kjd's comment makes me wonder how much intelligence is needed in each local node of a control network, vs the central controller, for different types of control networks? In machine vision networks this is highly variable, depending on a host of different factors. So smart cameras, the equivalent of a smart controller here, aren't always the best solution, for example.
Industrial environmental control and HVAC systems have long operated as distributed networked controller platforms tied together to a central energy management system.
Each networked ‘smart’ controller is responsible for driving a number of analog and digital actuators, and reading inputs from a number of different sensor types. Each networked ‘smart’ controller can also run advanced control routines for its local domain, while feeding shared info back into the network.
In that arena, it would seem to be an interesting challenge (both cost and efficiency –wise) to design your control network with each actuator being somewhat smart itself, but seemingly not contributing to the corporate intelligence.
Siemens and Georgia Institute of Technology are partnering to address limitations in the current additive manufacturing design-to-production chain in an applied research project as part of the federally backed America Makes program.
Most of the new 3D printers and 3D printing technologies in this crop are breaking some boundaries, whether it's build volume-per-dollar ratios, multimaterials printing techniques, or new materials types.
Independent science safety company Underwriters Laboratories is providing new guidance for manufacturers about how to follow the latest IEC standards for implementing safety features in programmable logic controllers.
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