A pattern shows up across smart factory projects delivered in the past two years: once the MES, SCADA, and ERP systems are in place, the "AI control" layer is the one most likely to be left hanging — either a veteran operator watches the line by experience, or it simply never gets built.
How to automate quality inspection, how to predict equipment failure days in advance, how to adjust line pacing dynamically — traditional automation systems cannot answer these questions. That is exactly the gap industrial AI control fills.
Not every production line justifies this layer. From real projects, scenarios that meet the conditions below tend to have a calculable return:
Visual quality inspection. Products with clear defect signatures — scratches, missing parts, wrong assembly, printing deviations. Human eyes fatigue after hours of staring; cameras with edge inference do not. Data volume is large and pass/fail criteria are well defined, making this the easiest entry point for AI control. Typically one industrial computer connected to multiple industrial cameras is enough to start.
Predictive maintenance. Equipment with rotating parts — CNC machines, fans, pumps — often shows anomalies in spindle vibration, temperature, or current days before an actual failure. A CNC protocol gateway or vibration sensors keep the data flowing, the edge side runs trend analysis, and alarms trigger maintenance before breakdown. Best suited to workshops with many machines and high downtime costs.
Process parameter optimization. Injection molding, welding, coating — processes where parameters constrain each other and tuning relies on operator experience. Once enough historical data accumulates, models can suggest parameter adjustments. This type pays off slowly but reuses best: once one line is tuned, the model extends to similar lines.
On-site safety. Hard-hat detection, zone-intrusion alerts, forklift-pedestrian warnings. Images never leave the plant; inference runs at the edge and drives audible-visual alarms directly, keeping compliance pressure to a minimum.
Conversely, if a line runs only a few dozen product variants at a slow pace and one experienced operator can keep up, this layer can wait — collect and store the data first, and add the model later once enough has accumulated.
In the projects we have seen, the first idea is usually the same: stream all video and sensor data to the cloud and run inference there.
Three problems surface once implementation begins:
① Latency.A round trip of several hundred milliseconds is clearly too slow for tight-paced processes.
② Bandwidth and cost.Dozens of HD video streams plus high-frequency vibration data pushed over the public internet makes bandwidth costs hard to contain.
③ Data compliance.On-site images and process parameters are, in many projects, simply not allowed to leave the factory.
The more dependable approach is a three-layer architecture: data acquisition → edge AI control → cloud platform. The cloud handles training, cross-plant comparison, and long-term archiving; real-time inference and on-site interlocking stay at the edge.
Referring to a typical industrial AI control topology, the role of each layer looks like this:
Data acquisition layergathers field data. Common building blocks include:
Data acquisition gateways for CNC machines — models with built-in protocol libraries for mainstream CNC brands save substantial protocol adaptation work.
Modular edge gateways with graphical programming, which field engineers can configure themselves.
Remote IO, 4G cellular modems, low-power acquisition devices, and similar units to fill gaps depending on site wiring conditions.
Edge AI control layeris the local brain of the system.
For compute-heavy scenarios, choose an ARM/x86 industrial computer (such as the EG628) for local model inference and vision inspection, pushing results straight down to the PLC.
For lighter scenarios, a dedicated Edge AI Box (such as the EG928), paired with a touchscreen and an operation guidance display for local HMI and anomaly alerts.
This layer is also the part of the system that is visible and tangible inside the plant — the set of equipment field maintenance staff face every day.
Cloud platform layercovers cross-plant analytics, model iteration, and visualization dashboards. For projects where data must stay in-house, a private deployment path avoids the compliance risk.
After walking through several delivered projects, a few things are worth clarifying early:
Certifications for target markets. Beyond CE, FCC, and ROHS, entering the EU, Latin America, or Southeast Asia may also call for RCM, ANATEL, SRRC, and similar approvals, which differ by region. The EG628 covers RCM, 3C, ANATEL, CE, FCC, NBTC, ROHS, WEEE, and network security certifications, which is friendly for export-oriented deployments; the EG928 holds 3C, CE, FCC, ROHS, and WEEE, suitable for most mainstream industrial markets.
Interface density. An industrial computer typically needs gigabit Ethernet, USB, serial ports, and display outputs simultaneously, and some scenarios also need GPIO/DIO. The EG628 has a built-in DIO interface that connects directly to field digital signals, removing one layer of external IO modules.
Balancing compute and power. Edge inference demands compute, but industrial sites have limited cooling and power budgets — the balance between compute, power draw, and stability has to be found deliberately.
Cloud deployment model. Private deployment is friendlier to data security but shifts operational cost upward; public cloud is faster to onboard but requires careful data-compliance handling.
Projects tend to go smoothly when advanced in this sequence:
Define the scenario first. Visual inspection, equipment health prediction, or process parameter optimization? Each imposes very different requirements on compute, camera count, and sensor types. Locking this down early keeps later hardware selection from going astray.
Then fix the acquisition layer. Decide which fields from which devices to collect — PLCs, drives, and CNC machines all speak different protocols. Getting the data flow working with one gateway that supports mainstream protocols prevents the AI stage from stalling on data.
Select edge hardware last. Only after the scenario and acquisition layers are clear does edge AI control hardware (industrial computer / Edge AI Box) have a basis for selection — how much compute to reserve, whether interfaces suffice, and how to provision expansion slots are decisions that belong to this stage.
