Industrial PCs for machine vision systems
A GigE camera streams 125 MB/s per port. Four cameras need four independent channels, a PCIe x16 slot for the frame grabber or GPU, and a CPU that does not throttle mid-inspection.
The weak link is usually the PC, not the camera
A machine vision system is only as reliable as its weakest component, and in deployed systems that is often the computing platform rather than the camera or the illumination.
The short version
Inline GigE inspection runs on a compact box with one LAN port per camera. AOI servers and deep-learning inspection need PCIe x16 for a GPU or frame grabber. Two different machines.
Where vision systems actually fail
Undersized RAM overflows the image buffer at high frame rates. Shared LAN ports drop frames mid-acquisition. A throttling CPU makes inspection latency drift. Rarely the camera.
What machine vision actually demands
Camera count, image size, acquisition rate and algorithm type decide the platform long before the brand does.
Dedicated camera bandwidth
GigE Vision streams 125 MB/s per port continuously. Sharing cameras on one port congests the link and drops frames. Each camera needs its own Gigabit channel.
PCIe expansion for GPU or frame grabbers
Deep learning inspection, 3D point clouds, CoaXPress and Camera Link all need a PCIe x16 slot that embedded PCs do not have.
Sustained CPU performance
Inspection runs on every part, every cycle. A CPU that thermal-throttles introduces variable latency and missed defects at the threshold.
Memory for image buffers
Four 5MP cameras at 30fps produce over 1.8 GB of raw image data per second. Too little RAM or slow bandwidth and the buffer overflows.
Two platforms for two very different jobs
Pick by what the system asks of the hardware, not by which one is newer.
EPC-309E
- 4× independent Intel Gigabit LAN, one camera per port
- 10th Gen Core i3 / i5 / i7
- BlockCore modular I/O: trigger DIO, serial for PLC
- Compact, fanless, cabinet-mount
- 9–36V DC with SafeCore power protection
IPC-627
- 1× PCIe x16, 1× PCIe x4, 2× PCI — 4 slots total
- Micro ATX industrial motherboards
- NVIDIA GPU, multi-port GigE frame grabbers, motion cards
- Up to 32GB DDR4, 8× RS-232
- Carbon steel chassis, 10G shock, 0–60°C
One is a compact box that fits inside the machine cabinet and takes four cameras directly. The other is a rack chassis that exists to hold cards: GPU, frame grabber, motion control.
Two deployments, two shapes of machine
The inline station and the AOI server are not the same problem in a different size.
Four cameras, no switch
Inline stations sit inside the machine cabinet: space constrained, heat constrained, and the cameras are close to the cabinet.
- Each camera gets its own 1 Gbps channel
- No managed switch between camera and processor
- BlockCore adds trigger DIO and PLC serial
- Solder joint, presence, label, surface and dimensional checks
Cards matter more than ports
AOI systems, 3D vision workstations and centralised multi-station servers need expansion, not just network ports.
- PCIe x16 for NVIDIA GPU inference
- Multi-port GigE or CoaXPress / Camera Link grabbers
- Motion cards for vision-guided robotics
- 0–60°C with 10G shock tolerance
EPC-309E or IPC-627
If the inspection runs on the CPU and the cameras plug straight in, take the compact box. If the system needs a GPU or a grabber card, take the rack chassis.
| Criteria | EPC-309E | IPC-627 |
|---|---|---|
| Form factor | Compact embedded box, cabinet mount | Wall-mount rack chassis, Micro ATX |
| Camera connection | 4× independent Intel Gigabit LAN | PCIe frame grabber cards, multi-port |
| Expansion | BlockCore modular I/O cards | 1× PCIe x16, 1× PCIe x4, 2× PCI |
| CPU | 10th Gen Core i3 / i5 / i7 | Micro ATX boards, 2nd–9th Gen Core |
| Memory | Per board configuration | Up to 32GB DDR4 |
| Serial | Via BlockCore | 8× RS-232 onboard |
| Environment | Compact, fanless, 9–36V DC | Carbon steel, 10G shock, 0–60°C |
| Best for | Inline GigE inspection inside the machine | AOI servers, GPU inference, grabber cards |
Frequently asked questions
Short answers. The long version is above.
How many GigE cameras can one industrial PC support?+
Do machine vision PCs need a dedicated GPU?+
What is the difference between the EPC-309E and the IPC-627?+
Why does camera network isolation matter?+
Specifying the vision platform
Camera count, image size and algorithm type, translated into hardware.