AI in Ultrasonic weld Inspection: Advanced detection

The new era of industrial inspection

Ultrasonic weld inspection has for decades been a fundamental tool for quality control and safety in critical industries such as energy, aerospace and petroleum. However, traditional methods face significant limitations: they rely heavily on human expertise, are time-consuming and, in some cases, have variability in results.

Today, artificial intelligence (AI) is no longer a future promise, but a transformative reality. It is redefining industrial inspection standards. This article will discuss how the fusion of ultrasound and AI is revolutionizing the industry, what types of welding can benefit, what level of accuracy it offers, and whether or not it can replace the certified inspector.

What is ultrasonic weld inspection with AI?

AI-assisted ultrasonic inspection brings together two potential technologies. On the one hand, traditional ultrasonic testing uses high-frequency sound waves to penetrate materials and detect internal discontinuities. These waves bounce back when a defect is encountered, generating echoes that form the basis of the analysis.

On the other hand, AI incorporates Machine Learning (ML) and Deep Learning algorithms, trained on vast databases containing ultrasonic signals from both defective and correct welds. These models identify patterns that indicate anomalies with increasing accuracy.

For a more technical understanding, see our article on Total Focusing Method (TFM) Ultrasound, one of the most advanced techniques in ultrasound testing.

How does AI ultrasonic inspection work?

AI-assisted ultrasonic inspection follows five essential steps that ensure automated, accurate and repeatable analysis. Below is an infographic with the five key stages of the process:

Process description:

• Data capture: Advanced ultrasonic equipment is used to record A-scan, B-scan and C-scan signals, adjusted for calibration and specific inspection objectives.
• Preprocessing: Signals are filtered, cleaned and organized so that they are interpretable by AI models.
• AI analysis: The system analyzes the signals and detects patterns that could represent defects.
• Defect classification: The AI identifies anomalies, and categorizes them e.g. cracks, inclusions or porosities based on their acoustic signature.
• Visualization and reporting: Results are presented in a clear, quantitative format, with graphs and heat maps showing defect type, location and dimensions. This process does not replace the inspector, it reduces subjectivity and improves efficiency, allowing professionals to focus on critical data-driven decisions.

Ultrasonic inspection applications with AI

What types of welds can be inspected with AI?

AI-enhanced ultrasonic weld inspection is flexible, can be applied to different weld types and materials in various industries, due to its penetration intensity and accuracy. Some of the most important applications include:

• Butt welds: Common in pipes and structures, to detect internal problems such as lack of fusion or porosity.
• Fillet welds: Used to join parts at an angle, where it is used to identify cracks or inclusions.
• Groove welds: Required in heavy constructions, to ensure the integrity of the weld.
• Welds on composite materials: Works as well on carbon steel, as on stainless steel, aluminum and special alloys.

This technology is one of the most widely used in sectors such as:

• Oil and gas: Ensures comprehensive weld inspection on gas pipelines and offshore structures.
• Power generation: Fundamental for safety in nuclear, thermal and hydroelectric plants.
• Manufacturing: Optimizes quality control in the production of critical components.
• Automotive and aerospace: Ensures the reliability of joints in vehicles and aircraft (see figure 1).

Leading companies are actively driving this integration. For example, The Phased Array Company (TPAC) stands out in the Non Destructive Testing (NDT) industry for its development of advanced ultrasonic inspection techniques, and is actively incorporating Artificial Intelligence (AI) to optimize its operations in the Oil & Gas sector.

Its efforts are focused on improving defect detection and real-time data processing, underscoring the growing adoption of AI in critical weld integrity applications. Recently, TPAC presented its advances in Non-destructive Inspection with ultrasonic technology at AMPP 2025.

Check out the full interview with TPAC on ultrasound and AI in inspection from Inspenet Tv with Gavin Dao, TPAC’s Director of Business Development, speaking regarding this technology and its advancements.

How accurate is AI defect detection?

Accuracy is one of the greatest advantages of Machine Learning Non-destructive Testing. AI models, especially those based on Deep Learning, can achieve extraordinary levels of accuracy, due to the potential to learn from huge volumes of data and recognize complex patterns that often escape the human eye. An example of this is how AI is automating industrial inspection and improving accuracy in NDT.

• High detection rate (True Positives): Automatic weld defect detection systems are excellent at identifying the presence of imperfections. This means fewer undetected defects.
• Low False Positive Rate: Although there is always a possibility, advanced models minimize the misidentification of a healthy area as a defect. This reduces the need for unnecessary secondary inspections.
• Detailed classification: AI can differentiate between different types of defects (cracks, porosity, inclusions, lack of penetration). This classification capability helps determine the severity of the defect.
• Consistency: Unlike human inspectors, whose accuracy can be affected by fatigue or experience, AI models maintain consistent performance 24/7.

The accuracy of these models depends directly on the quality and quantity of the training data; the more diverse and representative the data, the more robust and accurate the AI model will be. Standards organizations are working on guidelines to validate these systems, ensuring their reliability in critical applications.

Can AI replace the certified inspector?

This is a key question in industrial inspection automation, where the answer, at least for now and the foreseeable future, is no. AI does not seek to replace the human inspector, but to empower him. AI does not seek to replace the human inspector, but to empower him or her.

The role of the certified inspector is evolving. He is no longer a simple “detective” of defects but a manager and validator of data. AI can:

• Perform repetitive tasks: AI is excellent for continuous scanning and preliminary anomaly identification. This frees up inspector time.
• Process large volumes of data: Ultrasonic weld inspections generate a large amount of data that AI analyzes in fractions of seconds.
• Mitigating operational fatigue: Continuous real-time signal monitoring places a significant cognitive burden on human operators. In contrast, AI systems can process large volumes of data on a sustained basis without fatigue-related performance degradation.

However, the human inspector provides irreplaceable elements:

• Expert judgment: A certified inspector, with years of experience, is trained to interpret unusual anomalies or complex conditions that AI has not been trained to recognize.
• Critical decision-making: The AI suggests, but the human validates and decides on repairs or corrective actions. This involves understanding the operational context, regulations and safety implications.
• Adapting to the unexpected: An inspector can adjust his method or ultrasonic test, in the field, to unforeseen situations, something that pre-programmed algorithms cannot do.
• Development and supervision: The AI needs to be trained and its results validated. The inspector is key in this process.

Ultrasonic inspection assisted by artificial intelligence is a tool of high technological value. Its application optimizes efficiency and accuracy in the evaluation of welds. The synergy between AI algorithms and the experience of the certified inspector represents an advanced model of non-destructive inspection, where AI acts as an analytical assistant and the human operator provides the necessary strategic judgment to ensure the integrity and quality of welded joints. You can also learn about Advances in Inspector Training in Digital Non-destructive Testing and Artificial Intelligence.

The future of welding integrity and the industry

AI in ultrasonic weld inspection is not just an improvement, it is a paradigm shift. Automatic weld defect detection and non-destructive testing with Machine Learning are raising safety and efficiency standards. This is essential for unwavering weld integrity in critical infrastructures.

In Figure 2, a technician or engineer performing a weld inspection in an industrial environment uses measurement and test equipment, which includes a handheld device with a display showing wave graphs (possibly ultrasound) and a tablet presenting a data analysis or defect map.

Recent news, such as Augury securing $75 million to expand its leadership in industrial AI, demonstrates the growth and investment in this sector.

Entrepreneurs, managers and industry professionals must understand this technology as a strategic investment to optimize operations, and position companies at the technological forefront. Standards organizations and industry bodies are working to integrate these innovations, ensuring sustainable and secure growth.

The path to greater automation in inspection is inevitable. The synergy between human knowledge and the power of AI promises a safer and more efficient future for the entire industry. It is an evolution that will benefit operators, engineers and, ultimately, society as a whole.

Case Study: Samalayuca Pipeline Inspection

Applus+ carried out an advanced welding inspection on the Samalayuca – Sasabe Gas Pipeline (Mexico), a 300 km long infrastructure that crosses the states of Sonora and Chihuahua.

To ensure the integrity and safety of the welded joints, advanced non-destructive inspection technologies were employed:

• Automatic Ultrasound (AUT): Using the Rotoscan system, which allows a quick and precise inspection of welds, detecting possible internal defects without the need to disassemble the structures.
• Industrial Gammagraphy (RT) to complement the analysis with detailed radiographic images.

Thanks to this combination of technologies, a complete coverage of inspections was achieved, complying with the most demanding safety and quality standards in the energy sector.

This case demonstrates how the application of advanced inspection technologies, such as Automatic Ultrasound, is crucial to ensure the integrity of critical infrastructure in the oil and gas industry. Applus+ – Weld Inspection of the Samalayuca – Sasabe Gas Pipeline.

Conclusions

Ultrasonic weld inspection with AI is an unavoidable evolution. It seeks not to displace, but to raise the level of industrial inspection. Automatic weld defect detection is now faster and more accurate, the human-AI combination is the true driver of weld integrity.

Automation in inspection is not the end of the job, it is the beginning of an era of greater efficiency and safety. The certified inspector is still fundamental, but now with technological tools that magnify their capabilities. Synergy is key to success.

Modernize your inspection with AI and guarantee flawless welds!

References

1. Case Study: Applus+ – Welding Inspection of the Samalayuca – Sasabe Gas Pipeline
2. TPAC: The Phased Array Company: NDT Specialist (https://thephasedarraycompany.com/)