Revolutionize Your Manufacturing Process with IIoT Digital Twins

Are you ready to revolutionize your manufacturing processes and take your business to the next level?

Traditional manufacturing comes with a host of challenges, from long development times to inconsistent product quality. The IoT Digital Twins technology empowers manufacturers to overcome these obstacles, as well as unlock a world of possibilities by creating virtual replicas of their physical manufacturing processes to simulate, monitor, and optimize operations in real-time. This not only helps to reduce development times and costs but also improves product quality and increases organization’s productivity.

Digital twins enable manufacturers to identify potential issues before they occur, allowing them to take preventative measures and avoid costly downtime and disruptions. What’s the result? Improved efficiency, reduced costs, and enhanced product quality, leading to increased profitability and a stronger competitive edge.

Don't wait – embrace the power of IoT Digital Twins today and transform your manufacturing processes and uncover efficiencies that are yet to be utilized.

In today's manufacturing landscape, more than 31% of production processes have been digitized through the use of smart IoT devices. With over 43 billion IoT devices being connected and generating enormous amounts of data, it is essential to manage and leverage this data effectively. One of the possible use cases is predictive maintenance. According to Gartner, the average cost of machine downtime is $5,600 per minute, which pinpoints the critical need of manufacturers to eliminate equipment failure issues and switch from reactive to proactive maintenance strategies. AR and VR-enabled devices have also been utilized to reduce the cost of basic equipment repairs and troubleshooting.

The Digital Twin technology takes IIoT and other business systems to the next level by enabling new capabilities. It allows manufacturers to simulate factory operations with new equipment or parameters, leading to better decision-making and cost optimization by consolidating supply chain and HR data in one space. Digital Twins also allow for the simulation of factory throughput by changing parameters using a virtual copy of the physical environment, which helps manufacturers identify bottlenecks before making equipment purchases. This kind of simulation offers manufacturers an unparalleled level of control and insight into their production processes, leading to increased efficiency and improved product quality, while enforcing a competitive position.

Unfortunately, enterprise-grade IIoT Digital Twin solutions are complex systems that involve a multitude of interconnected devices, sensors, networks, and applications.

Here is a typical blueprint of the solution architecture we implement for our customers. It includes Edge computing that involves processing data locally on edge devices, such as sensors and gateways, instead of sending every operation to the cloud. This approach reduces latency, increases efficiency, and ensures real-time responsiveness, making it ideal for the real-time monitoring and control of production processes.

Edge computing also paves the foundation for the integration of legacy SCADA systems and devices, enabling manufacturers to leverage their existing infrastructure while implementing new IoT solutions. As for the cloud aspect of a solution, the level of complexity significantly increases, as we need to ingest large amounts of data to be stored, processed, and analyzed.

This requires robust data management capabilities that can handle both structured and unstructured data, and provide real-time insights of the system's performance.

Achieving interoperability in such a complex system requires standardization of protocols and interfaces, as well as compatibility between different hardware and software components.

Security measures need to be implemented at all levels of a system, from devices and networks to third-party applications integrated with a platform.

Building an enterprise-grade IoT Digital Twin solution can be costly, as it requires investment in hardware, software, and skilled personnel.

Additionally, maintenance and support costs can be significant, especially for systems that are deployed at scale for multiple factories.

The main issue, however, is managing large volumes of data. An average factory generates circa 50 terabytes of data annually, which makes both scalability and cost management a complex endeavor.?

Addressing these challenges encouraged us to build a framework that enables efficient management of BigData, and ensures scalability while keeping costs under control.

This framework is based on the latest developments of Microsoft Azure IoT Digital Twins and is designed to meet the specific needs of manufacturing facilities. It allows us to leverage edge computing for the purpose of real-time monitoring and control of production processes, which leads to increased efficiency and productivity.?

With this being said, let's deep dive into how Digital Twins help to address the abovementioned challenges.?

Every object in this world will soon have a digital twin.

In manufacturing, IoT Digital Twins can be used not only to monitor the performance of production equipment, but also to identify maintenance needs, and optimize production processes.

Today, they are applied at the stage of manufacturing facilities design and planning to reduce construction costs and improve factory throughput.

In combination with AI, IoT Digital Twins make informed decisions and react faster than humans to resolve equipment failure, change a supplier, or shut down unsafe factory lines.

IoT Digital Twins is not just a fancy 3D visualization.

It is a powerful ecosystem that enables organizations to optimize their operations, and transform their manufacturing processes- from creating a virtual representation of physical objects, systems, and processes to identifying issues and testing potential solutions based on simulating different scenarios.?

This leads to more informed and automated decision-making, improved productivity, and better adaptability to changing market needs.

Thus, IoT Digital Twins offer significant benefits to organizations looking to improve their operations and stay ahead of the competition.

Let`s now look at how we model such complex industrial digital twins

There are three main approaches to modeling IoT Digital Twins visualization:

• BIM Import: involves importing data related to construction projects and equipment from a BIM system to the IoT Digital Twin solution.?
• CAD design from scratch: typically used for simple manufacturing processes where BIM is not well maintained.?
• 3D Scanning: encompasses physical objects and spaces scanning using 3D technology like Autodesk Reality Capture to create a digital representation. This method is useful for retrofitting existing structures or creating a Digital Twin of a specific physical object that has no CAD model yet.?

Each of these approaches has its advantages and disadvantages, with the optimal choice being directly dependent on the specific project's needs. Regardless of the approach used, modeling IoT Digital Twins visualization requires expertise in 3D modeling, data science, and domain-specific knowledge.

The most popular and cost-effective scenario of building digital twins visualization is a BIM import. Autodesk is an industry leader in this segment allowing seamless integration at all stages of factory construction.

IoT Digital Twins can enable advanced simulations of construction projects, leading to improved project delivery and outcomes. This can enhance the decision-making capabilities?of construction teams, as they can anticipate and optimize project performance using simulated Digital Twins data insights.

Additionally, IoT Digital Twins can enable better collaboration between different teams and stakeholders, ensuring that everyone has access to the latest data. Depending on the specific BIM 360 product offering, IoT Digital Twins can be leveraged to improve construction documentation, planning, and operations.

Overall, IoT Digital Twins can enhance and play a crucial role in the capabilities of Autodesk? BIM 360? and improve the outcomes of a construction project.

So how do we integrate these two complex ecosystems?

Fortunately, Microsoft and Autodesk have collaborated already for a couple of years to integrate Azure IoT Digital Twins with Autodesk Platform Services.?

This integration enables developers to build IoT applications that leverage both IoT data and Autodesk immersive experiences, as well as enable better collaboration between different stakeholders in the development process. For example, architects, engineers, and construction teams can collaborate more effectively by leveraging the same data and insights to optimize project performance.?

Here is an example of a simple Web App utilizing both Autodesk Platform Services and Azure IoT Digital Twins to display BIM models of the manufacturing process.

The app allows the user to select a model of the factory line, which triggers a request to an Azure Functions-based microservice that extracts metadata of the selected model using Autodesk Platform Services. The metadata is stored in Azure Cosmos DB and used to map the entities of Digital Twin models. A user can view the manufacturing process 3D visualization with all real-time properties of equipment and its analytics.

Now let's look into key concepts of IoT Digital Twins that simplify interoperability with a real factory.

IIoT Digital Twin ontology is a set of concepts and categories that represent the properties and relationships between entities in a Digital Twin system. It is a formal representation of the domain knowledge that serves as a common language for communication between different stakeholders involved in the Digital Twins development process.?

Digital Twin ontology is essential to unify the approach towards Digital Twin systems development and implementation in different manufacturing domains. These domains have their own unique concepts and terminology, which can lead to miscommunication and misunderstandings among stakeholders.

Digital twins ontology provides a standardized vocabulary that can be used across domains, ensuring that stakeholders have a common understanding of a Digital Twin system and its components. It allows systems to exchange data and information in a standardized format, reducing the need for custom integrations and simplifying the process of connecting different systems.

Furthermore, Digital Twin ontology enables the development of intelligent apps that can analyze data and information in a Digital Twin system based on abstract interfaces. By representing domain knowledge in a formal and structured way, apps can understand the relationships between entities and make informed decisions.

Overall, Digital Twin ontology is an essential component of Digital Twin systems. It enables communication, interoperability, and intelligent decision-making, making Digital Twin systems more efficient.

Technically Azure IoT Digital Twin ontology is described in DTDL language and defines how data will be stored within the IoT system and projected into the visualization. It is a very intuitive JSON format that allows developers to describe domain ubiquitous language and share the same model with all integrated apps.

At Intellias, we have built our own framework and blueprints to deploy IIoT Digital Twins at scale for our customers. It can enable manufacturers to speed up their IIoT deployments and cut time to market for their solutions by up to 50%. It supports common IoT scenarios, including remote asset monitoring, built-in predictive maintenance, fleet management, and beyond. Also, we provide a wide spectrum of IoT services for full-cycle enterprise IoT systems development.

Thank you for your time reading this article hope it was useful for you, feel free to contact me for more details about making a Digital Twin work for your business.?