact of Sensor Data Characterization with Directional Nature of Fault and Statistical Feature Combination for Defect Detection on Roll-to-Roll Printed Electronics Yoonjae Lee, Minho Jo, Gyoujin Cho, Changbeom Joo, Changwoo Lee Sensors.2021; 21(24): 8454. CrossRef
Recently, applying nanoscale functional materials, there have been great advances in the flexible sensor system, which provides a large number of applications for soft electronics, such as skin-attachable sensors, artificial electronic skins, and soft robotic systems. Here, we developed a highly sensitive and flexible device on the basis of polymeric piezoelectric nanofibers and elastomeric packing structures. To produce the nanofibers, we applied the electrospinning process with a representative piezoelectric co-polymer, poly (vinylidenefluoride-co-trifluoroethylene) (PVDF-TrFE). Unlike the conventional electrospinning, we applied an anisotropic fiber collection system, which could obtain uniaxially aligned nanofiber array. The aligned nanofibers were sandwich-packed with bridge-shaped PDMS substrates, thereby integrating the flexible piezoelectric sensor. As an external force made a deflection of the bridge in the sensor, the embedded nanofibers generated piezoelectricity in a longitudinal direction of the fibers. The piezoelectric sensor generated good discernable outputs versus the varied mechanical input deflection from tens of micrometers to the sub-micrometer. With this great sensing ability, we could monitor heart pulse signals on the wrist skin by measuring tiny deflections generated from the expansion of the radial artery underneath the skin. Our study suggests a potential application of flexible sensor in the field of wearable health-monitoring medical systems.
Since becoming highly functional, complex and flexible, the machining system of CFRP(Carbon Fiber Reinforced Plastic) has recently become highly functional, complex and flexible, its has its controllers are changing into open and distributed structures. These, and need controlling to be controlled to maintain good quality of for a quality of machined parts. In particularSpecifically, an open controller is required urgently needed to apply the optimal processing program for each material and development of embedded SW, which enables after-production of CFRP, CFRP-metal stack material, waterjet processing, inspection, and modification. As theThe characteristics of CFRP materials may create processing defects such as stratified material stripping and un-cut., a A process monitoring module that can minimize or prevent the defects this technology needs to should be applied to hence reducinge tool wear causedthrough by high hardness carbon fiber. Since CFRP is mostly made from additive forming, there are many drilling processes, that require precision measurement techniques and process signal monitoring technology, exist. Tsince the cutting force load and various signals generated during processing are weaker than those during metal processing. An open controller for process control and monitoring of a CFRP processing system was therefore developed. The system will then It is going to develop open controller SW structural design and open platform, multi-channel signal processing algorithm and sensor system, process specific functions (CFRP process control, boundary detection, etc.) and mount drilling tool parent monitoring algorithm on open platform.
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Comparative Analysis and Monitoring of Tool Wear in Carbon Fiber Reinforced Plastics Drilling Kyeong Bin Kim, Jang Hoon Seo, Tae-Gon Kim, Byung-Guk Jun, Young Hun Jeong Journal of the Korean Society for Precision Engineering.2020; 37(11): 813. CrossRef
Monitoring technology of machining operations has a long history since unmanned machining was introduced. Lots of research papers were presented and some of them has been commercialized and applied to shop floor. Despite the long history, many researchers have presented new approaches continuously in this area. This paper presents current state of monitoring technology of machining operations. The objectives of monitoring are shortly summarized, and the monitoring methods and the unique sensor technologies are reviewed. The main objective of the monitoring technology remains same; tool condition monitoring (TCM). The general approaches also remain similar; signal processing and decision making. But, the innovative methods for every step of process monitoring are being provided to improve the performance. More powerful computing is lowering the wall of much more data from more sensors by fast calculation. This technology also introduces the novel decision making strategies such as Artificial Intelligent. New materials and new communication technologies are breaking the limitation of sensor positions. Virtual machining technology which estimates the machining physics is being integrated with monitoring technology.
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Reducing the Loss Cost by Setting the Optimal Replacement Cycle for Cutting Tools using FOM-Tool Monitoring Jae Hoon Jang, Seon Jun Jang, Su Young Kim Journal of the Korean Society of Manufacturing Technology Engineers.2023; 32(3): 169. CrossRef
Tool Wear Monitoring System based on Real-Time Cutting Coefficient Identification Young Jae Choi, Ki Hyeong Song, Jae Hyeok Kim, Gu Seon Kang Journal of the Korean Society for Precision Engineering.2022; 39(12): 891. CrossRef
Prediction of Drill Bit Breakage Using an Infrared Sensor Min-Jae Jeong, Sang-Woo Lee, Woong-Ki Jang, Hyung-Jin Kim, Young-Ho Seo, Byeong-Hee Kim Sensors.2021; 21(8): 2808. CrossRef
Recent Developments and Challenges on Machining of Carbon Fiber Reinforced Polymer Composite Laminates Jaewoo Seo, Do Young Kim, Dong Chan Kim, Hyung Wook Park International Journal of Precision Engineering and Manufacturing.2021; 22(12): 2027. CrossRef
Cutting Force Estimation Using Feed Motor Drive Current in Cutting Process Monitoring Ki Hyeong Song, Dong Yoon Lee, Kyung Hee Park, Jae Hyeok Kim, Young Jae Choi Journal of the Korean Society for Precision Engineering.2020; 37(11): 803. CrossRef
Evaluation of the Grinding Performance of an Engine Block Honing Stone through Monitoring of Workload and Heat Generation Jang-Woo Yun, Sang-Beom Kim Journal of the Korean Society of Manufacturing Process Engineers.2019; 18(4): 69. CrossRef
Implementation of Wireless Condition Monitoring System in a Cutting Tool Using Accelerometer Yong Tae Kim, Yoo Su Kang, Hyung Jin Kim, Young Ho Seo, Byeong Hee Kim Journal of the Korean Society of Manufacturing Technology Engineers.2019; 28(3): 198. CrossRef
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