Digital Twins in Quality Control – What If You Could Predict Changes in Quality

July 8, 2020

Digital twins offer the most efficient solution for enhancing quality control. The concept was first introduced almost 20 years ago but was limited to companies with massive resources, such as NASA.

Thanks to the latest intelligent manufacturing technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning, implementing a digital twin is more efficient and less costly.  

Creating a digital twin is easier than ever before, opening the doors to a wide range of benefits in manufacturing. You may even be able to predict changes in quality and take corrective action.  

Here is a closer look at the concept of digital twins and how to use them for smart production processes.

What Is a Digital Twin?

A digital twin is essentially a virtual model of a physical process. This allows operators to analyse each component of a physical process and detect issues before they occur, including issues that may impact the quality of products.

Digital twins may use a variety of technologies to monitor physical processes, including virtual reality, augmented reality, 3D graphics, data modelling, and sensors. These technologies create a replica of the physical process.

Companies have implemented digital twins at various manufacturing levels:

1. Process level – recreates the entire manufacturing process

2. System level – monitors and improves an entire production line

3. Asset level – focuses on a single piece of equipment within the production line

4. Component level – focuses on a single component of the manufacturing process

No matter the level, digital twins typically rely on three primary elements – past data, present data, and future data. Companies review the historical performance of equipment or processes. They also obtain real-time data from sensors and outputs from various equipment.

Machine learning and input from engineers allows companies to use digital twins to predict future data. While the previous elements help businesses understand their processes, machine learning makes it easier to predict changes.

Practical Uses for Digital Twins in Manufacturing

Thanks to the early adopters of the digital twin concept, there are many examples of the benefits of creating a virtual mirror of physical systems and processes.  

The implementation of digital twins is suitable for a broad range of applications. Some of the ways that companies are already using this concept include:

1. Testing new systems

2. Managing assets in real-time

3. Advanced diagnostics

4. Improving efficiency

5. Cross-group collaboration

6. Quality control tools  

The benefits of digital twins are far-reaching and unique to different industries. It is predicted that by 2022, over two-third of companies implementing IoT will also have deployed a digital twin solution into production.

As more companies implement this concept, more advantages and applications are likely to be discovered. This is especially true with factory automation, which depends heavily on real-time data analysis.

Testing New Systems

A common use of digital twins is to test new systems. You can create a virtual representation of an entirely new system or use the technology to test changes to equipment or manufacturing processes.  

Using digital twins for testing allows manufacturers to test ideas before investing millions of dollars in physical equipment or expanded facilities. If the idea proves to be effective, the virtual model also eases the transition to the new processes, as engineers already have a blueprint for the changes.

Managing Assets in Real-Time

Another common use of digital twins is to monitor and manage assets in real-time. The technology can collect data from equipment and machines to give engineers real-time access to the performance of each component and process. Engineers can easily detect potential money-saving solutions and handle maintenance and repair more efficiently.  

Advanced Diagnostics

Digital twins help optimise maintenance, repair, and operations (MRO) of equipment and machines. The technologies used to manage digital twins obtain data from sensors and the outputs from equipment. Using this data, engineers can diagnose equipment issues with greater speed and agility.

For example, an engineer may use a digital twin to review machinery load levels, material components, and cycle times to determine the risk of malfunction. They may also use the available data to improve preventative maintenance practices, increasing the longevity of assets.  

Improving Efficiency

Digital twins are often used with optimised production technology, such as machine learning software. The insight provided by digital twins paired with machine learning helps businesses increase the efficiency of their processes.  

You can review any component or piece of equipment in the manufacturing process, along with reviewing the entire manufacturing process. The detailed analysis of each component in the manufacturing process helps reduce waste and increase yields.  

Digital twins also increase the efficiency and speed of scaling manufacturing processes. Virtual models make it easier to recreate or scale existing processes.  

Cross-Group Collaboration

Some manufacturing processes involve more than one group. For example, you may need to send a part to another facility for the final fabrication processes. Using digital twins improves your ability to collaborate with multiple groups.

Digital twins provide access to each stage of the manufacturing process. Multiple groups can rely on the same data to monitor the production run.  

Quality Control Tools

Quality control is an important part of any manufacturing process. With the use of digital twins, engineers can analyse data in real-time. They can detect quality control issues as they occur and then use data obtained from the digital twin to uncover the causes of the issues.  

Potential Challenges of Digital Twins

Creating an effective digital twin often requires access to big data, which is increasingly handled using cloud-based enterprise solutions. The massive amounts of data used to analyse assets and processes create potential data security risks and considerations, such as:

1. Access privileges

2. Data encryption

3. Addressing vulnerabilities

4. Frequent security audits

According to one expert, about 75% of digital twins will include at least five different endpoints by 2023. Each connection between cloud storage solutions and devices increases the security risks. To enjoy the benefits of digital twins, companies will need to actively evaluate potential vulnerabilities.

In the end, the use of digital twins is predicted to increase dramatically in the coming years. More businesses are starting to realise the potential of this concept and are actively applying it to their manufacturing processes.  

As more companies adopt digital twins, it becomes more essential for others to follow suit. If you want to increase efficiency and quality control, developing a digital twin may be a necessity.

Feel free to also check out our other posts:

The End of On-Site Monitoring: How The Pandemic Will Change the Way Engineers Run Their Production Lines

Digital Twin: A Bridge Between Physical and Digital Worlds

Towards Industry 4.0: Digital Transformation in Manufacturing