MachineMetrics Blog

START DRIVING DECISIONS WITH MACHINE DATA.

Ready to empower your shop floor?

Learn More
Categories:
    Graham Immerman
    Graham Immerman MachineMetrics / January 22, 2021 Aerospace and Defense / January 22, 2021

    Industry 4.0 Implementation Challenges in Aerospace and Defense Manufacturing

    The future of manufacturing is reliant on an ability to develop cyber-physical systems within the shop floor which drive industrial automation, interoperability, and other optimization applications. This outlook is one where data becomes gold as the capture, processing, and transfer of data will drive every Industry 4.0 business model. Equipment manufacturers in the aerospace and defense industry rely on Industry 4.0 and digital transformation technologies to improve productivity and create new revenue generation models but it all starts with data collection from the factory floor.

    Industry 4.0 is predicted to solve many of the challenges currently affecting aerospace and defense OEMs, such as difficulties with inventory supply, the need to remain innovative, and developing energy-efficient equipment. To reap these benefits, OEMs must deploy data capturing frameworks and strategies which tell the story about the operations and interrelated processes on the shop floor. But this is still a very recent change to traditional processes. Until the Industrial Internet of Things (IIoT) came into the picture around a decade ago, capturing data from every corner of the shop floor and from manufacturing processes was next to impossible.

    Solving the Challenges of Extracting Data in Aerospace and Defense OEM Facilities

    Advancements in IIoT solutions and Edge computing have enabled the harnessing of data from the deepest part of the shop floor and transactional processes. IIoT solutions have also enabled the ability to aggregate and analyze every piece of data captured from the aerospace and defense equipment manufacturing processes. Using IoT hardware such as sensors, radio frequency identification devices (RFID), and smart devices, capturing diverse forms of data is a reality. This makes it possible to implement Industry 4.0 business models such as predictive maintenance and data-driven production optimization activities. Today, the aforementioned IIoT hardware can be connected to legacy equipment which solves the dilemma of extracting data from assets with no wireless or digital I/O capabilities.

    It is important to note that limitations with extracting data also exist in modern equipment. Although newer machines are capable of transferring data through a wireless network or wired cable, the captured data generally revolves around throughput, machine utilization, and working duration. Data such as equipment vibration and operational temperature are usually (and wrongly) overlooked. This shouldn’t be the case as these data sets play important roles when developing predictive maintenance schedules and monitoring equipment performance.

    IIoT hardware provides OEMs with the tools to complement the data capturing efforts of modern equipment. With a vibration sensor, the frequency of an equipment vibration during use can be measured. The captured data can then be used to determine the effect of vibrations on tool heads and other moving components within the equipment.

    Solving the Challenges of Large-scale Industry 4.0 Implementations Using an IIoT Platform

    Edge computing provides the decentralization required to deliver near real-time automation which is a hallmark of Industry 4.0, which should be good news when applied to the aerospace and defense sector. Although the ability to process data using the Edge is a foundational part of every Industry 4.0 implementation, facility-wide operations require more computing resources than the average Edge device can provide.

    Enter IIoT platforms.

    IIoT platforms provide the scalable computing resources required for factory-wide Industry 4.0 Implementation. Take for example the need to implement a data-driven production optimization strategy to ensure every process within a master production plan is optimized. For the above scenario, inventory data, material handling data, machine utilization data, and scheduling data must be analyzed to develop an optimized master production plan.

    Edge computing cannot provide the resources needed to analyze and optimize the interrelated processes within a production plan, so therefore the capabilities of an IoT platform are required. Note that not every IoT platform can handle the challenges facing the aerospace and defense OEM sector. An industry-specific IIoT platform capable of providing the supportive analytical tools, security, and scalability to optimize Industry 4.0 models within aerospace and defense OEM facilities is therefore required for a successful implementation.

    START DRIVING DECISIONS WITH MACHINE DATA.

    Ready to empower your shop floor?

    Learn More

    Comments

    Leave a comment

    Subscribe to our mailing list