5.1 Trend one: AI vision moves from "recognition" to "understanding"
Conventional vision algorithms rely on feature extraction and template matching, and still need manual teaching for new products. In the next phase, deep-learning-based vision models will enable "zero-teaching" or "few-shot teaching"—after a new workpiece comes online, the algorithm auto-learns product features and generates the trajectory.
5.2 Trend two: from single-machine intelligence to line-wide intelligence
Vision-following dispensing will no longer be an isolated machine; it will form "cell-level" line units together with loading, inspection, labeling, AOI and robotic palletizing. Machines interconnect via industrial protocols such as OPC-UA, Profinet and CC-Link, writing process data back to MES.
5.3 Trend three: multi-process integration
Dispensing, spraying, potting, labeling and AOI inspection will gradually merge onto one platform—a single machine completes the "recognize + dispense + inspect" trinity, further compressing takt and line footprint.
5.4 Trend four: calibration-free, plug-and-play
Over the next 3–5 years, leading vendors such as SukiAuto will release standardized models with "factory pre-calibration + one-button on-site start," compressing the time from unboxing to powered-up dispensing to within 2 hours.
5.5 Trend five: accelerated domestic substitution
Over the past decade, the high-end vision-dispenser market has long been dominated by German and Japanese brands. As domestic vendors like SukiAuto achieve self-controllable core components in algorithms, servos and control cards, the market penetration of domestic vision-following dispensers will keep rising.
