How automation, machine learning, and Blockchain are driving the future of electronics manufacturing
process improvements and high- quality products. The use of neural networks is a well-established ML technique in Industry 4.0. It’s beginning to be supplemented with deep reinforcement learning in the Cloud. Deep reinforcement learning adds a framework of goal-oriented algorithms to a neural network core. Initially, reinforcement learning was confined to repeatable environments like playing games; today, algorithms can operate in more ambiguous environments in the real world. In the future, advanced reinforcement learning implementations may achieve artificial general intelligence. ML is not just in the Cloud; it’s reaching onto the factory floor to the Edge. The expansion slots in industrial PCs and programmable controllers on the factory floor increasingly host ML and AI accelerator cards for intelligent process control. Tiny machine learning (tinyML) is optimized for deployment in low-power applications. The use of tinyML in sensor applications
Figure 1. IO-Link can be used to connect sensors and other devices using diverse interfaces to Ethernet, PROFINET, or Modbus networks. Image source: Banner Engineering
Figure 3. IEC 62443 is a comprehensive set of IACS security standards. Image source: IEC
IEC. IEC 62443 is an 800-plus-page series of standards for Industrial Automation and Control Systems (IACS) in 14 subsections and four tiers (Figure 3). Key sections that define the product development and security requirements for components are: ■ IEC 62443-4-1: Product Security Development Lifecycle Requirements – defines a secure product development lifecycle including initial requirements definition, secure design and implementation, verification and validation, defect and patch management, and end-of-life. ■ IEC 62443-4-2: Security for Industrial Automation and Control Systems: Technical Security Requirements for IACS Components – specifies security capabilities that enable a component to mitigate threats for a given security level.
GlobalPlatform and defines a common structure for evaluating the security of connected products and addresses IoT- specific compliance, security, privacy, and scalability challenges. SESIP provides clear definitions of security functionality on components and platforms in the form of Security Functional Requirements (SFRs). It also provides strength metrics that measure robustness against attacks in the form of SESIP ‘levels’ from 1 to 5, with 1 being self-certification and 5 corresponding to extensive testing and third-party certification.
converted to the IO-Link protocol for serial connectivity defined in IEC 61131-9 with a single 4- or 5-wire unshielded cable defined in IEC 60974-5-2 (Figure 1). In addition to providing a new networking layer to capture more granular information about factory processes, IO-Link supports rapid deployment and remote configuration, monitoring, and diagnostics of connected devices to support line and process changes needed for mass customization in Industry 4.0 factories. Wireless IIoT devices, from sensors to robots, also contribute to the growing networking layers. Various wireless protocols, including Wi-Fi, 5G, LTE, and others, are used in modern factories. For example, AMRs use a combination of onboard sensors and Wi-Fi connectivity to understand their environment, identify possible
obstacles and move safely and efficiently from place to place. Colaborative robots (cobots) are designed to work with people to improve operational efficiency and often require wireless connectivity. In some cases, AMRs move cobots from task to task as needed (Figure 2).
IEC 62443 is a series of standards developed by the International Society of Automation (ISA) 99 committee and approved by the
Increasing cyber dangers
The increasing layers in industrial networks, combined with the explosion in the number of connected devices, are resulting in a growing number of security threat vectors and increasing cyber dangers. Several industrial and IoT-specific security standards and methodologies have been developed, including International Electrotechnical Commission (IEC) 62443 and the Security Evaluation Standard for IoT Platform (SESIP).
ML from the Cloud to the Edge ML is a key enabler of intelligent automation, supporting continuous
Figure 2. An AMR (bottom) can navigate from place to place using a combination of onboard sensors and wireless connectivity and pick up and move a cubit (top) to a new workstation. Image source: Omron
Figure 4. Arduino’s Tiny Machine Learning Kit is designed for developing IIoT sensor applications. Image source: Digi-Key
SESIP is published by the
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