Physical artificial intelligence (AI) company Makinarax is participating in a national research and development (R&D) project with a total budget of 11.8 billion won.
On October 14, Makinarax announced that it has been selected as a collaborative research and development institution for the '2026 Machinery and Equipment Industry Technology Development Project (Manufacturing-Based Production System)' led by the Korea Institute for Industrial Technology Planning and Evaluation (KEIT). The project is titled 'Development of an AI-Based Autonomous Production System for Hot Forging Mold Design Correction and Forged Parts.'
Hot forging is a plastic processing method that involves heating metal to over 1,000 degrees Celsius and applying pressure with a press weighing thousands of tons. When creating mold designs based on part drawings, the designer previously had to extract design parameters from the drawings one by one, leading to significant variations depending on the skill level of the worker.
To address this issue, Makinarax plans to utilize its AI operating system 'Runway' and the drawing decision-making AI agent 'DrawX.' The core of the project is to have AI recognize forging drawings, extract design parameters, and recommend mold design corrections based on this data. An integrated control system based on a digital thread will also be implemented on Runway, connecting design, process, and quality data, along with process anomaly diagnosis based on multi-sensor data such as temperature, load, and vision AI-based quality prediction for forged parts.
This initiative is expected to help overcome the skilled labor shortage currently faced by the manufacturing industry through AI-based autonomous production technology.
The project is set to run until December 2029, with 2.4 billion won allocated to Makinarax for R&D expenses.
Yoon Seong-ho, CEO of Makinarax, stated, "The forging process takes place in a high-temperature environment with high-intensity work, making it a field that has been considered difficult for AI application due to many variables and a high reliance on the worker's intuition. Through this demonstration, we aim to solve practical challenges in the industrial field and develop autonomous production technology."
* This article has been translated by AI.
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