Flexxbotics Uploads Open-Source Connector Drivers to GitHub
Flexxbotics, a supplier of workcell digitalization technologies for robot-driven manufacturing, has placed its open-source project Flexxbotics Transformers on GitHub to provide industrial-grade, production-ready connector drivers for factory equipment and automation systems.
The Transformers were released under the Apache 2.0 license and are designed using software-defined automation to promote open interoperability across manufacturing environments through tools, machines and automation. They provide a standardized abstraction layer for connecting machines, programmable logic controllers, robots, inspection and test equipment, sensors, safety systems and other factory assets using open industrial protocols and vendor-specific proprietary interfaces.
Using parallelized data pipelines with multi-threaded and asynchronous controls to aggregate factory data for manufacturing autonomy and industrial AI data ingestion, the Transformers provide interoperability across more than 1,000 models of factory equipment for point-to-point integrations, according to Flexxbotics. They can ingest complex data streams that can contribute to AI training data sets and interoperate with each other to enable many-to-many connectivity across all connected devices.
The GitHub open-source repository includes production-ready transformers, templates and examples for developing automated integrations, including:
- Equipment Transformers that use existing protocols and interfaces including OPC UA, MQTT, Siemens S7comm, Beckhoff ADS TwinCAT and Fanuc FOCAS2.
- Workcell Transformer that composes multiple transformers to orchestrate automation logic and execute multi-threaded and asynchronous controls.
- Transformer Template for creating new device connector drivers.
- Automation scripts that are callable from the controls configurator and HMI to run in real time and interact directly with the Transformers to execute tasks.
Flexxbotics highlighted several practical uses in factory settings, such as:
- APC (advanced process control)/R2R (run-to-run) systems, tool orchestration, automated test equipment test automation, automated production lines, advanced robotic machine tending and regulated process automation.
- Process characterization, correlation and root cause analysis.
- Autonomous process control for adjustments and corrections of variables, parameters and offsets.
- Orchestration of multi-operation automated processes with autonomous changeovers and digital thread compliance traceability.
- Collection and contextualization of incompatible multi-source data from factory assets for physical and industrial AI training.
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