Companies can build their own shop-floor monitoring software without paying for expensive third-party subscriptions.
Unlocking the Power of FANUC Focas 2: The Ultimate Guide to CNC Data Connectivity
FOCAS2 enables read and write access to servo axis and spindle data including absolute coordinates, relative coordinates, machine tool coordinates, remaining tool movement, and actual feed speed. This real-time positional data is essential for process monitoring and quality control applications.
Focas 2 libraries are designed to be compatible across various FANUC controller series (e.g., 30i, 31i, 32i). Why Choose Focas 2 over Other Solutions?
It is a robust, mature, and proven technology designed specifically for the industrial environment.
FOCAS 2 is a proprietary library of C-language application programming interfaces (APIs) developed by FANUC. It enables external software applications to communicate directly with FANUC CNC controllers over an Ethernet or PCMCIA interface. By serving as a digital bridge, FOCAS 2 allows factories to monitor machine status, track production metrics, extract diagnostic data, and feed information into wider Manufacturing Execution Systems (MES) and Industrial Internet of Things (IIoT) platforms. Core Architecture and How It Works
Absolute, relative, machine, and distance-to-go coordinates. Servo motor load currents and feed rates. Spindle speed, temperature, and override percentages. 2. Program and Operational Status Active NC program numbers and current block lines. Controller mode (Automatic, Manual, Edit, MDI). Execution status (Running, Hold, Stopped, Alarms). 3. Tooling and Offset Information Tool offset values (geometry and wear). Tool life management data (usage count and remaining time). 4. Machine Diagnostics and Alarms Active alarm codes, messages, and historical alarm logs. Diagnostic data for troubleshooting hardware failures. 5. Custom Macro Variables
Open-source wrappers (such as pyfocas ) allow data scientists and automation engineers to quickly script data collection pipelines and integrate CNC data into machine learning models.
