Automated NDT in Industry 4.0

Introduction

Automated NDT is an increasingly important discipline for driving industries, as it allows the condition and quality of assets to be verified without causing surface or structural damage. Traditionally, NDT methods require significant manual effort and expertise, making the process slow and prone to human error. However, the advent of automation and digital technologies is transforming the NDT landscape, making it more efficient, accurate, and reliable.

This article discusses the advances in automated NDT, its benefits, applications, and future prospects in the digital age, and how it fits into the Industry 4.0 revolution.

The evolution of Non-Destructive Testing

Traditional methods

Traditional NDT methods, such as ultrasonic, radiographic, magnetic particle, and liquid penetrant testing, have been used for decades. These methods rely heavily on skilled technicians who interpret the results manually. Although effective, these methods are labor intensive and can be influenced by human error, leading to inconsistent results.

The shift towards automation

The digital revolution has brought about significant changes in NDT, with automation playing a key role. Automated NDT systems use advanced technologies such as robotics, Artificial Intelligence (AI), and Machine Learning (ML) to perform inspections more accurately and quickly. These systems can operate continuously, reducing downtime and increasing productivity.

Key technologies driving automated NDT

In Industry 4.0, advanced NDT has been integrating, through the development or adaptation of new technologies, monitoring and assessing in real time and in an automated way the conditions of the equipment or products generated. Among the new technologies developed or adapted are the following.

Robotics

Robotic systems are increasingly being integrated into NDT processes. They can navigate complex geometries and hostile environments, performing inspections that are difficult or dangerous for technicians. For example, crawler robots equipped with ultrasonic sensors can inspect pipelines and storage tanks, while drones can assess the condition of bridges and wind turbines.

Artificial Intelligence and Machine Learning

AI and ML algorithms are revolutionizing data analysis in NDT. These technologies are designed to process large amounts of data quickly and accurately, identifying defects and anomalies that might go undetected during the technical inspection. AI-powered software can take into account previous inspections and continuously improve their accuracy and efficiency over time.

Internet of Things (IoT)

All data, devices, and sensors are connected to each other and can be accessed through the cloud, thus registering a database and thus enabling AI and digital twins¹. IoT is a key enabler of automated NDT.

Cloud storage

Cloud storage allows you to store data and files in an off-site location that you access via the public Internet or a private network connection. Data you transfer off-site for storage becomes the responsibility of an external cloud provider, the provider hosts, secures, manages, and maintains the servers and associated infrastructure, and ensures access to the data when you need it². The safeguarding of automated NDT data ensures that the behavior of monitored equipment and product variables is known.

Digital twins

The digital twin is a digital entity that has exactly the same properties as the physical object. As a result, the behavior and state of the real object can be accurately seen and predicted. An AI can be used as a basis for such simulations³. In automated NDT, the integration of digital twin technology helps to improve inspection procedures.

Advanced instrument and sensor technologies

Advanced digital imaging and sensor technologies, such as Phase Array Ultrasonic Testing (PAUT) and Computed Radiography (CR), provide high-resolution images and detailed information about the material being inspected and can be adapted to automated NDT. These technologies allow more accurate detection and characterization of defects, improving the reliability of NDT. Figure 1, shows a waveform instrumentation for continuous monitoring of long pipe lengths⁴ as an example of integration of advanced NDT into Industry 4.0.

Benefits of automated NDT

Increased efficiency

By automating these tests, inspections can be performed faster than manual methods. Robots and drones inspect large areas quickly, and AI algorithms can analyze data in real time, providing immediate results. This increased efficiency reduces downtime and enables more frequent inspections, ensuring continuous monitoring of critical assets.

Increased accuracy and consistency

By eliminating human error, automation ensures more accurate and consistent results. AI algorithms can detect even the smallest defects, and advanced imaging technologies provide detailed information on material conditions. This accuracy is particularly important in industries where safety is paramount, such as aerospace and nuclear power.

Cost savings

The initial investment in automated NDT systems is significant, the long-term savings are substantial. Automated systems reduce labor costs, minimize downtime, and extend asset life by enabling proactive maintenance. This leads to a higher return on investment and an overall improvement in operational efficiency.

Improved safety

Automation reduces the need for human technicians to work in hazardous environments. Robots and drones can perform inspections in hazardous locations, such as power lines or underwater pipelines, minimizing the risk of accidents and injuries. This improves workplace safety and ensures compliance with strict safety regulations.

Automated NDT applications

Aerospace industry

This industry relies heavily on NDT to ensure the safety and reliability of aircraft components. Automated NDT systems can inspect critical parts, such as wings, fuselage, and engines, for defects and corrosion. AI algorithms analyze the data, providing detailed reports that help make informed maintenance decisions.

Oil and gas industry

Automated NDT is used in the oil industry to inspect pipelines, storage tanks, and offshore platforms. Robotic crawlers and drones can traverse challenging terrain and environments, performing inspections that are difficult for technicians. This ensures the integrity of critical infrastructure and prevents catastrophic failures.

Power generation

Power plants, including nuclear, coal, and wind, use automated NDT to monitor the condition of their assets. Robots equipped with advanced sensors can inspect boilers, turbines, and other components for signs of wear and tear. This proactive approach helps maintain optimal performance and extend equipment life.

Manufacturing industry

In manufacturing processes, automated NDT is used to inspect products and components for defects during the production process. This ensures that only high-quality products reach the market, reducing the risk of recalls and warranty claims. AI-powered systems can analyze data from multiple inspections, identifying patterns and trends.

Future prospects for automated NDT

Integration with Industry 4.0

The concept of Industry 4.0, characterized by the integration of digital technologies into manufacturing and industrial processes, presents significant opportunities for automated NDT. By integrating NDT systems with intelligence of things (IoT) devices, digital twins, and data analytics platforms, companies can achieve real-time monitoring and predictive maintenance. This approach ensures the optimal performance and lifespan of assets.

To ensure quality, it is important to embrace the digital transformation of non-destructive evaluation. It can be an integral part of cyber-controlled production and asset lifecycle maintenance. These two value propositions covering the asset lifecycle require digitally controlled NDT procedures and qualitative data to support automated decision-making, for most known situations³.

Advances in Artificial Intelligence and Machine Learning

As AI and ML technologies continue to advance, the capabilities of automated NDT systems will improve. Future systems will be able to analyze even larger datasets with greater accuracy, identifying subtle patterns and anomalies that were previously undetectable. This will improve the reliability and efficiency of NDT processes in various industries.

Development of new sensor and instrumentation technologies

Continued research and development in sensor and instrumentation technologies and innovations equipped with cutting-edge technologies such as Internet of Things (IoT) connectivity, artificial intelligence (AI), and machine learning (ML) algorithms will provide real-time information and deeper insights into material properties and defects. This will help to set immediate and more accurate maintenance strategies. These advances will further enhance the capabilities of automated NDT systems.

Conclusion

As the digital age evolves, the integration of automated NDT with Industry 4.0 and the development of new technologies will further enhance their capabilities, ensuring continuous improvement of inspection processes. Industries that embrace these innovations will benefit from greater operational efficiency, improved safety, and a higher return on investment.

References

1. MAXIMILIAN TOPP. The NDT 4.0 Guide – Everything you need to know; Accessed on 20 May 2024. https://sentin.ai/en/the-ndt-4-0-guide-everything-you-need-to-know/
2. IBM What is Cloud Storage; Accessed 20 May 2024. https://www.ibm.com/mx-es/topics/cloud-storage
3. NORBERT MEYENDORF, NATHAN IDA, RIPUDAMAN SINGH, JOHANNES VRANA. NDE 4.0: Progress, promise, and its role to industry 4.0; Accessed on 21 May 2024. https://www.sciencedirect.com/science/article/abs/pii/S096386952300172X.