Industry 4.0 relies on intelligent automation in electronics manufacturing. Automation is not only becoming increasingly powerful but also ubiquitous, extending from the edge to the cloud, and is being used in sensors, robots and collaborative robots, programmable logic controllers (PLCs), and a wide variety of other devices. Semiconductor wafers, integrated circuits, passive components, packaging, and electronic systems all depend on intelligent automated production, encompassing numerous application areas such as consumer, green energy, automotive, medical, industrial, and military/aerospace. A unified Manufacturing Execution System (MES) monitors, controls, tracks, and records the entire manufacturing chain from raw materials to finished products in real time.
Cyber-physical automation systems in Industry 4.0 transcend traditional manufacturing activities, relying on various forms of machine learning (ML), from deep reinforcement learning in the cloud to tinyML at the edge, to achieve flexible production, continuous improvement, and consistently high quality. The increasing number of connectivity layers and the convergence of edge computing, the Industrial Internet of Things (IIoT), and cloud computing are exacerbating cybersecurity challenges. Blockchain has recently emerged as a promising avenue for comprehensive and secure supply chain management.
This article explores key automation trends in the electronics manufacturing sector, including increasing connectivity layers, growing demands for cybersecurity, dedicated implementations of deployed ML, and how traceability and MES support real-time production metrics and analytics. It then introduces some of the technologies needed to fully realize Industry 4.0's promise of high-quality, low-cost mass customization, including how DigKey offers a wide range of solutions to meet the needs of automation system designers. Finally, it discusses how blockchain can be used to deploy highly secure enterprise-grade supply chain management systems.
More and more connection layers
The Industrial Internet of Things (IIoT) in Industry 4.0 includes more wired and wireless network layers for sensor networks, autonomous mobile robots (AMRs), and other systems. For example, IO-Link was developed to provide simplified wired network connectivity to a large number of sensors, actuators, indicators, and other previously unconnected legacy edge devices, enabling them to interface with technologies such as Ethernet IP, Modbus TCP/IP, and...
Higher-level network connectivity such as PROFINET. With IO-Link, the inputs and outputs (IO) of these devices are captured and converted to the IO-Link protocol for serial connections as specified in the IEC 61131-9 standard via a single 4-wire or 5-wire unshielded cable as defined in the IEC 60974-5-2 standard (Figure 1). In addition to providing a new network layer for more granular information about factory processes, IO-Link also supports rapid deployment and remote configuration, monitoring, and diagnostics of connected devices to support the production line and process changes required for mass customization in Industry 4.0 factories.
A wide variety of wireless IIoT devices, from sensors to robots, also contribute to the creation of increasingly complex network layers. Various wireless protocols, including Wi-Fi, 5G, and LTE, are already used in modern factories. For example, AMRs (Autonomous Mobile Robots) use a combination of onboard sensors and Wi-Fi connectivity to understand their surroundings, identify potential obstacles, and move safely and efficiently from one location to another. Collaborative robots (cobots) are used to work with humans to improve efficiency. These robots typically require wireless connectivity. In some cases, AMRs allow collaborative robots to switch between different tasks as needed.
More and more cyber dangers
The increasing number of layers in industrial networks, coupled with the explosive growth in the number of connected devices, has led to a proliferation of security threat vectors and a surge in network risks. Currently, several industrial and IoT security standards and methodologies have been established, including IEC 62443 and the Security Evaluation Standard for Internet of Things Platforms (SESIP).
IEC 62443 is a series of standards developed by Committee 99 of the International Institute of Automation (ISA) and approved by the IEC. The IEC 62443 standard is an over 800-page series of standards for Industrial Automation and Control Systems (IACS), comprising four main parts and fourteen subparts (Figure 3). The key parts defining product development and component safety requirements are:
IEC
62443-4-1: Product Safety Development Lifecycle Requirements – Define the safety product development lifecycle, including initial requirements definition, safety design and implementation, verification and approval, defect and patch management, and end of life.
IEC 62443-4-2: Safety of industrial automation and control systems: Technical safety requirements for IACS components – Specifying safety capabilities that enable components to mitigate threats at specific safety levels.
The SESIP standard, published by GlobalPlatform, defines a common framework for assessing the security of connected products and addresses the unique challenges of compliance, security, privacy, and scalability specific to the Internet of Things (IoT). SESIP provides clear definitions of security functions for components and platforms in the form of Security Functional Requirements (SFRs). The standard also specifies strength metrics, measuring robustness against attacks in the form of SESIP “levels,” ranging from Level 1 to Level 5, where Level 1 is self-certification and Level 5 corresponds to extensive testing and third-party certification.
Machine learning from the cloud to the edge
ML is a key driver of intelligent automation, enabling continuous process improvement and high-quality products. The use of neural networks is a mature machine learning technique in Industry 4.0. This is the beginning of complementing it with deep reinforcement learning in the cloud. Deep reinforcement learning adds a goal-oriented algorithmic framework to the core of neural networks. Initially limited to repeatable environments such as playing games, reinforcement learning today can operate in more ambiguous environments in the real world. In the future, implementing advanced reinforcement learning may lead to general artificial intelligence.
ML is not just in the cloud, but is penetrating into factory floors and the edge. More and more expansion slots of industrial PCs and programmable controllers in factory floors are being fitted with ML and AI accelerator cards to enable intelligent process control.
TinyML is optimized for deployment in low-power applications. Its use in sensor applications is growing rapidly. For example, IIoT sensor analytics in battery- or energy-harvested edge devices is a micro-ML application. Arduino provides a micro-ML kit using the Arduino Nano 33 BLE Sense board, which features an MCU and various sensors to monitor motion, acceleration, rotation, sound, gestures, proximity, color, light intensity, and movement (Figure 4). It also includes an OV7675 camera module and an Arduino shield. The onboard MCU can implement deep neural networks based on the TensorFlow Lite open-source deep learning framework for on-device inference.
Real-time metrics and analysis
Real-time metrics and analytics are crucial aspects of intelligent automation. Traceability 4.0 combines product visibility, supply chain visibility, and production line visibility from previous generations of traceability, providing a complete history covering all aspects of the product. Furthermore, it includes all machine and process parameters to support optimization of the overall equipment efficiency (OEE) metric for the manufacturing process.
Traceability is crucial across numerous industries, from medical device manufacturing to automotive and aerospace. In the case of medical devices, extensive tracking and traceability are required by regulatory mandates. Automotive and aerospace systems may have tens of thousands of components that need to be traced. Beyond just part history, traceability includes tracking the geometry and tolerances (GD&T) of individual parts. GD&T enables precision manufacturing and allows for assembly based on the precise GD&T values of parts, supporting high-precision assembly in industries such as aerospace and automotive manufacturing.
Traceability can improve the accuracy and efficiency of product recalls. Traceability enables manufacturers to identify affected products and suppliers, as well as suppliers of any defective parts.
Traceability can accelerate the implementation of corrective and preventative actions. Similar to product recalls, knowing complete product origin information enables manufacturers to effectively and purposefully schedule field service and maintenance.
Traceability and MES
A unified implementation of MES with traceability can generate a searchable database containing all information related to an individual product, covering planned designs and final results. For example, before production begins, traceability is used to track the arrival of individual parts or materials, including incoming quality test data, the location of the supplying plant, and so on. MES verifies this information against the planned design and inputs it into associated work and work-in-process databases.
The traceability data provided by combining IIoT with MES enables large-scale product customization in Industry 4.0 environments. MES ensures that the right materials, processes, and other resources are in the right places, thereby guaranteeing the lowest production costs and the highest product quality. Simultaneously, MES can be combined with traceability to demonstrate compliance with government regulations, allowing auditors or other personnel to access the data at any time as needed.
Blockchain
Blockchain is a decentralized or distributed digital ledger system used to record transactions between multiple parties in a verifiable and tamper-proof manner. Any transaction where trust is a critical factor, such as supply chain management, presents a potential application for blockchain. In supply chains with many participants, blockchain can improve transaction efficiency and make transactions verifiable and tamper-proof. The advantages of using blockchain in supply chain activities are listed below:
Replacing manual processes. Paper-based manual processes relying on signatures or other forms of physical verification have the potential to be improved using blockchain. Its limitation lies in the need for a limited and easily identifiable range of participants in the ledger. A courier company with a constantly changing database of unfamiliar customers might not be a good choice for blockchain. However, a manufacturing business with a limited, slowly changing, and trustworthy group of suppliers would be a good blockchain option.
Enhancing traceability. Blockchain can provide a valuable tool for improving supply chain transparency and meeting growing regulatory and consumer information requirements. For example, blockchain can support the Drug Supply Chain and Safety Act and the U.S. Food and Drug Administration's unique device identifier authorization. In the automotive and other industries, where suppliers across the entire supply chain can participate in recalls, blockchain can provide a valuable tool for implementing traceability guidelines issued by the Automotive Industry Action Group.
Summarize
Intelligent automation, the foundation of Industry 4.0, relies on the implementation of numerous technologies, including increasingly sophisticated wired and wireless network layers, which in turn exposes it to increasingly complex cybersecurity threats. Furthermore, machine learning is expanding from the edge to the cloud to support real-time metrics and analytics, including traceability and a unified MES (Manufacturing Execution System). Finally, blockchain technology is being introduced to support tamper-proof, verifiable databases.
