Industrial CT Scanning: Automated Defect Detection for Turbine Blades | Synopsys
Visual Anomaly Detection: Opportunities and Challenges
Clarifai is pleased to announce pre-GA product offering of PatchCore-based visual anomaly detection model, as part of our visual inspection solution package for manufacturing which also consists of various purpose-built visual detection and segmentation models, custom workflows and reference application templates.
Users only need a few hundred images of normal examples for training, and ~10 anomalous examples for each category for calibration & testing only, especially with more homogeneous background and more focused region-of-interest.
Smart Manufacturing at Audi
Some 5,300 spot welds are required to join the parts that make up the body of an Audi A6. Until recently, production staff used ultrasound to manually monitor the quality of spot welds based on random sampling. Now, however, engineers are testing a smarter way of determining weld quality. They are using AI software to detect quality anomalies automatically in real time. The robots collect data on current flow and voltage on every weld. An AI algorithm continuously checks that those values fall within predetermined standards. Engineers monitor the weld data on a dashboard. If a fault is detected, they can then perform manual checks.
Why AI software companies are betting on small data to spot manufacturing defects
The deep-learning algorithms that have come to dominate many of the technologies consumers and businesspeople interact with today are trained and improved by ingesting huge quantities of data. But because product defects show up so rarely, most manufacturers don’t have millions, thousands or even hundreds of examples of a particular type of flaw they need to watch out for. In some cases, they might only have 20 or 30 photos of a windshield chip or small pipe fracture, for example.
Because labeling inconsistencies can trip up deep-learning models, Landing AI aims to alleviate the confusion. The company’s software has features that help isolate inconsistencies and assist teams of inspectors in coming to agreement on taxonomy. “The inconsistencies in labels are pervasive,” said Ng. “A lot of these problems are fundamentally ambiguous.”
Mariner Speeds Up Manufacturing Workflows With AI-Based Visual Inspection
Traditional machine vision systems installed in factories have difficulty discerning between true defects — like a stain in fabric or a chip in glass — and false positives, like lint or a water droplet that can be easily wiped away.
Spyglass Visual Inspection, or SVI, helps manufacturers detect the defects they couldn’t see before. SVI uses AI software and NVIDIA hardware connected to camera systems that provide real-time inspection of pieces on production lines, identify potential issues and determine whether they are true material defects — in just a millisecond.
In situ infrared temperature sensing for real-time defect detection in additive manufacturing
Melt pool temperature is a critical parameter for the majority of additive manufacturing processes. Monitoring of the melt pool temperature can facilitate the real-time detection of various printing defects such as voids, over-extrusion, filament breakage, clogged nozzle, etc. that occur either naturally or as the result of malicious hacking activity. This study uses an in situ, multi-sensor approach for monitoring melt pool temperature in which non-contact infrared temperature sensors with customized field of view move along with the extruder of a fused deposition modeling-based printer and sense melt pool temperature from a very short working distance regardless of its X-Y translational movements. A statistical method for defect detection is developed and utilized to identify temperature deviations caused by intentionally implemented defects.
Fabs Drive Deeper Into Machine Learning
For the past couple decades, semiconductor manufacturers have relied on computer vision, which is one of the earliest applications of machine learning in semiconductor manufacturing. Referred to as Automated Optical Inspection (AOI), these systems use signal processing algorithms to identify macro and micro physical deformations.
Defect detection provides a feedback loop for fab processing steps. Wafer test results produce bin maps (good or bad die), which also can be analyzed as images. Their data granularity is significantly larger than the pixelated data from an optical inspection tool. Yet test results from wafer maps can match the splatters generated during lithography and scratches produced from handling that AOI systems can miss. Thus, wafer test maps give useful feedback to the fab.
AI tool locates and classifies defects in wind turbine blades
Using image enhancement, augmentation methods and the Mask R-CNN deep learning algorithm, the system analyses images, highlights defect areas and labels them.
After developing the system, the researchers tested it by inputting 223 new images. The proposed tool is said to have achieved around 85 per cent test accuracy for the task of recognising and classifying wind turbine blade defects.
Automated Defect Detection (complete pipeline and demo)
Quality Check (QC) is an integral part of each manufacturing process. Every serious manufacturing team performs multiple quality checks both during and at the end of the production process. The good news is that, recent advances in Artificial Intelligence (AI) made a lot of visual inspection tasks possible to automate. Now, AI models can even surpass human performance in some visual inspection problems.