ANYmal Closes the Remote Inspection Loop with Aker BP and Cognite
Aker BP, Cognite, and ANYbotics partner in pioneering offshore remote inspections with ANYmal X on the Valhall platform in the North Sea. ANYmal X, the only Ex-certified legged robot, was tested for integrated robotic inspections in offshore Ex-rated zones, showcasing the benefits of Cognite’s real-time digital twins and comprehensive AI-powered data platform. This is a significant step in Aker BP’s aim to implement remote inspections as an enabler for unmanned operation of complex offshore processing platforms by 2027-2029.
The Future of Oil and Gas Inspection Software
The very nature of oil and gas operations makes assets susceptible to corrosion. Regular inspections help detect early signs of corrosion, thereby preventing potential leaks or failures. Modern technologies, such as drones and visual AI, have revolutionized this aspect, allowing for more detailed, quicker, and safer inspections.
Optelos stands out as a quintessential example of this type, merging the capabilities of the aforementioned software types into one cohesive solution. From managing visual data from UAVs to operationalizing visual AI for corrosion inspections and creating 3D digital twins, integrated platforms provide a holistic approach to oil and gas inspections.
Interesting Engineering on UVeye – The MRI for Cars
✈️ Korean Air Makes Progress On Drone Swarm Inspections
Korean Air is making progress on its novel approach to drone-based aircraft inspections, which uses a swarm of drones to further reduce inspection time and ensure complete coverage even if one drone malfunctions. Since demonstrating the drone swarms in late 2021, the airline has refined the technology and received government support to further development.
The airline’s drone swarm approach uses the latest drone enhancements, such as pre-set inspection plans, geofencing to keep drones in restricted areas, a collision avoidance system and artificial intelligence (AI). The drones are made locally by a Korean manufacturer. AI will enable the drones to detect various defects such as dents and cracks.
Reality Show: X-ray Vision Can See Through Metal
A typical aircraft maintenance inspection involves maintenance technicians and engineers walking around an aircraft recording new defects and damage with a pencil in a notebook. Locations are often described in language like ‘3 inches from the left side of the window.’ The inspection can often take hours or days. But what if you could hold a digital device and see locations of all previous damage and repairs highlighted in 3D?
AI Driven Vision Inspection Automation for Forged Connecting Rods
An Effort Towards Reducing Industrial Textile Waste
Textiles include various types of materials made from natural and synthetic fibers. To ensure the finished products are defect-free, inspecting the fibers during the production process is important. This also can result in a 45% to 60% savings on the total expenditure due to wastage or recalling defective products.
Line scan cameras are widely used to detect defects in the textile industry. These use single pixel lines for the construction of continuous 2D images as the materials pass through the production line. The cameras can capture superior quality images of various types of materials, which help in detecting any pattern changes without any breaks. Additionally, these cameras can notify operators about any changes in color and texture.
Smart Devices, Smart Manufacturing: Pegatron Taps AI, Digital Twins
Today, Pegatron uses Cambrian, an AI platform it built for automated inspection, deployed in most of its factories. It maintains hundreds of AI models, trained and running in production on NVIDIA GPUs. Pegatron’s system uses NVIDIA A100 Tensor Core GPUs to deploy AI models up to 50x faster than when it trained them on workstations, cutting weeks of work down to a few hours. Pegatron uses NVIDIA Triton Inference Server, open-source software that helps deploy, run and scale AI models across all types of processors, and frameworks.
Taking another step in smarter manufacturing, Pegatron is piloting NVIDIA Omniverse, a platform for developing digital twins “In my opinion, the greatest impact will come from building a full virtual factory so we can try out things like new ways to route products through the plant,” he said. “When you just build it out without a simulation first, your mistakes are very costly.”
Inspection of Tapered Rollers for a Global Bearings Manufacturer
It was decided to use a Deep Learning AI powered inspection technique since the defects were qualitative and across a wide range of roller SKUs. The key steps followed in this workflow consisted of image collection, image annotation, Deep Learning model selection/training, deriving an optimized Edge inference model, deployment on the production floor and, finally, maintenance.
Qualitas worked collaboratively with the customer to collect and annotate a sufficient number of good (G) and not-good (NG) images of the tapered rollers, showing both the cylindrical and larger flat surfaces. A few hundred images were thus collected and processed. This image data was used to train the chosen Deep Learning AI model iteratively till acceptable performance was achieved. A key consideration was to keep false positive and false negative predictions sufficiently low across the wide variety of SKUs for a range of subjective surface defects.
Pleora’s Visual Inspection System Ensures End-to-End Quality for DICA Electronics
DICA Electronics Ltd is deploying Pleora’s Visual Inspection System to reduce manufacturing quality escapes and gather key data from manual processes to help speed root cause analysis. The system uniquely requires just one image to start using AI, with continuous and transparent training based on operator actions to improve and speed automated decision support. With just one good image, Inspection apps for incoming, in-process, and final quality control steps automatically compare products to a “golden reference” and visually highlight differences and deviations for an operator. As the operator accepts or rejects potential errors, the AI model is transparently trained based on their decisions. After even just one inspection, the AI model will start automatically suggesting a decision for the operator. Over time, the speed and accuracy of automated decision-making will improve as the system continuously learns from operator preferences. In comparison, most AI inspection tools require numerous good and bad images plus time-consuming and expensive algorithm development before they can be deployed in production.
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.
Startup’s Vision AI Software Trains Itself — in One Hour — to Detect Manufacturing Defects in Real Time
NVIDIA Metropolis member Covision creates GPU-accelerated software that reduces false-negative rates for defect detection in manufacturing by up to 90% compared with traditional methods. In addition to identifying defective pieces at production lines, Covision software offers a management panel that displays AI-based data analyses of improvements in a production site’s quality of outputs over time — and more.
“It can show, for example, which site out of a company’s many across the world is producing the best metal pieces with the highest production-line uptime, or which production line within a factory needs attention at a given moment,” Tschimben said.
An implementation of YOLO-family algorithms in classifying the product quality for the acrylonitrile butadiene styrene metallization
In the traditional electroplating industry of Acrylonitrile Butadiene Styrene (ABS), quality control inspection of the product surface is usually performed with the naked eye. However, these defects on the surface of electroplated products are minor and easily ignored under reflective conditions. If the number of defectiveness and samples is too large, manual inspection will be challenging and time-consuming. We innovatively applied additive manufacturing (AM) to design and assemble an automatic optical inspection (AOI) system with the latest progress of artificial intelligence. The system can identify defects on the reflective surface of the plated product. Based on the deep learning framework from You Only Look Once (YOLO), we successfully started the neural network model on graphics processing unit (GPU) using the family of YOLO algorithms: from v2 to v5. Finally, our efforts showed an accuracy rate over an average of 70 percentage for detecting real-time video data in production lines. We also compare the classification performance among various YOLO algorithms. Our visual inspection efforts significantly reduce the labor cost of visual inspection in the electroplating industry and show its vision in smart manufacturing.
Industry 4.0 and the pursuit of resiliency
There are two parts to the Zero D story. Visual inspection and asset performance management (APM). Visual inspection uses computer vision models focused on quality inspection. APM uses machine learning models based on time series data to determine health of assets and probable failures in the future. Toyota is using Maximo Visual Inspection, and now they are also using the Maximo Asset Performance Management (APM) suite. They tested Maximo APM on some of their machinery that does liquid cooling and found that was another problem area for them. By implementing the software into this pilot, they are now able to monitor the asset health 24×7 and predict probability of failure in the future.
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.”
Applying Artificial Intelligence to Food Tray Production
Neurala, a supplier of AI (artificial intelligence)-based visual inspection technology, began working with apetito to detect cases of the five most reported missing components from meal trays using Neurala’s Vision Inspection Automation (VIA) software. VIA consists of two software programs, Inspector and Brain Builder. Using these programs, apetito was able to build anomaly-detecting systems in 10-20 minutes and immediately begin testing.
With apetito’s earlier weight-based inspection system, the company could only flag an incomplete tray, without understanding what was missing. With VIA’s ability to inspect multiple regions of interest on the trays, apetito can now see specifically which components are missing and identify trends in missing components to avoid their occurence in the future.
Ford presents its prestigious IT Innovation Award to IBM
The Maximo Visual Inspection platform can help reduce defects and downtime, as well as enable quick action and issue resolution. Ford deployed the solution at several plants and embedded it into multiple inspection points per plant. The goal was to help detect and correct automobile body defects during the production process. These defects are often hard to spot and represent risks to customer satisfaction.
Although computer vision for quality has been around for 30 years, the lightweight and portable nature of our solution — which is based on a standard iPhone and makes use of readily available hardware — really got Ford’s attention. Any of their employees can use the solution, anywhere, even while objects are in motion.
Ford found the system easy to train and deploy, without needing data scientists. The system learned quickly from images of acceptable and defective work, so it was up and running within weeks, and the implementation costs were lower than most alternatives. The ability to deliver AI-enabled automation using an intuitive process, in their plants, with approachable technology, will allow Ford to scale out rapidly to other facilities. Ford immediately saw measurable success in the reduction of defects.