North State Software - UML State Machine Code Generators

Learn about:

..:: UML State Machine Framework

Success Stories:

..:: Robotics
..:: Booklet Maker
..:: Microscopy


..:: 8/1/2011
NSF V2.0 Release

..:: 2/24/2013
NSF Lower Cost

..:: 8/13/2014
NSF Open Source

State Machine based control system for booklet maker.


An established print and mail handling company was looking to expand its product line into the print-on-demand document marketplace. The new products required a high degree of modularity to support various material form factors, as well as printing and feeding platforms. In addition, the products would be creating customer specific documents of variable size and content, requiring high-speed reading of coded information on each sheet. These features required a rapidly configurable control system capable of handling and tracking material from multiple, simultaneous input channels.

Solution and Results

The team created a modular controls framework using UML state machines to implement the core behaviors for each of the various reading, feeding, collating, merging, stitching, and delivery devices. The framework was designed such that new modules and system configurations could be created by simply defining them in configuration files, rather than having to write new code.

The controls framework handled tracking the material, along with its data, as it was processed through the machine, allowing for variable collations based on unique data for each sheet. The tracking system also allowed for integrity checks to be performed based on the encoded information. Such checks included account number consistency, as well as proper sequence and number of pages. In the event of integrity or other errors, the system was able to auto-recover by re-synchronizing the collations.

The high throughput requirements were met by the control system's ability to arbitrate material delivery between modules. The arbitration system optimized time between deliveries, and allowed for addition time between sheets and packets at the end of collations. Additional features supporting high throughput included an adaptive learning algorithm to optimize feeder speeds and sensing material arrival and departure times.

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