NC machining data, which cause excessive cutting force, accelerate tool wear, reduce the roughness of machined surfaces, and in severe cases, result in tool breakage and material waste. Thus, the cutting conditions should be optimized according to the material-spindle speed-feed rate combination. However, it is very difficult to perfectly predict and optimize the dynamic characteristics of machining, such as tool vibration and wear, and spindle thermal deformation. Further, predicted tool paths are accompanied by machining errors. This study proposes an advanced adaptive control method that can balance the machining load, improve tool life, and reduce machining time. The proposed method 1) synchronizes the spindle load and NC-data and stores it, 2) analyzes the stored data to create a reference curve that can balance the machining load, 3) adjusts the tool feed rate using a reference curve, 4) engages rapid traverse when the load is small, and 5) applies an approach feed rate when the tool approaches a workpiece, reducing the impact on the tool when the tool meets the workpiece. Case examples proved that the use of the proposed balanced load reduced machining time and increased tool life.
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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
Transfer robots for large-sized panels used in the display industry need to compensate for path error and reduce vibration. The iterative learning control (ILC) technique can simply compensate for the uncertainty of a control system in a repetitive motion. This study introduces an ILC compensation system applied with an accelerometer to a display panel transfer robot control system. The ILC technique was used to reduce the path error and vibration induced the flexibility of the large size robot. This method was applied to a robot system without the system model of the mechanical and measurement elements. To improve the iterative learning performance through the accelerometer, the ILC is configured by applying an acceleration element and time shift method to the PD-Offline ILC algorithm. In addition, based on the characteristics of repetitive motion, the ILC derives an acceleration data-based position estimation value. In this study, the ILC system and a large-sized panel transfer robot were implemented in MATLAB-Simulink with RECURDYN. The path errors and vibration level of the robot with a suggested ILC of 20 repeated learnings were reduced by more than 90%.
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In this study, the behavior of the driver was derived by conducting a crash simulation considering automated vehicle accident conditions using autonomous emergency braking (AEB) and a human body model (HBM). Based on car-to-car intersection accident conditions in the OSCCAR project and the actual accident report, a crash accident case was selected. The base crash scenario was reconstructed by conducting a driving simulation with reference to the selected accident cases. Additional simulations applying AEB are performed. Based on the boundary conditions, a car-to-car crash simulation was performed to derive a crash pulse. This crash pulse and HBM were applied to a simple cabin model for conducting driver behavior analysis. The results confirmed that the head behavior of the driver of the opposing vehicle increased in the lateral direction.
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Vehicle-motion-based Front Wheel Steer Angle Estimation for Steer-by-Wire System Fault Tolerance Seungyong Choi, Wanki Cho, Seung-Han You Journal of the Korean Society for Precision Engineering.2024; 41(5): 347. CrossRef
In this study, we designed and manufactured a 3-axis force sensor for an intelligent gripper that safely grips an unknown object. The 3-axis force sensor consists of an Fx force sensor, an Fy force sensor, and an Fz force sensor in one body, and is manufactured by attaching a strain gauge. The characteristics evaluation showed that the rated output error was within 0.2, the nonlinearity error was within 0.05, and the reproducibility error was within 0.06%. Therefore, the 3-axis force sensor designed and manufactured in this study can be used to measure weight and control the force used to grip an unknown object by attaching it to the intelligent gripper.
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Control Method of Electric Gripper Using Current Control System Ji-Hye Min, Gab-Soon Kim Journal of the Korean Society for Precision Engineering.2023; 40(9): 725. CrossRef
Development of an Intelligent Gripper that Determines the Gripping Force According to the Weight of the Object Han-Sol Kim, Gab-Soon Kim International Journal of Precision Engineering and Manufacturing.2023; 24(12): 2259. CrossRef
Research on the automation of many types of construction equipment, including motor graders, is being actively conducted. In a motor grader cabin, the operator has difficulty observing the working environment because of a constructed field of view. Thus, workers rely on their experience and senses. Further, the working environment of the blade must be observed, and a control algorithm should be created to enable autonomous operation. In this study, a blade rotation control strategy considering the soil distribution was proposed. First, a co-simulation environment was constructed using RecurDyn for multibody dynamics analysis and EDEM for discrete element method simulation, and simulations were performed to determine the correlation between soil distribution and the blade rotation angle. Work quality and blade load were analyzed according to the simulation results. The optimal blade rotation angle according to soil distribution was obtained to develop the strategy for autonomous flattening and scattering work. The proposed control strategy was implemented in a 1/4 full-scale motor grader experimental setup. An experiment to evaluate work quality was conducted to validate the effectiveness of the proposed methods. The experimental results indicated that the proposed strategy effectively performed scattering work.
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Path Planning Strategy for Implementing a Machine Control System in Grader Operations Jae-Yoon Kim, Jong-Won Seo, Wongi S. Na, Sung-Keun Kim Applied Sciences.2024; 14(20): 9432. CrossRef
Due to recent development of sensor technology and IoT, research is being actively conducted on PHM (Prognostics and Health Management), a methodology that collects equipment or system status information and determines maintenance using diagnosis and prediction techniques. Among various research studies, research on anomaly detection technology that detects abnormalities in assets through data is becoming more important due to the nature of industrial sites where it is difficult to obtain failure data. Conventional machine learning-based and statistical-based models such as PCA, KNN, MD, and iForest involve human intervention in the data preprocessing process. Thus, they are not suitable for time series data. Recently, deep learning-based anomaly detection models with better performances than conventional machine learning models are being developed. In particular, several models with improved performance by fusing time series data with LSTM, AE (Autoencoder), VAE (Variational Auto Encoder), and GAN (Generative Adversarial Network) are attracting attention as anomaly detection models for time series data. In the present study, we present a method that uses Likelihood to improve the evaluation method of existing models.
Interest in the use of thin film of Ruthenium-Samaria doped ceria cermet (Ru-SDC) as anode in solid oxide fuel cells is increasing due to its high oxygen storage capacity and high chemical and thermal stability. To have enough structural integrity between sputtered Ru-SDC films and underlying substrates, good adhesion property is required. In this work, scratch resistance and failure mode for Ru-SDC films with various SDC composition were investigated using a scratch test method employing linearly increasing load from 1 to 50 N using a 200 μm radius Rockwell C indenter. Scratched surfaces were examined with a field emission scanning electron microscope. Chemical compositions in scratch tracks were analyzed by energy dispersive X-Ray spectroscopy. Critical loads for films with different SDC ratios were assessed and associated failure modes were identified. The highest scratch resistance among tested film compositions was the one that contained 50% of SDC. Failure modes of tested films regardless of the ratio of SDC were identified to be the initiation of tensile cracks with rapid increase of friction coefficient followed by chipping, and eventually the generation of a severe crack network.
Hastelloy-X material is widely used in aircraft engines, furnaces, and chemical process components due to its excellent oxidation resistance and high-temperature strength. In the case of making plate-shaped parts, its quality can be improved by forming limit diagram (FLD), which can predict crack and failure of a product. However, experimental-based FLD can be costly and time-consuming. In this paper, we tried to predict the formability of Hastelloy-X through FE simulations using the GTN (Gurson-Tvergaard- Needleman) model. First, appropriate values for GTN model parameters were derived from RSM using tensile test. FLD based on GTN model was then obtained by applying derived parameters to FLD simulations. These obtained parameters can be used to predict the formability of sheet metal undergoing severe deformation processes in aircraft and gas turbine engine manufacturing.
Relative position estimation between body segments is one essential process for inertial sensor-based human motion analysis. Conventionally, the relative position was calculated through a constant segment to joint (S2J) vector and the orientation of the segment, assuming that the segment was rigid. However, the S2J vector is deformed by soft tissue artifact (STA) of the segment. In a previous study, in order to handle the above problem, Lee and Lee proposed the relative position estimation method using time-varying S2J vectors based on inertial sensor signals. Here, time-varying S2J vectors were determined through the joint flexion angle using regression. However, it was not appropriate to consider only the flexion angle as a deformation-related variable. In addition, regression has limitations in considering complex joint motion. This paper proposed artificial neural network models to compensate for the STA by considering all three-axis motion of the joint. A verification test was conducted for lower body segments. Experimental results showed that the proposed method was superior to the previous method. For pelvis-to-foot relative position estimation, averaged root mean squared error of the previous method was 17.38 mm, while that of the proposed method was 12.71 mm.
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Wearable Inertial Sensors-based Joint Kinetics Estimation of Lower Extremity Using a Recurrent Neural Network Ji Seok Choi, Chang June Lee, Jung Keun Lee Journal of the Korean Society for Precision Engineering.2023; 40(8): 655. CrossRef
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