How automation, machine learning, and Blockchain are driving the future of electronics manufacturing
changing group of trusted suppliers is a good candidate.
The intelligent automation that’s the foundation of Industry 4.0 relies on numerous technologies for its implementation, including a growing number of network layers with wired and wireless connectivity that result in increasingly complex cyber security threats.
industries, from medical device manufacturing to automotive and aerospace. In the case of medical devices, regulatory requirements demand extensive tracking and traceability. Automobiles and aerospace systems can have tens of thousands of parts to track. It’s not just part history; traceability includes tracking individual part geometric dimensioning and tolerancing (GD&T). GD&T enables precision manufacturing and the installation of parts based on their exact GD&T values, supporting high-precision assemblies for industries like aerospace and automotive manufacturing. Traceability can improve the accuracy and efficiency of implementing product recalls. It enables the manufacturer to identify all the affected products and the supplier or suppliers of any defective components. Corrective and preventative actions can be accelerated through the use of traceability. Like product recalls, knowing the complete provenance of products enables manufacturers to efficiently target and schedule service and maintenance activities for products in the field. Figure 5. Traceability 4.0 is a comprehensive implementation that supports the diverse requirements of Industry 4.0 operations. Image source: Omron
Strengthening traceability. Blockchain can provide a good tool for improving supply chain transparency and meeting growing regulatory and consumer information requirements. For example, the Blockchain can support the Drug Supply Chain and Security Act and the unique device identifier mandate from the U.S. Food and Drug Administration. In the automotive and other industries, suppliers throughout the supply chain can be involved in implementation of recalls, and Blockchain can provide a good tool for implementing the Traceability Guideline published by the Automotive Industry Action Group. Summary The intelligent automation that’s the foundation of Industry 4.0 relies on numerous technologies for its implementation, including a growing number of network layers with wired and wireless connectivity that result in increasingly complex cyber security threats. In addition, machine learning is being implemented from the edge to the Cloud to support real-time metrics and analytics, including traceability and unified MES. Finally, Blockchain technology is being introduced to support tamperproof and verifiable databases.
incorporating traceability can produce a searchable database of all the information related to individual products, including as-planned designs and as-built results. For example, traceability is used to track individual components and materials as they arrive, including inbound quality testing data, location of the supplying factory, and so on, before production starts. MES verifies that information based on the planned design and feeds into kitting operations and work in process databases. Traceability data supplied by the IIoT combined with MES supports the mass customization of products in Industry 4.0. MES enables the right materials, processes, and other resources to be at the right place to ensure the lowest production cost and highest quality result. Also, MES and traceability can combine and demonstrate compliance with government regulations and make the data readily accessible to auditors or others as required.
Blockchain
A Blockchain is a decentralized, or distributed, digital ledger system for recording transactions between multiple parties in a tamperproof and verifiable manner. Any transactions where trust is important, like supply chain management, are potential uses for blockchain. In a supply chain with many participants, Blockchain can improve transaction efficiency and make transactions verifiable and tamperproof. Two examples of the benefits of using Blockchain in supply chain activities include: Replacement of manual processes. Manual paper-based processes that rely on signatures or other forms of physical verification can potentially be improved using Blockchain. The limitation is that the universe of participants in the ledger must be finite and easily identifiable. A delivery company with a constantly changing database of unfamiliar customers may not be a good candidate for Blockchain. A manufacturing operation with a finite and slowly
is growing rapidly. One example of a tinyML application is IIoT sensor analytics in Edge devices powered by batteries or energy harvesting. Arduino offers a Tiny Machine Learning Kit that includes an Arduino Nano 33 BLE Sense board containing an MCU and a variety of sensors that can monitor movement, acceleration, rotation, sounds, gestures, proximity, color, light intensity, and movement (Figure 4). An OV7675 camera module and an Arduino shield are also included. 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 analytics
Real-time metrics and analytics are essential aspects of intelligent automation. Traceability 4.0 combines product visibility, supply chain visibility, and line-item visibility from previous generations of traceability and provides a complete history of all aspects of a product. In addition, it includes all machine and process parameters and supports overall equipment effectiveness (OEE) metrics that optimize manufacturing processes (Figure 5).
Traceability and MES
Traceability is vital in many
Unified MES implementations
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