[Editor's Note] On November 18, the Technology Innovation Center of the Korea Institute of Technology Information Promotion (KIIT) held the 4th Joint Technology Exchange Meeting. Various presentations were given at this meeting under the themes of 'Big Data, AI, and Smart Manufacturing.' Among them, the Korea Institute of Industrial Technology (KITECH) presented a technology titled 'Cutting Process Monitoring and Machinability Evaluation,' which enables monitoring of operational phenomena during machining on machine tools and allows for the judgment and control of machining processes based on monitoring data. Accordingly, this publication arranged an interview with Choi Young-jae, a senior researcher at the Korea Institute of Industrial Technology who announced this technology, to learn more about the related technology.




■ Please introduce the Digital Transformation Research Division at the Korea Institute of Industrial Technology.

The Digital Transformation Research Division of the Korea Institute of Industrial Technology conducts research on DNA (Digital, Network, AI) specialized in the manufacturing industry, particularly in the equipment sector which can be considered a production system, and supports related technologies for small and medium-sized manufacturing enterprises.

My research focuses on the field of manufacturing equipment, specifically on real-time monitoring of the process mechanisms of machining equipment (lathes, milling machines, etc.), enabling operators and managers to recognize and control abnormal machining conditions or information regarding the machining process.

■ Please introduce 'Cutting Monitoring and Machinability Evaluation'.

The cutting process monitoring involves monitoring the machining status in real time through various types of CNC signals and sensors, diagnosing and controlling abnormal machining conditions (chatter, wear, etc.) from digital signals, and developing a system applicable to small and medium-sized enterprises so that companies can utilize it.

I am curious about what processing phenomena are and how they can be judged and controlled through monitoring.

During cutting, the tool wears out, abnormal vibrations occur during processing, and overloads occur depending on the age of the system.

This phenomenon can be considered an abnormal situation, and judgment is made by observing the signals from sensors and CNCs. However, in this study, the judgment is not made by the operator but is made through an algorithm based on a DB or training data.

However, so far, the control is only at the level of adjusting the RPM or the speed of the feed.

In the future, more intelligent control will be possible.

■ I would like to hear about the background of this research and how it differs from other similar studies.

The distinguishing feature of this study is that it aims for commercialization by developing monitoring technology that can be immediately applied to manufacturing SMEs, rather than intelligence research used by machine tool manufacturers to produce high-level processing equipment.

I would like to hear about the outlook on what economic effects and results are expected in the future using this technology.

Although a large workforce is still employed in manufacturing sites in Korea, unmanned and automated operations will eventually become essential in this field due to the perception of it as a 3D industry and a shortage of experts.

By providing support to help small and medium-sized enterprises prepare for this, we can contribute to preventing industrial hollowing out and improving productivity.

■ Finally, please say a few words to the readers.

If many companies take an interest and apply this, it can be utilized in a wider range of fields, so I hope you will join in the practical efforts of small and medium-sized enterprises.