Traditional manufacturing comes with a host of challenges, from long development times to inconsistent product quality. IoT Digital Twins technology empowers manufacturers to overcome these obstacles and 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 time and costs but also to improve product quality and increase the 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. The result is 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 to transform your manufacturing processes and uncover efficiencies that are yet to be utilized.
AR IIoT Digital Twins simulation
In today’s manufacturing landscape, more than 31% of production processes have been digitized via smart IoT devices. With over 43 billion IoT devices connected globally and generating enormous amounts of data, it is essential to effectively manage and leverage this data. One of the possible use cases is predictive maintenance. According to Gartner, the average cost of machine downtime is $5,600 per minute, highlighting the critical need of manufacturers to eliminate equipment failures 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 allowing 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 solidifying their competitive position.
Unfortunately, however, enterprise-grade IoT digital twin solutions are complex systems that involve a multitude of interconnected devices, sensors, networks, and applications.
Intellias IoT Digital Twins Framework
The blueprint of the solution architecture we typically implement for our customers includes Edge computing, which 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 lays 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 the 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 to handle both structured and unstructured data and provide real-time insights into 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 the system, from devices and networks to the third-party applications integrated with the 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 data volumes. An average factory generates around 50 terabytes of data annually, making both scalability and cost management complex endeavors.
Addressing these challenges encouraged us to build a framework that enables efficient management of big data 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 leading to increased efficiency and productivity.
That said, let’s dive into how Azure 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 designing and planning manufacturing facilities to reduce construction costs and improve factory throughput. In combination with AI, IoT digital twins allow for making informed decisions and react faster than humans to resolve equipment failures, change a supplier, or shut down unsafe factory lines.
Key features of IIoT Digital Twins
IIoT Digital Twins is not just a fancy 3D visualization.
Digital twins form 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.
Digital Twins 3D Visualization options
There are three main approaches to modeling IoT Digital Twins visualization:
- BIM import involves importing data related to construction projects and equipment from a building information modeling (BIM) system to the digital twin solution.
- CAD design from scratch is typically used for simple manufacturing processes where BIM is not well maintained.
- 3D scanning encompasses scanning physical objects and spaces 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, visualizing IoT digital twins requires expertise in 3D modeling and data science along with domain-specific knowledge.
The most popular and cost-effective scenario for visualizing digital twins is BIM importing. Autodesk is an industry leader in this segment, allowing for seamless integration at all stages of factory construction.Shape
The most popular and cost-effective scenario for visualizing digital twins is a BIM import. Autodesk is an industry leader in this segment, allowing for seamless integration at all stages of factory construction.
Autodesk BIM 360 platform
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 twin data insights.
Additionally, IoT digital twins can enable better collaboration among 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 critical role in the capabilities of Autodesk® BIM 360™ and improve construction project outcomes.
So how do we integrate these two complex ecosystems?
Fortunately, Microsoft and Autodesk have already collaborated for a couple of years to integrate Azure Digital Twins with Autodesk Platform (formerly Forge) Services (APS).
Autodesk Platform Services integration with Azure Digital Twins
This integration enables developers to build IoT applications that leverage both IoT data and immersive Autodesk experiences as well as enable better collaboration among various 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.
Autodesk Platform Services capabilities
Here is an example of a simple web app utilizing both Autodesk Platform Services APIs and Azure Digital Twins to display BIM models of the manufacturing process.
Here is the way how a simple Azure IoT Digital Twin app utilizes Autodesk Platform Services capabilities to simulate 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 API. The metadata is stored in Azure Cosmos DB and used to map the entities of digital twin models. A user can view a 3D visualization of the manufacturing process with all real-time equipment properties and its analytics.
Now let’s look into key concepts of IoT digital twins that simplify interoperability with a real factory.
IIoT digital twins ontology
IIoT digital twins ontology refers to a set of concepts and categories that represent the properties of 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 among stakeholders involved in the digital twins development process.
Digital twins ontology is essential to unify the approach towards developing and implementing digital twin systems in different manufacturing domains. Each domain has its 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 twins 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 twins ontology enables the development of intelligent apps that can analyze information in a digital twins 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 twins ontology is an essential component of digital twins systems. It enables communication, interoperability, and intelligent decision-making, leading to greater efficiency of digital twins systems.
Azure Digital Twins Definition Language – DTDL
Technically, the Azure Digital Twin ontology is described in DTDL language and defines how data will be stored within the IoT system and projected into the visualization. DTDL is an 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 the time to market for their solutions by up to 50%. It supports common IoT scenarios, including remote asset monitoring, predictive maintenance, fleet management, and beyond. Also, we provide a wide spectrum of IoT services for full-cycle enterprise IoT systems development.
Contact us for more details about making digital twins work for your business.