In the case of TV products, space constraints and design requirements make it advantageous to use a worm gear that has a small volume and a self-locking function. Single enveloping worm gear teeth are classified as ZA, ZN, ZK, ZI, and ZC according to international standards. However, combining worm shafts and worm wheels with different tooth profiles can significantly worsen meshing transmission errors and reduce the lifespan of the worm gear. Despite these challenges, due to processing limitations, ease of manufacturing, and cost reduction, combinations of worm shafts and worm wheels with different tooth profiles are still considered. In this study, we confirmed the meshing transmission error for a worm gear that combined a ZA tooth shape worm shaft with a ZI tooth shape worm wheel. Additionally, we examined the contact stress and fatigue life characteristics of the material combinations using finite element analysis (FEM).

This study proposed a conditional generative adversarial network (cGAN) model for predicting steel plate deformation based on heating line positions in a line heating process. A database was constructed by performing finite element analysis (FEA) to establish relationships between heating line positions and deformation shapes. Deformation shapes were converted into color map images. Heating line positions were used as conditional labels for training and validating the proposed model.
During the training process, generator and discriminator loss values, along with MSE and R² metrics, converged stably, demonstrating that generated images closely resembled the actual data. Validation results showed that predicted deformation magnitudes had an average relative error of approximately 3% and a maximum error of less than 7%. These findings confirm that the proposed model can effectively predict steel plate deformation shapes based on heating line positions in the line heating process, making it a reliable predictive tool for this application.

Holonic Manufacturing Systems (HMSs) are regarded as a foundation of cyber-physical production systems as they enable computers to conduct intelligent process planning, scheduling, and control by endowing manufacturing components with autonomy and collaboration. In an HMS, autonomy is realized by specifying holons that represent virtual agents of manufacturing components, while collaboration is facilitated through a communication mechanism that enables data exchange and decision making throughout a holarchy of holons without human intervention. This study presents the development of a virtualized holon model and a predictive process planning procedure using the Asset Administration Shell (AAS), i.e., a standardized model that can identify digital representation of manufacturing components to ensure interoperability. Specifically, an AAS-based information model was proposed to define operator, machine, product, and order holons. In addition, a predictive process planning procedure based on the Contract Net Protocol was developed to automatically allocate tasks while predicting task execution times. This study can contribute to the designing of an AAS- domain specific information model for HMS to increase interoperability in the holon holarchy, exhibiting the feasibility of AAS applications in predictive process planning on HMS.

This paper proposes an algorithm to improve path planning and tracking performance for autonomous robots using a Four- Wheel Steering (4WS) system in constrained environments. Traditional Ackermann steering systems face limitations in narrow spaces, which the 4WS system aims to address. By extending the Hybrid A* algorithm to adapt to the unique characteristics of the 4WS system, and integrating it with Model Predictive Control, the study achieves efficient path planning and precise tracking in complex environments. A distinctive aspect of the proposed approach is its adaptive control strategy, dynamically switching between three modes—Normal driving, Pivot, and Parallel movement—based on the vehicle's motion state, thus enhancing both flexibility and efficiency. The algorithm's performance was validated through MATLAB simulations in a logistics warehouse setting, showing high path tracking accuracy in confined spaces. The study effectively demonstrates the feasibility of the proposed method in a simulated environment.

In continuous-process systems, failures of rolling-element bearings typically cause accidents, reduced productivity, and production-related financial losses. Therefore, predicting both the lifespan of rolling-element bearings and their replacement time is crucial for preventing machine system failures. Accordingly, numerous studies have reported various machine and deep learning classifiers for predicting the lifespan of bearings. However, these studies did not consider degradation trends of bearings. Thus, this study aimed to develop an algorithm to predict the lifespan of a bearing by considering its degradation trend. A vibration dataset of bearings was obtained at low and high speeds. Using a second-order curve-fitting model, various degradation patterns in the dataset were classified. Appropriate time-domain or frequency-domain feature variables applicable to the design of a classifier were determined according to classified patterns. In addition, the classifier was trained using multiple bidirectional long short-term memories. Finally, the performance of the developed classifier was verified experimentally.

Predicting fall risk is necessary for rescue and accident prevention in the elderly. In this study, deep learning regression models were used to predict the acceleration sum vector magnitude (SVM) peak value, which represents the risk of a fall. Twenty healthy adults (aged 22.0±1.9 years, height 164.9±5.9 cm, weight 61.4±17.1 kg) provided data for 14 common daily life activities (ADL) and 11 falls using IMU (Inertial Measurement Unit) sensors (Movella Dot, Netherlands) at the S2. The input data includes information from 0.7 to 0.2 seconds before the acceleration SVM peak, encompassing 6-axis IMU data, as well as acceleration SVM and angular velocity SVM, resulting in a total of 8 feature vectors used to model training. Data augmentations were applied to solve data imbalances. The data was split into a 4 : 1 ratio for training and testing. The models were trained using Mean Squared Error (MSE) and Mean Absolute Error (MAE). The deep learning model utilized 1D-CNN and LSTM. The model with data augmentation exhibited lower error values in both MAE (1.19 g) and MSE (2.93g²). Low-height falls showed lower predicted acceleration peak values, while ADLs like jumping and sitting showed higher predicted values, indicating higher risks.

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.

The subtle feature is one of the characteristic lines and represents the most noticeable line in the automotive panel. In this study, we proposed a method to predict the radius of curvature of products according to the material, its thickness, its punch angle, and its punch radius. The radius of curvature was divided into three regions, namely, the non-linear, transition, and linear regions. In the non-linear region, the prediction model for the radius of curvature with different forming conditions was derived using the finite element analysis. In the linear region, the radius of curvature was assumed to be the sum of the punch radius and the thickness of the material. In the transition region, a model connecting two regions (Non-linear and linear region) was developed based on the continuity condition. The prediction model presented a very small RMSE with the value of 0.314 mm. Using the prediction model, the radius of curvature with various forming variables could be predicted and the required radius of punch, to obtain a certain value of the radius of curvature, could be precisely predicted.

Recently, interest in Prognostics and Health management (PHM) has been increasing as an advanced technology of maintenance. PHM technology is a technology that allows equipment to check its condition and predict failures in advance. To realize PHM technology, it is important to implement artificial intelligence technology that diagnoses failures based on data. Vibration data is often used to diagnose the state of the rotating machine. Additionally, there have been many efforts to convert vibration data into 2D images to apply a convolutional neural network (CNN), which is emerging as a powerful algorithm in the image processing field, to vibration data. In this study, a series of PHM processes for acquiring data from a rotary machine and using it to check the condition of the machine were applied to the rotary table. Additionally, a study was conducted to introduce and compare two methodologies for converting vibration data into 2D images. Finally, a GUI program to implement the PHM process was developed.

Bone plates made of biodegradable polymers have been used to fix broken bones. 3D printers are used to produce the bone plates for fracture fixing in the industry. The dimensional accuracy of the product printed by a 3D printer is less than 80%. Fracture fixing plates with less than 80% dimensional accuracy cause problems during surgery. There is an urgent need to improve the dimensional accuracy of the product in the industry. In this paper, a methodology using machine learning was proposed to improve the dimensional accuracy. The proposed methodology was evaluated through case studies. The results predicted by the machine learning methodology proposed in this paper and the experimental results were compared through the experiment. After verification, results of the proposed prediction model and the experimental results were in good agreement with each other.

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.

It is important to minimize electric energy consumption of a data center that uses enormous electricity for maintaining an adequate indoor temperature. Most data centers have applied the outdoor air cooling method on account of economic feasibility. However, it is necessary that data centers have an efficient control method in order to achieve extra energy savings. In this paper, we propose an artificial intelligence based real-time optimal control method that minimizes electricity consumption and assures safe operation simultaneously. The main idea of our proposed method is to evolutionary search the optimal range of controlled variable during a normally operative condition. Furthermore, an optimal operating condition can be achieved without requiring large-scale data to learn a model. Experimental results demonstrate that indoor temperature of a data center can be constantly controlled safely and cost effectively based on our proposed methodology.

Lithium-ion batteries are one of the main parts of electrical devices and are widely used in various applications. To safely use lithium-ion batteries, fault diagnosis and prognosis are significant. This paper analyzes resistance parameters from electrochemical impedance spectroscopy (EIS) to detect the fault of lithium-ion batteries. The internal fault mechanisms of batteries are so complex; it is difficult to detect abnormalities by direct current-based methods. However, by using alternating-current-based impedance by EIS, the internal degradation processes of the batteries can be detected. Impedance variation from EIS is verified under accelerated degradation test conditions and normal cycling test conditions. The results showed a significant relationship between fault and increase in resistance.

Despite the importance of the usage stage in life cycle assessment (LCA), there is a lack of comprehensive studies on the usage stage modeling. Based on the literature review, this paper establishes a general framework of the usage stage modeling by redefining existing models and proposing new models. The proposed computational framework can provide the overview of the current research as well as lead researchers and practitioners to consider proper modeling techniques. The framework includes the representative usage scenario method, usage context modeling, and time series usage modeling. Also, future research directions are suggested with the proposed computational framework.