{"id":74489,"date":"2024-05-31T07:57:54","date_gmt":"2024-05-31T05:57:54","guid":{"rendered":"https:\/\/intellias.com\/?post_type=blog&p=74489"},"modified":"2024-07-12T13:37:27","modified_gmt":"2024-07-12T11:37:27","slug":"industrial-metaverse-the-next-stage-of-the-manufacturing-industry","status":"publish","type":"blog","link":"https:\/\/intellias.com\/next-stage-of-the-manufacturing-industry\/","title":{"rendered":"Industrial Metaverse: The Next Stage of the Manufacturing Industry"},"content":{"rendered":"
Industrial metaverse<\/em>. When leaders in the industrial sector hear this phrase, they picture semi-autonomous conveyor belts, employees designing product<\/a> mockups in virtual reality, and sustainable resource management.<\/p>\n Many industrial automation projects face cultural resistance. Employees are disgruntled with cameras invading their privacy. Labor union leaders lament robots taking over humans\u2019 jobs, while stakeholders view proposed innovation skeptically due to unclear return on investment (ROI). In reality, all sides are experiencing tunnel vision.<\/p>\n We\u2019re still in the Industry 4.0 era \u2014 a period of rapid adoption of emerging technologies, from cloud and edge computing<\/a> to machine learning (ML) and artificial intelligence (AI)<\/a>. In recent years, manufacturing leaders have built a solid technological foundation to support their operations. Now, they\u2019re eager to scale the available technology stack to support new use cases.<\/p>\n <\/p>\n Source: Deloitte \u2014 Exploring the industrial metaverse<\/a><\/em><\/p>\n The metaverse isn\u2019t a singular technology but a collection of building blocks organizations can use to replicate physical environments.<\/p>\n At Intellias, the industrial metaverse combines industrial digital twins with AR\/VR capabilities powered by advanced analytics and artificial intelligence<\/strong>.<\/p>\n IntelliTwin Platform<\/p>\n Let\u2019s look at the role and value of each technology.<\/p>\n A digital twin is a virtual replica of equipment, an ecosystem, or a facility that emulates its physical characteristics using real-time data. Digital twins<\/a> synchronize the physical and digital worlds, offering a 360-degree view of an asset\u2019s condition, performance, and other characteristics. They can include data points like motion, temperature, vibrations, or energy consumption captured with modern connectivity technology.<\/p>\n Thanks to these characteristics, digital twins can support various use cases<\/a> across sectors: predictive equipment maintenance, optimized energy management, product process simulation, surgical training, precision farming, and more.<\/p>\n In the manufacturing sector, 71% of leaders said their enterprises already used digital twin technology in 2023 according to an Altair survey<\/a>. Among adopters, 94% said that digital twins improved new product development<\/a>, while 62% cited maintenance and warranty cost optimization.<\/p>\n <\/p>\n Source: Altair \u2014 2023 Global Digital Twin Survey Report<\/a><\/em><\/p>\n Intel has been progressively digitizing its factories since the early 2000s. Its facilities can be operated remotely via Remote Operation Centers (ROCs), allowing engineers to monitor and control operations from any location via a software platform.<\/p>\n The company also relies on digital twins<\/a> to model and analyze complex factory operations, evaluate the risks of process changes, and train staff. For example, using a digital twin of the Automated Material Handling System (AMHS), Intel engineers can remotely monitor performance, identify problems early, and accurately predict production metrics. With digital twins, Intel increased productivity and reduced unit throughput time while maintaining product quality despite complex manufacturing procedures.<\/p>\n Siemens<\/strong>, a leader in developing and implementing digital twin solutions, recently launched<\/a> a new digital twin module to create 3D simulations of CFD thermal environments. Electronics manufacturers face heat dissipation challenges due to miniaturization and increasing processing demands. Complex IC package architectures like 2.5D, 3D IC, and chipset-based designs bring complex thermal management challenges. Siemens\u2019 thermal twin enables high-fidelity 3D thermal analysis and secure model sharing.<\/p>\n Today, most companies rely on descriptive or informative digital twins<\/strong>:<\/p>\n Predictive or autonomous digital twins<\/strong> are the next evolutionary step. Such systems can learn from data, make decisions, and act for users, with or without direct interaction. Predictive digital twins enable remote monitoring and preventive maintenance, allowing operators to identify and resolve problems before they become operational nuisances.<\/p>\n Predictive models also support advanced \u201cwhat-if\u201d planning, optimizing processes and workflows without wasting time, asset capacity, or source materials. Siemens\u2019 digital twin technology<\/a> allows electronics manufacturers to run digital what-if part selection analysis during product design, using its database of component intelligence for over 600 million manufacturer part numbers.<\/p>\n In future metaverse industries, we will see wider deployment of all three types of digital twin models, covering every element and workflow in the manufacturing process \u2014 from floor layout and equipment positioning to source materials and humans.<\/p>\n AR and VR extend digital twin capabilities by providing a new interface. Instead of analyzing data via dashboards or supervising a model via a screen, users can directly interact with modeled assets in an extended reality environment.<\/p>\n Manufacturers actively use AR in product design and prototyping. Holographic 3D models can overlay real geometries onto AR goggles to help workers evaluate different concepts or assembly processes.<\/p>\n BMW<\/strong>, for example, has tested AR-powered prototyping in one of its plants. The system uses data from BMW Group\u2019s product data management system to visualize vehicle models and components. Users can drag and drop CAD files from the database to AR goggles to reproduce them in 3D with detail. The AR app is controlled by hand gestures, allowing direct interaction from multiple collaborators without geographical restrictions. BMW says AR saves them up to a year<\/a> on vehicle module validation.<\/p>\n Employee training and remote assistance are other widespread use cases of AR\/VR in manufacturing<\/strong>. Studies show the high effectiveness of VR safety training<\/a> and better outcomes, as it helps learners connect visual, verbal, and motor knowledge.<\/p>\n Audi<\/strong> was an early adopter of VR<\/a> for employee training, creating courses for new hires at its manufacturing facilities. The company also used VR<\/a> to optimize special containers for sensitive parts to improve productivity. Airbus<\/strong> relies on a video platform with AR capabilities<\/a> to more effectively connect on-site technicians with subject matter experts for equipment repairs.<\/p>\n The latest mixed reality devices, like the industry-ready HoloLens 2 and Apple Vision Pro, offer high definition and low latency for advanced object modeling and real-time interactions. In the industrial metaverse, VR, AR, and digital twins can substitute costly physical simulations and tests without affecting product quality or safety. These applications also support global team collaboration in virtual workspaces for brainstorming, iteration, and concept validation.<\/p>\n Greater connectivity and digitalization means immense volumes of data.<\/p>\n Cloud computing makes it easy for manufacturers to capture, store, and retrieve large amounts of data, while advanced data analytics techniques help transform it into insights for decision-making.<\/p>\n Over 70% of large manufacturing companies use data analytics tools<\/a>, but many business intelligence systems only produce diagnostic or descriptive analytics<\/strong>.<\/p>\n Both help companies learn from the past but not necessarily predict the future \u2014 something that machine learning and deep learning models can do using a combination of past and real-time data.<\/p>\n Machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) can handle structured and unstructured data, such as text and images, but typically require feature extraction to be effective. In contrast, artificial neural networks (ANNs), including convolutional neural networks (CNNs) for visuals and recurrent neural networks (RNNs) for sequential data, directly process unstructured data. ANNs excel in tasks like image recognition and natural language processing by learning from complex patterns in raw data without extensive manual preprocessing.<\/p>\n Predictive models<\/strong> anticipate future events (such as equipment failure due to wear and tear) based on historical and real-time data. Prescriptive models<\/strong> suggest an optimal course of action to reach the target outcome (for example, sending a maintenance crew at a particular time to minimize downtime).<\/p>\n Using these techniques will enable new operational capabilities in the industrial sector.<\/p>\n <\/p>\n Source: Accenture \u2014 Next Generation Manufacturing Systems Architecture<\/a><\/em><\/p>\n Many industry leaders have advanced predictive and prescriptive capabilities for workflows. For example, Airbus, GE Digital, and Delta TechOps<\/strong> co-developed Skywise<\/a> \u2014 a data-driven aircraft operations platform with a predictive maintenance solution<\/a> that anticipates component failure by analyzing aircraft sensor data.<\/p>\n ExxonMobil<\/strong> and WinGD developed<\/a> a predictive cylinder condition monitoring service for the maritime industry to help vessel owners reduce unplanned stoppages, optimize engine performance, save costs, and extend engine overhaul intervals.<\/p>\nUnpacking the industrial metaverse: Key technology components<\/h2>\n
Digital twins<\/h3>\n
Digital twin technology adoption<\/h4>\n
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Augmented reality(AR) and virtual reality (VR)<\/h3>\n
Advanced analytics and artificial intelligence (AI)<\/h3>\n
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