AUToSCAN-AI LLP
AUToSCAN-AI has developed a platform, NDicateTM, that integrates analysis algorithms for the commonly used NDI methods such as Visual Inspection, Ultrasonic PAUT, TOFD, Thermography, Computed Tomography, and Digital Radiography.
Service
United States
Overview
AUToSCAN-AI has developed a platform, NDicateTM, that integrates analysis algorithms for the commonly used NDI methods such as Visual Inspection, Ultrasonic PAUT, TOFD, Thermography, Computed Tomography, and Digital Radiography. The Platform is capable of processing input signals in the form of images to automatically detect defects and extract relevant attributes such as position and size.
The platform developed utilizes many traditional computer vision concepts and modern machine learning methods, such as Convolutional Neural Networks. The combination of these technologies allows for reliable pattern recognition tasks such as defect detection and classification. As an outcome, fast and accurate reports of defect size, shape, and intensity are generated. In addition the model can learn to solve new problems by training on datasets previously assessed by experts. This ability to learn by example allows NDicateTM to adapt and automate new applications formerly requiring human effort and specific expertise.
Products & Services
Ultrasonic Testing
NDicateTM software platform significantly enhances Phased Array Ultrasonic Testing (PAUT) by automating and improving various aspects of the inspection analysis process, leading to more accurate and consistent weld evaluation.
Quantitative Analysis
The software provides quantitative data related to the size, shape, and location of discontinuities. This data assesses the severity of found indications and makes informed decisions about the acceptance of the welds.
Automated Data Analysis
The developed algorithms analyze the large amount of data generated during PAUT more quickly and accurately than traditional approaches. This leads to faster and more reliable detection of flaws.
Automated Reporting
NDicateTM automates the generation of inspection reports by extracting key information from ultrasonic data analysis. This saves time for inspectors and ensures that reports are consistent and comprehensive.
Defect Recognition and Classification
NDicateTM platform can be trained to recognize and classify different types of defects in the welds. This includes crack-like and planar types of discontinuities such as lack of fusion, incomplete penetration, or slag inclusions. By automating defect recognition, NDicate improves the consistency of inspections and reduces the risk of human error.
Digital Radiography
The NDicateTM platform brings several advantages to radiography inspection. NDicate software can be applied to enhance radiography inspection in various ways.
Defect Detection
It detects and classifies defects in radiographic images. This includes identifying cracks, voids, inclusions, and other weld anomalies. Automated defect detection improves the speed and accuracy of inspections
Automated Image Analysis
It automates the analysis of radiographic images, allowing for quicker and more consistent evaluations. This can include measurements of dimensions, identification of specific features, and the extraction of relevant information from the images.
Anomaly Recognition
It recognizes patterns associated with different types of anomalies. This includes variations in density, shape, or size that may indicate defects
Computed Radiography
Automated Image Segmentation
NDicateTM automates the segmentation in CT images. This facilitates quantitative analysis, area measurements, and the identification of abnormalities by providing more accurate and consistent measurements
Anomalies Detection and Classification
This assists operators in identifying potential areas of concern more quickly and accurately, improving the overall efficiency of the inspection process
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