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In the manufacturing field, daily "awareness" such as "if we fix this, it will get better" and "I feel like there is a lot of waste in this task" is generated. However, many factories struggle with the issue that these insights do not take shape, and improvement activities do not continue. When voices like "I'm too busy to focus on improvements," "the results are hard to see even if we try," and "no one will notice anyway" overlap, improvement activities eventually become mere formalities. So, how can improvements take root as a "culture of the workplace" and continue to be revitalized? The Ministry of Economy, Trade and Industry's "Smart Manufacturing Construction Guidelines" presents a five-step framework to activate improvement activities as "Transformation Challenge Map No. 46" in response to these challenges. *For detailed content of the column, you can view it through the related link. For more information, please download the PDF or feel free to contact us.*

In recent years, the remarkable development of AI technology has particularly brought attention to an approach known as "Deep Unfolding (DU)." This is a technique that combines insights from traditional iterative algorithms with the powerful learning capabilities of deep learning. In this article, we will delve into the fundamental concepts of deep unfolding, provide explanations using specific mathematical formulas, discuss representative architectures, and explore its advantages and future prospects. *For detailed content of the column, please refer to the related links. For more information, feel free to download the PDF or contact us.*

In manufacturing sites, many people may have experienced a situation where "the machine suddenly stopped." When parts break or motors seize up, unexpected troubles can arise, halting production and delaying deliveries to customers. Especially in today's environments, maintenance of equipment (inspections and parts replacements) often relies on "experience and intuition," and by the time one notices that "something is wrong," it is often already too late. To reduce such troubles, it is very important to "detect signs (precursors) before a failure occurs." The Ministry of Economy, Trade and Industry has outlined solutions to these issues in the "Smart Manufacturing Construction Guidelines," presenting them as "Transformation Challenge Map No. 44." This guideline aims to establish a system for "detecting precursors of equipment malfunctions and maintaining stable operations." *For more detailed information, you can view the related links. For further details, please download the PDF or feel free to contact us.

In the manufacturing industry, maintenance is crucial for continuing production, and the collection of data from production equipment and the identification of factors causing malfunctions have become important challenges. However, in factories where numerous machines are operating, the differences in data formats and communication methods for each piece of equipment make data collection and management difficult. This article explains the construction of a centralized monitoring system for PLCs utilizing the KV series. *For detailed content of the column, you can view it through the related link. For more information, please download the PDF or feel free to contact us.*

Large language models (LLMs) generate text by predicting the next word in a sentence. There is active research on applying this characteristic to time series data for time series forecasting using LLMs. This time, I would like to introduce a paper focused on anomaly detection in time series titled "Large language models can be zero-shot anomaly detectors for time series?" In this paper, the authors convert time series data into text and apply the language processing capabilities of LLMs to investigate how well they can handle the non-linguistic task of "anomaly detection in time series" without additional training (zero-shot). *For more details on the column, you can view it through the related links. For more information, please feel free to download the PDF or contact us.*

In the manufacturing industry, maintenance is crucial for continuing production, and the collection of data from production equipment and the identification of factors causing malfunctions have become important challenges. However, in factories where numerous machines are operating, the differences in data formats and communication methods for each piece of equipment make data collection and management difficult. This article explains the construction of a PLC centralized monitoring system utilizing the KV series. *For detailed content of the column, you can view it through the related links. For more information, please download the PDF or feel free to contact us.*

The development of CNC has greatly advanced the control technology of cutting machines; however, several challenges still exist that limit processing performance. These challenges stem from the physical complexity of the machining process itself, indicating the need for more advanced control strategies. *For more detailed information, please refer to the related link. For further details, you can download the PDF or feel free to contact us.*

When building control programs in CODESYS, as the project grows larger, it is common to encounter logic that you want to use across multiple programs or function blocks (FB) that you want to reuse. This is where the library system comes into play. CODESYS already has many libraries available, supporting our development with standard functions and communication protocol compatibility. However, it is actually quite easy for us to create our own libraries as well. In this article, we will introduce how to create a library in CODESYS and the steps to use that library in a project. *For more detailed information, you can view it through the related links. For more details, please download the PDF or feel free to contact us.*

In manufacturing sites, when a veteran technician retires, have you ever experienced a sudden instability in quality or a decline in productivity? The "Smart Manufacturing Construction Guidelines" formulated by the Ministry of Economy, Trade and Industry presents solutions to these challenges as "Transformation Challenge Map No. 33." This guideline aims to achieve the effective promotion of automation and unmanned operations by appropriately incorporating robotics to assist human work in manufacturing. So, how close is your factory to this vision of realization? *For more detailed information, you can view it through the related links. For further details, please download the PDF or feel free to contact us.*

In the manufacturing industry, maintenance is crucial for continuing production, and the collection of data from production equipment and the identification of factors causing malfunctions have become important challenges. However, in factories where numerous machines are operating, it is difficult to collect and manage data due to differences in data formats and communication methods for each piece of equipment. This article explains the construction of a PLC centralized monitoring system utilizing the KV series. *For more detailed information, you can view the related links. For further details, please download the PDF or feel free to contact us.*