- Automating AI and robot-based quality inspection eliminates bottlenecks in mass production of drones
- Modular application by airframe shape… Supports up to 750 quality inspections per day
- Deployable in up to 2 weeks… Expect 33% improvement in ROI on a case-by-case basis
AI mobility diagnostic solution company WEFLO announced that it has launched "FloOne," an AI and robot-based quality inspection automation platform that supports the transition of the defense drone manufacturing process to the dark factory.
Dark Factory refers to an unmanned automated factory that operates 24 hours a day without intervention by workers. It is a next-generation production model pursued by the global manufacturing industry to reduce labor costs and standardize quality, and the introduction of dark factories is also in full swing in the defense drone industry, which is rapidly expanding its scope of use such as reconnaissance, surveillance, strikes, and cluster management.
However, the quality inspection process at the drone manufacturing site has been considered an area with high automation difficulty in the process of converting the dark factory. The quality inspection, which checks major parts such as motors, propellers, gas balance, drive systems, and electronic abnormalities, and the condition of the gas, is still centered on worker experience and manual work. The resulting increase in inspection time and limitations in quality data management have been pointed out as major challenges in the dark factory transition.
FloOne was developed to solve bottlenecks that occur during the pre-shipment quality inspection stage during the drone manufacturing process. It collects physical data such as sound, vibration, and electromagnetic waves generated from drone aircraft through analog fusion sensors, and automatically diagnoses whether it is normal or abnormal through AI analysis combined with vision data. The inspection results are accumulated as data and automatically linked to the manufacturer's QMS (Quality Management System) and MES (Manufacturing Execution System). This can be used to build a data-driven production environment such as pre-shipment quality assurance, defect pattern analysis, and manufacturing history management.
WEFLO expects the speed of drone manufacturers to switch to the Dark Factory to speed up through FloOne. It is explained that by converting quality inspection, which was operated manually, to a data-based automation system, operational efficiency and quality reliability of the entire manufacturing process can be secured at the same time.
FloOne is not limited to a specific aircraft or manufacturing line, but is designed to be modular according to the customer's manufacturing process and drone type. Depending on the customer production environment, it is possible to build a site within up to two weeks, and it can support quality inspections of up to 750 drones per day.
WEFLO is also confirming the effect of improving productivity by automating quality inspection based on actual drone manufacturing and operation site application cases. Through joint development experience with global AAM OEM manufacturers and combat experiments and demonstration projects in the defense sector, ROI improvement of up to 33% was confirmed. In addition, the vertically AI-based inspection accuracy is differentiated from FlowOne in that it has secured 99.8% accuracy based on public certification standards, implementing a trust-based quality data system beyond simple automation.
As drones are mobility that can lead to operational risks even with small abnormalities, a system that verifies and manages quality based on data from the manufacturing stage is important, said Yeejung Kim, CEO of WEFLO. "We will make quality standards for defense, public, and industrial drone manufacturing sites based on Wiplo's vertical AI and multimodal fusion sensor technology."
