• Journal of Dental Rehabilitation and Applied Science
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• v.40 no.3
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• pp.169-178
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• 2024

Recently, several attempts have been made to integrate AI into the field of dentistry. To overcome the limitations of traditional fixed prosthetic fabrication methods such as CAD-CAM (computer-aided design-computer-aided manufacturing), AI programs are being developed for automated crown fabrication, and various studies are underway to applicate in clinical situation. In these case studies, single-unit fixed prostheses were fabricated using an AI program (Dentbird Crown, Imagoworks Inc, Seoul, Korea) in both the anterior and posterior regions and the fabrication time and accuracy were compared with previously used CAD-CAM method. The first case is a 44-year-old woman who presented for re-fabrication of a zirconia prosthesis due to a prosthesis fracture on the lingual side of the upper right lateral incisor. The second case is a 53-year-old male patient who presented for a crown restoration on an upper left first molar following root canal treatment, where he received a final zirconia restoration. In both cases, the first prosthesis was designed manually using a CAD program, the second prosthesis was designed using AI alone, and the third prosthesis was designed using AI and then modified by CAD program, and the three designs were superimposed to compare suitability. When evaluated after temporary placement, the final prosthesis demonstrates adequate stability, retention and support, resulting in functional and esthetic satisfaction.

• Journal of Platform Technology
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• v.8 no.3
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• pp.22-29
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• 2020

The 4th industrial revolution is progressing by country as a mega trend that leads various technological convergence directions in the social and economic fields from the initial simple manufacturing innovation. The epidemic of infectious diseases such as COVID-19 is shifting digital-centered non-face-to-face business from economic operation, and the use of AI and big data technology for personalized services is essential to spread online. In this paper, we analyze cases focusing on the application of artificial intelligence technology, which is a key technology for the effective implementation of the digital new deal promoted by the government, as well as the major technological characteristics of the 4th industrial revolution and describe the use cases in the field of disaster response. As a disaster response use case, AI assistants suggest appropriate countermeasures according to the status of the reporter in an emergency call. To this end, AI assistants provide speech recognition data-based analysis and disaster classification of converted text for adaptive response.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.216-221
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• 2022

The 4th industrial revolution is accelerating the transition to digital innovation in various aspects of our daily lives, and efforts for manufacturing innovation are continuing in the manufacturing industry, such as smart factories. The 4th industrial revolution technology in manufacturing can be used based on AI, big data, IoT, cloud, and robots. Through this, it is required to develop a technology to establish a production facility data collection and analysis system that has evolved from the existing automation and to find the cause of defects and minimize the defect rate. In this paper, we implemented a system that collects power, environment, and status data from production facility sites through IoT devices, quantifies them in real-time in a cloud computing environment, and displays them in the form of MQTT-based real-time infographics using widgets. The real-time sensor data transmitted from the IoT device is stored to the cloud server through a Rest API method. In addition, the administrator could remotely monitor the data on the dashboard and analyze it hourly and daily.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3330-3344
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• 2023

This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.29-35
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• 2019

The structure of the machinery industry due to the 4th industrial revolution is changing from precision and durability to intelligent and smart machinery through sensing and interconnection(IoT). There is a growing need for research on prognostics and health management(PHM) that can prevent abnormalities in processing machines and accurately predict and diagnose conditions. PHM is a technology that monitors the condition of a mechanical system, diagnoses signs of failure, and predicts the remaining life of the object. In this study, the vibration generated during machining is measured and a classification algorithm for normal and fault signals is developed. Arbitrary fault signal is collected by changing the conditions of un stable supply cutting oil and fixing jig. The signal processing is performed to apply the measured signal to the learning model. The sampling rate is changed for high speed operation and performed machine learning using raw signal without FFT. The fault classification algorithm for 1D convolution neural network composed of 2 convolution layers is developed.

• Journal of Venture Innovation
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• v.2 no.1
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• pp.1-11
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• 2019

Many research or consulting institute refered to Artificial Intelligence, Internet of Things, Blockchain technology and 3D Printing as key driving forces and technologies of 4th industrial revolution. Compared with traditional manufacturing as a subtractive manufacturing(SM), 3D printing technology as an additive manufacturing(AM) will revolutionary impacts on many industries. This study compared 3D printing with traditional manufacturing in the economic, manufacturing, and marketing perspectives. This study also analyzed issues of 3D printing for the purpose of building business ecosystem. Finally agenda for the further research were suggested.

• Journal of Fashion Business
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• v.27 no.4
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• pp.141-162
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• 2023

With the prospect that integrating creative AI in the fashion design field will become more visible, this study considered the case of creative fashion design development through Human-AI Co-creation (HAIC). Methodologically, this research encompasses a literature review and empirical investigations. In the literature review, the fashion design and creative HAIC processes, and the possibilities of integrating AI in fashion design were considered. In the empirical study, based on the case analysis of generating fashion design through HAIC, the HAIC type according to the role and interaction method, and characteristics of humans and AI was considered, and the HAIC process for fashion design was derived. The results of this study are summarized as follows. First, HAIC types in fashion design are divided into four types: AI-driven passive HAIC, human-driven passive HAIC, flexible interaction-based HAIC, and integrated interaction-based value creation HAIC. Second, the stages of the HAIC process for creative fashion design can be broadly divided into semantic data integration, visual ideation, design creation and expansion, design presentation, and design/manufacturing solution and UX platform creation. Third, in fashion design, HAIC contributes to human ability, enhancement of creativity, achievement of efficient workflow, and creation of new values. This research suggests that HAIC has the potential to revolutionize the fashion design industry by facilitating collaboration between humans and AI; consequently, enhancing creativity, and improving the efficiency of the design process. It also offers a framework for understanding the different types of HAIC and the stages involved in the creative fashion design process.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.355-359
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• 2001

In this study, experiments were carried out to investigate the characteristics of grinding and wear process of diamond wheel grinding ceramic materials. Normal component of grinding resistance of $AI_2O_3$ was less then that of $Si_3N_4$ and $ZrO_2$. It is because the resistance for grain shedding is less then that for layer formation. For the case of $Si_3N_4$ and $ZrO_2$, as the grain mesh number of wheel increases, the surface roughness decreases. For the case of $AI_2O_3$, the surface roughness does not decreases. For the case of $Si_3N_4$ and $ZrO_2$, grinding is carried out by abrasive wear processes. For the case of $AI_2O_3$, grinding is carried out by grain shedding process.

• Journal of Technologic Dentistry
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• v.37 no.1
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• pp.23-29
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• 2015

Purpose: The selective laser melting (SLM) process for dentistry, which is one of the additive manufacturing technologies (AM) allows for rapid production of a three-dimensional model with complex shape by directly melting metal powder. This process generates detailed items of a three-dimensional model shape through consolidation of a thin powder layer by utilizing both selective melting and laser beam simultaneously. In regard to SLM process, Fe-base powder, Ti-6AI-4V powder, AI-base powder, etc. have been researched. It is believed that the aforementioned technologies will be widely utilized in manufacturing metal parts using metal powder of raw material. This study chose Ni-Cr-Mo metal powder in order to manufacture metal powder materials that would be used in the selective laser melting for dentistry. Methods: This study manufactured metal powder using mechanical alloying technique (MA) among those metal powder manufacturing techniques. Moreover, this study aimed to utilize the metal powder manufactured after observing the characteristics of powder as preliminary data of Ni-Cr-Mo metal powder. This study could obtain the following conclusions within the experimental limitations. Results: As a result of mechanically alloying Ni-Cr-Mo powder over time, its mean particle size was $66.93{\mu}m$ $54.4{\mu}m$ and $45.39{\mu}m$ at 10h, 20h and 30h, respectively. The gtain form of metal powder by mechanical alloying technique was a sponge-like shape of irregular plate; however, the gtain form manufactured by high-pressure water aromization process had the following three types: globular type, chain type and oval type. Conclusion: This study found $37.65{\mu}m$ as the mean particle size of Ni-Cr-Mo metal powder, which was manufactured using water atomization technique under the following conditions: water atomization flux of 300 liter/min, hydraulic pressure of $400kgf/cm^2$ and injection angle of $45^{\circ}$. This study confirmed that the grain form of powder (solid particle form) would vary depending on the manufacturing process.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_2
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• pp.247-255
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• 2022

Robot Process Automation (RPA) is a computer technology called Robotic Process Automation, a form of business process automation based on the concept of software robots or artificial intelligence (AI) walkers. In general, in traditional workflow automation tools, software developers design software that creates a set of actions to automate tasks and interfaces for the back-end systems using internal APIs or dedicated script languages. However, in RPA software, automation can be implemented by configuring an operating processor as if the general user is directly performing the task of the application. In other words, it can be said that it is a suitable development method for automating simply repetitive tasks rather than developing specific programs in which all necessary functions are implemented, as in general software development. Thus, this is more appropriate for configuring and automating RPA software in traditional manufacturing companies that are not easy to develop and apply smart factories or high-end AI software. Therefore, this research aims to analyze the requirements required at the actual manufacturing companies, focusing on the manufacturer's case in Changwon, Gyeongsangnam-do, called SinceWin Co., Ltd., and to examine the possibility of RPA software in the manufacturing companies by implementing actual RPA software that supports office automation. Through the research, it was confirmed that the actually implemented RPA software met the requirements of the company and helped manufacturer practice significantly by automating the parts that were worked error-prone and manually periodically.