Feature: Industrial
A smart, fully-automated factory will involve systems such as robots and cobots, automated guided vehicles (AGVs), conveyor segments and manipulators to move components and subsystems around the factory
is a manufacturing environment where products can be customised down to the unit level based on each customer’s needs, and one that can quickly switch schedules if a key source material is in short supply or if the customer-ordering systems indicate a change in ordering behaviour.
SBCs at base Manufacturers can leverage existing investments in machine tools and production automation to deliver significant benefits. Many of the machine tools already in use can be adapted for the Industry 4.0 environment, as long as they are augmented with additional levels of communication and intelligence, done economically and effectively with SBCs. Indeed, there may be no need to replace the programmable logic controllers (PLCs) that provide instructions for each machine. Many fieldbus protocols have been
adapted to work over industrial Ethernet to ensure that commands can be relayed to and from a nearby compute module. One possibility for integrating this functionality into both existing and new machine tool panels lies in the Kunbus range of DIN-rail-compatible Revolution Pi modules, an open-source industrial PC based on Raspberry Pi. Tese combine the compute power of an Arm Cortex-A processor with Ethernet connectivity, plus expansion for sensor feedback through a range of I/O modules and fieldbus interfaces. Industrial Shields, a manufacturer
of industrial automation devices, uses the Raspberry Pi platform to provide another option for implementing smarter, more-flexible PLCs, by coordinating the movements of subsystems and materials
around the factory. A USB connection provides the interface to a barcode reader or RFID scanner that picks up the tag on an incoming pallet or product carrier. An HDMI-connected display provides confirmation to an operator assigned to supervise the operations. When the package is confirmed, the Raspberry-Pi- based PLC uses industrial networking and I/O connections to activate motors to move the package through a series of conveyors to its destination. Alternatively, it may communicate a route to an AGV for product pick-up and delivery.
Future-proofed A key advantage of using hardware such as the Raspberry Pi is its future upgradability. Many industrial-control solutions already use Computer Module 4, which provides greater processing power, since it is based on a quad-core Arm Cortex-A72 processor. Tis module supports intensive machine-learning and vision-processing applications. In addition, as Computer Module 4 runs Linux, the many tools and development environments on that platform (such as Tensorflow, PyTorch and OpenCV) provide easy access to highly- sophisticated techniques for analysing components and subsystems for their operation and control. Te supervisory systems can also
harness the processing power of the Intel ecosystem. Intel’s NUC family includes models that scale up in cost-effective performance to processors such as the i7-8665U, a quad-core device running at several gigahertz. NUC-based computers provide a high degree of local intelligence, reacting to
alerts generated by PLCs and other SBCs on the shopfloor, and sharing graphical updates with operators to identify and remedy problems quickly.
Equipment monitoring and information analysis At the other end of the scale, flexible processes need responsive, easily- programmable low-level control. Tis can be delivered, for example, by the Arduino platform, a combination of microcontroller- based hardware and optimised soſtware- development environment that supports rapid prototyping and algorithm evaluation. Te Arduino Pro Portenta is a low-cost
yet powerful option through the dual-core pairing of the Arm Cortex-M7F and M4F processor cores, both of which support integer and floating-point arithmetic. Tis makes the Arduino Pro Portenta suitable for handling mathematical models and closed- loop control algorithms. For greater performance in a compact
package, the DFRobot LattePanda couples an Arduino-compatible microcontroller with an Intel quad-core 1.8GHz processor. Here, the SBC can perform tasks such as AI-assisted equipment monitoring, image processing and computer numerical control, making it highly suited to building customised machine tools. BeagleBone AI provides a further option
for adding support for machine learning, smart sensor and image processing in real time. By using various sensor modalities, it’s a route to non-destructive testing in real time, coupled with equipment monitoring. Te onboard dual-core Arm Cortex-A15 running at 1.5GHz works with a pair of Texas Instruments (TI) C66 digital signal processors and four embedded vision engines which support TI’s deep-learning soſtware. All the hardware and development
environments currently available offer a path to Industry 4.0 for systems integrators, machine builders and factory owners. Te migration process is becoming easier to navigate, supported by a rich and growing selection of SBCs, which enable existing tools to be seamlessly upgraded and incorporated into a network for a far smarter factory.
www.electronicsworld.co.uk September 2023 23
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