In a highly competitive economy where productivity is everything, inefficiencies resulting from poor maintenance practices and resource management are detrimental to any manufacturing facility. They are not only financially damaging, but also allow competitors to get ahead. That is where digital twins in manufacturing come in as a strategic necessity.
These virtual replicas of physical systems let engineers and managers understand how each component connects with the other. They provide an excellent understanding of how a physical system works under certain conditions and the long-term maintenance situation.
This guide will give you a solid understanding of what a digital twin is and how it works. We will also discuss the process of creating a digital twin and how it pairs up with AI to transform the manufacturing capabilities of your facility.
What are Digital Twins in Manufacturing?
A digital twin is a digital or virtual replica of a physical object, workspace, or machine. This copy of the real-world object behaves exactly like its twin and mimics its different interactions. The complexity of a digital twin depends on how it is created and its intended use by the company.
Digital twins are usually created by attaching sensors to the real-world objects that an organization wants to twin or mimic. These sensors record critical data about performance and other aspects of a machine or object. In the modern manufacturing age, they are becoming the key to implementing industrial IoT.
A digital twin usually has three parts:
- The physical system or object
- The virtual replica
- The link between these twins
When the digital twin mimics the behavior of the physical object, it allows engineers and managers to analyze real-time performance, modify the behavior, and optimize it.
Four Main Types of Digital Twins
Today, there are several types of digital twins with varying roles.
- Component Twins: These twins are the smallest yet really important, as they form the building blocks of the manufacturing machinery.
- Asset Twins: An asset is defined as a combination of a few components. Asset twins show how different parts work together and the improvements engineers can make to those combinations.
- System Twins: System twins go a step higher and show how different assets work together in a system. Understanding these interactions is critical to boosting productivity.
- Process Twins: These are the highest level of manufacturing twins, where you see how systems operate on a large scale inside a manufacturing facility. Understanding them ensures that the whole manufacturing operation runs error-free.
Benefits and Uses of Digital Twins in Manufacturing
Let us discuss some of the benefits digital twins bring to the table.
1. Process Optimization
Modern manufacturing facilities are full of various sensors that tell managers about their current state. With this information, engineers can create a digital twin of different processes that happen within that facility.
It helps operators identify output bottlenecks by understanding the object and how its different components or assets work together. Once operators fully understand how a system works, they can optimize the machinery or manufacture it differently, which helps them increase the quality and the scale of the outputs.
2. Better Productivity
A digital twin greatly helps reduce inefficiencies and increase the productivity of a manufacturing system. Managers can monitor the performance in real-time, resolve any problems they encounter, and cut down on maintenance costs. For instance, a digital twin can be deployed to analyze pressure and temperature data to identify potential issues before they get out of hand.
In addition, digital twins in manufacturing can simulate physical systems in varying environmental conditions to understand their effects. These simulations identify a system’s weak spots and tell engineers where to focus during the next maintenance phase.
Another area one can explore is optimizing performance with edge computing solutions by pairing them with digital twins. It allows for real-time data analysis and rapid decision-making, significantly improving the utility of the virtual replica.
3. Asset Integrity Management
A manufacturing facility stays profitable only when it operates continuously. This is true for all industries, including petrochemicals and hydro-processing. These industries have seen a rapid rise in the adoption of digital twins for asset integrity management. Digital twins help refineries and manufacturing facilities simulate the current conditions of assets on a screen as if they were a real, physical site.
In the petrochemical industry, this is especially useful for visualizing a large amount of data in an elaborate 3D model. It provides actionable intelligence to facility managers so that they can schedule tank maintenance and visualize operations from any location.
4. Lower Maintenance Durations
Manufacturing facilities often schedule shutdowns for maintenance activities. During the maintenance phase, the machines are deeply inspected and renovated. This process requires a lot of tools and labor, which is a bit expensive.
While this is necessary for smooth operations, maintenance shouldn’t take too long, or the productive capacity takes a hit. In addition, it almost always shows issues that you didn’t anticipate before, which requires managerial expertise and financial flexibility.
That is because any manufacturing facility shows its age as time goes by, with each new maintenance phase requiring more work than the previous one.
Digital twins in manufacturing are an excellent solution to this problem because they are really precise in showing the weak spots of a system. They tell the maintenance teams which areas are critical points of failure, how much more can they go on before completely breaking down, and when to repair them.
5. Simulating Hypothetical Scenarios
Digital twins offer an excellent way to simulate various conditions under which the manufacturing equipment could operate. However, these conditions don’t always stay the same during manufacturing.
As time passes, those variances in conditions add up and cause damage to the manufacturing system. Structural digital twins in manufacturing can help engineers simulate extreme conditions, understand their impact on the integrity of machines, and prepare a plan for their long-term health.
Creating Digital Twins: A Step-by-Step Guide
Since a digital twin is all about capturing accurate data and gaining insights, it is crucial to build it the right way. To begin with building a reliable digital twin, one must understand it has three important pillars:
- Sensor deployment
- Data processing
- Accurate model generation
Once you understand these basics, you can follow the steps to create successful digital twins in manufacturing.
Step 1: Define Why You are Building it
The first step of creating a digital twin is determining who will use it. How are they going to use it? Is it to be used by one group or multiple groups within your organization? A digital twin can be created for a range of purposes, and defining those purposes is critical.
Usually, digital twins in manufacturing are used for:
- Accelerating product development
- Enhancing product capabilities
- Analyzing and optimizing complex processes
- Boosting the system efficiency
Answering the important question immediately builds a strong foundation for your digital model. Careful planning at the initial stage avoids costly overruns and ensures that the system works as intended.
Step 2: Collecting High-Quality Data
Digital twins in manufacturing can replicate the behavior of their real-world peers only if you capture accurate data. To gain valuable data, you must answer these two questions:
- What kind of data do you already possess?
- Which additional data do you need to build a useful twin?
Without understanding and answering these questions, you will create data lakes that won’t be of much use. They’ll contain inaccurate data that wouldn’t fare well in analysis.
Step 3: Create a Visual Representation
After answering the foundational questions, it is time to generate the initial digital model. The initial model is based on the geometry and system constraints of the original system. Here is what this model helps with:
- Optimal sensor placement
- Making the model computationally efficient with constraining
- Generating accurate information
Once the model is generated, data from the real-world asset and the model are compared to assess the latter’s accuracy. It allows the manufacturing facility to fine-tune the model according to the real-world asset and make it as accurate as possible.
Step 4: Implementing the Digital Twin
Now that you have created the initial model and know what to do, it is time to implement digital twins in manufacturing. This multi-step process starts with gathering data, storing it safely, and processing it efficiently. Once these three steps are completed, you need to represent the data accurately in your system.
The following are some important considerations for this stage:
- Structuring data transfer between devices
- Managing data security during transfer and storage
- Creating a system that isn’t overly complex
Step 5: Optimization and Expansion
While digital twins in manufacturing start small and simple, they grow over time to become more and more complex, capturing whole systems. When creating a digital twin, always take short-term goals and long-term gains into consideration. A small digital twin might be helpful in the short term, but to gain the most from it in the long run, you need to move towards complexity.
Enhancing Digital Twins in Manufacturing: The Role of AI
If one defines a digital twin in the simplest terms, it is a collection of several data points. In the initial stages, engineers rely on statistical data analytics for diagnostic analysis. With machine learning and advanced AI algorithms, one can take this analysis several notches above and gain even more valuable insights from the digital replica.
Several organizations are exploring the role of AI in IoT devices at this point, a trend that will only strengthen in the coming years.
Novel Product Enhancements
The role of digital twins in manufacturing is to provide quality data to an organization in huge volumes, whether it comes from a small component or the whole machine. Machine learning algorithms can transform this data capture mechanism and provide completely new ways of enhancing products.
Real-World Examples: Rolls-Royce
Rolls-Royce has created a dedicated digital twin platform that aggregates data from all manufactured aircraft engines. By collecting historical data on one platform, Rolls-Royce has generated highly efficient maintenance schedules. Thanks to the use of AI-based predictive analysis, Rolls-Royce has extended periods between successive maintenance phases by up to 50%.
Not only that, but Rolls-Royce has also helped its clients become much more efficient using AI and digital twins. One of their airline clients has cut down 200 million kg of carbon dioxide and 85 million kg of fuel through flight routine optimizations since 2014.
Computer Vision for Quality Control (QC)
Computer vision algorithms can help manufacturers create a new era of data capture by analyzing images and videos. This analysis can greatly help teams identify performance quirks and check how different physical parameters would perform in a specific environment.
In essence, it provides engineers with another layer of data for real-time process inspections. Engineers can now set up automated systems instead of roaming assembly lines to identify defects.
Real-World Examples: NVIDIA
The partnership between NVIDIA and HPE serves as a great example here. Last year, they deployed AI-based video analysis tools in their European manufacturing facilities and produced great results. Manual inspection just wasn’t enough to keep pace with the 1,000 different server configurations possible on the production line.
NVIDIA developed a dedicated edge-to-cloud architecture to train deep learning models for detailed audits. The GPU giant managed to reduce out-of-the-box QC issues by 25% and enhanced inspection speed for each server by 96 seconds.
Unlocking New Industrial Avenues With Digital Twins and AI
The use of digital twins in manufacturing has been limited, but the situation is expected to change rapidly. According to a McKinsey & Company report, the adoption of digital twins will grow at 60% annually and generate $73.5 billion by 2027.
Similarly, the impact of AI on manufacturing hasn’t ballooned, but it is something that we can expect to change in the coming months and years. Manufacturing teams can ask the generative AI algorithm for the latest asset performance numbers instead of going through detailed custom reports each time.
Many manufacturing leaders are already deploying AI pilot projects on a smaller scale. According to a Deloitte survey, around 93% of companies believe AI will have a significant role in the manufacturing sector in the coming years.
Wrapping Up
The idea of using digital twins in manufacturing is no longer a distant dream but a reality that is becoming more and more prevalent. The real-world examples discussed above show how companies are leveraging this technology to get ahead and why more companies want a piece of this pie.
After you build a digital twin, you need to gain valuable and actionable insights from it. That is where ZirkelTech comes in with our world-class digital consultations. Whether you are in manufacturing, finance, or construction, we provide cost-effective centralized management of Salesforce, MS Dynamics applications, and SAP S/4 HANA.
ZirkelTech is at the forefront of the generative AI revolution and consistently explore its new applications in product engineering. With that, we are also heavily invested in data science & analytics, the Internet of Things (IoT), Cloud and DevOps, and much more.
So, contact us today and transform your manufacturing processes through our AI-powered consultations.
FAQs
1. How are digital twins in manufacturing used?
In manufacturing, a digital twin is a virtual replica of a physical product, machine, or production process. It allows manufacturers to analyze and optimize operations, predict issues, design maintenance schedules, and improve production efficiency.
2. How do digital twins in manufacturing handle inaccurate or incomplete data?
Digital twins make up for incomplete or inaccurate data by:
- Implementing data cleansing techniques
- Monitoring data quality
- Using feedback loops to update the model based on real-world information
- Conducting data quality checks to identify and correct errors
3. What are the risks of over-reliance on digital twin simulations?
One of the biggest risks of relying on a digital twin is that the data can be inaccurate due to being infected with malware. Another risk in using a digital twin is overconfidence in a model’s accuracy, especially when there is no human monitoring the data.
4. What is the future of digital twins in manufacturing?
Digital twins have a promising future, thanks to groundbreaking developments in AI, data analytics, and IoT. They are becoming more accurate and sophisticated, and we will see them integrated across various industries.