Smart factories are facilities that digitize all aspects of manufacturing or production that allow digitization. Such operations continuously record data via connected equipment and systems — and then disseminate that data to allow machines to run self-optimizing routines. Such programs might help the facility time production of a given end product; proactively prevent mechanical issues; and streamline interconnected manufacturing tasks. Comprehensive approaches to building smart factories leverage cloud tools, AI, IIoT, and big data analytics for monitoring supply-chain forecasts and triggering (increasingly adaptable) production lines to respond.
Networks underpinning smart-factory functions
Let’s now get into the specifics of smart-factory connectivity. Industrial protocols supporting smart-factory functions usually require certification of physical components; read more about this at the Motion Control Tips article: Trends in Ethernet, PoE, IO-Link, HIPERFACE, and single-cable solutions. CAT5e and CAT6 as well as Power over Ethernet (PoE) connectivity is increasingly common in automated machines and robotics. Elsewhere, flexible CAT5e and CAT6 cables support CC-Link Industrial Ethernet (IE) networks and come in cable-carrier-bundled assemblies sporting UL certification for North American markets.
Consider industrial controllers supporting the CC-Link IE Field industrial network and allowing data exchanges to 1 msec for realtime equipment control. Some such controllers also leverage the network for remote monitoring, edge computing, data computing, and the integration of hardware and software. It’s typical that these controllers have a form Windows 10 IoT installed, though the operating system VxWorks as well as open-platform Edgecross can also be used to process and distribute data. Some such industrial computers even include touchscreens to double as human-machine interfaces (HMIs).
The main benefit of HIPERFACE DSL is how it allows the routing of motor power and position feedback through one cable — in turn trimming complexity and cost. Plus smart HIPERFACE DSL encoders include internal memory to store motor information … so upon initial connection, a servodrive can query this information to help automate motor commissioning.
In the same way, single-cable solutions based on Ethernet or even digital subscriber line (DSL) cables improve machinery incorporating linear actuators — often offering compatibility with amplifiers from various manufacturers for quick and seamless controller to actuator integration.
Single-cable IO-Link is also seeing increased adoption for industrial connectivity. Some intelligent-motor suppliers have begun to integrate IO-Link primaries into the core offerings for supporting connectable sensors for decentralized automation concepts. Of course, motors that can communicate via industrial Ethernet or CAN bus don’t need to connect to IO-Link networks as secondaries.
IO-Link can also digitize legacy analog connectors on components to impart bidirectional communications and quicker commission times. No wonder some have adopted IO-Link connectivity on the controls side for multi-protocol support and connection with serial interfaces.
Protocols and cloud connectivity serve smart-factory functions
Consider the various protocols and communications leveraged in IIoT connectivity — for example, SCADA, MES, and enterprise resource planning (ERP) architectures. These are the most involved in IT/OT (operational technology) convergence — often involving enterprise-level tasks, gateways, and other connectivity to allow system configuration through standard web browsers … as well as operational adjustments and additional managerial actions.
To be clear, comprehensive SCADA installations (despite their complexity and considerable installation requirements) excel at big-data capture and processing; maintenance and use of historical data; and execution of analytics routines. However, smart-factory solutions allow faster setup of remote-access networks, edge-computing systems, and central or on-machine (HMI) control over pertinent machine settings and data.
Employed in many IIoT installations is Structured Query Language (SQL) — programming that allows synchronization of data and event logs to MySQL and MS SQL database servers. The benefit here is IT personnel access that’s more simply implemented than alternatives relying on controls. That’s true whether the systems employ basic controls such as Raspberry Pis or complex PC-based IoT database interfaces (which usually necessitate additional hardware and software).
Also seeing massive adoption for how they support multi-pronged IIoT design approaches (involving software, hardware, and connectivity) are infrastructure, platform, and software as a service (IaaS, PaaS, and SaaS respectively) or cloud services. These include Alibaba Cloud, Tencent Cloud, Google Cloud, IBM Cloud, and Oracle Cloud. However, in the U.S., today’s two leading public (offsite not company or machine network) cloud service providers for machine automation are:
- Amazon Web Services Inc. with AWS cloud software and services
- Microsoft with Azure IoT Edge cloud software and services
Such cloud services primarily support the use of databases (through products such as Amazon simple storage service or S3 buckets and Amazon DynamoDB managed database services), online and local applications, and on-demand computational power. Related to the latter are AWS Lambda services that allow Python, Node.js, Java, and C# programming to run on the service’s servers. HMIs let end users make the most of these IIoT functions.
Of course, cloud services serve other functions too. Part of what’s driving AWS and Azure adoption for IIoT is how more engineers have become comfortable with building out their own infrastructure on these platforms. After all, cloud-based data services free engineers from extra design work on underlying hardware and software — because the provider executes IT tasks. AWS and Azure also allow use of software that abstracts dataflows and communications — simplifying some design work with development environments having attractive GUIs to shield engineers from dealing with programming minutia.
Cloud services also facilitate advanced engineering with virtual machines that run operating systems and applications … over which design engineers maintain control. What’s more, cloud services can accommodate various communication services on protocols employing publish-subscribe principles — to be the master service for them all. That eliminates the need for time-consuming addressing during system setup.
All such features can facilitate advanced capabilities including machine learning for categorizing and distilling data … and making predictions to prompt machine and production adjustments.
A related trend is increased use of pre-curated cloud portals from suppliers. These portals (which give engineers a simple way get started with IIoT) are online services that connect with users’ controllers and touchscreen HMIs. Then engineers can customize HMI screens and dashboards with trends … and configure HMI email notifications using a rule engine managed from the cloud portal. The list of functions goes on. Some arrangements allow remote software updates on the components — and remote viewing of the components’ web visualizations.
Touchscreen HMIs and controllers certified for AWS GreenGrass Core essentially leverage AWS (including AWS Lambda and Things Graph) to let connected edge devices (such as sensors and actuators) locally act on data they generate — and use the cloud for data management, storage, and analytics. With AWS IoT Greengrass, connected devices can also run Docker containers of the containerization service from Docker Inc.
Recall that in the context of industrial programming, a container is a piece of executable software that holds the codes, system tools, runtimes, libraries, and settings needed for the standalone running of an application. In many machine designs, containers are designed to communicate and synchronize data to other systems or execute various predictions — even when disconnected from the internet. Advantages of building applications in containers include:
- Easy deployment onto devices
- Portability of software to allow its use across different platforms
- Improved security by providing a sandbox for engineers’ applications
Some HMIs and DIN-rail-mountable controllers accept installation of Docker … and in fact, some suppliers regularly release prebuilt containers to extend services onto these products.
Anywhere an HMI connects to the cloud, it’s likely working in some IIoT capacity to inform corporate analytics and continual operations improvements. That’s true of automated installations involving one to hundreds of machines. Protocols supporting IIoT functions including various forms of data communications and HMI connectivity with edge devices include:
- Open Platform Communication Unified Architecture or OPC UA
- Representational State Transfer or REST and its application programming interfaces (APIs)
- The advanced message queuing protocol or AMQP
- Message queuing telemetry transport or MQTT
MQTT — so core to many IoT connectivity structures — is a protocol supporting scalable communications between sensors and mobile devices. Any built-in device support for MQTT is useful because it’s applicable in Amazon AWS IoT services. In addition, MQTT (like AMQP) is lean and standardized … and MQTT can be implemented on gateway HMIs handling field-device data for onsite and cloud systems. HMIs offering the most MQTT support are meant to be connected to value-added services for provisioning data that’s been edge processed in third-party systems — and run off cloud services. Such HMIs can connect as a MQTT publisher (and send messages to a broker) or a subscriber (and request messages from a broker) or a broker (and manage data and connections with publishers or subscribers).
Interoperability standard OPC UA is also indispensable for leveraging the full promise of connected HMI technology. OPC UA includes publish-subscribe communications in its specification definitions, so can serve as an alternative to MQTT for data transport to the cloud. Those in motion control most value the standardized communication protocol of OPC UA complemented by time-sensitive networking (TSN) as a vendor-independent fieldbus for decentralized automation. OPC UA with TSN can even render additional PLCs unnecessary — as in machines employing integrated servomotors, for example. After all, more systems than ever now benefit from distributed architectures incorporating smart motors and other components capable of processing commands and executing tasks (motion and other) while communicating with other devices in realtime. In some cases, the latter can include HMIs serving as edge gateways to handle some of the axes’ process logic (as well as connections to ERP systems and the cloud).
Example of how HMIs use MySQL database connectivity
Employed in many IIoT installations is SQL mentioned earlier. This relational database management system is free, open source, and widely supported. It’s also secure — so safely integrated into controller HMIs and panel PCs. One SQL benefit is IT personnel access that’s more simply implemented than alternatives relying on controls (and usually necessitating additional hardware and software). That’s true of system controls as simple as Raspberry Pis or as complex as PACs with IoT database interfaces.
In fact, SQL also works with some controller HMIs that collect and display machine data for easier monitoring and analytics. For example, connecting such HMIs to a MySQL database allows data collection, organization, and storage in flexible and trusted databases for easy access and optimized business operations.
Some supplier design software helps engineers use MySQL via smart HMIs and put data in Excel spreadsheets (and tabular data in the files of other common software) to:
- Display information on the HMI screen
- Synchronize data and event logs to a remote MySQL server on the local network
- Manage that data on the server.
Then engineers can use MySQL and MS Excel to collect, analyze, and respond to the data for more informed decisions and optimized operations.
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Filed Under: Digital transformation (DX)