How machine vision works in RIBE Anlagentechnik’s camera-monitored assembly facility
The German company RIBE Anlagentechnik develops innovative assembly systems, including inspection systems, for bumpers. SICK’s machine vision helps to identify the individual components, and it also monitors each work operation. This particular system concept could prove revolutionary for other manufacturers and suppliers as well.
As the level of individualization in production areas increases, so does the importance of special-purpose systems with innovative potential. RIBE Anlagentechnik specializes in delivering added value to its end customers. The company has demonstrated its specific strengths in technologies associated with assembly and inspection systems for vehicle interiors/exteriors and related components. Managing Director Dietmar Heckel regards the cobot and robot technologies with innovative Industry 4.0 solutions and digitalization concepts not only as a supporting pillar of RIBE Anlagentechnik, but also as a cross-sectoral growth field.
Where Four-Legged Robot Dogs Are Finding Work
High-Performance Machine Vision: Versatile lighting for subtle surface 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.
3D Vision Technology Advances to Keep Pace With Bin Picking Challenges
When a bin has one type of object with a fixed shape, bin picking is straightforward, as CAD models can easily recognize and localize individual items. But randomly positioned objects can overlap or become entangled, presenting one of the greatest challenges in bin picking. Identifying objects with varying shapes, sizes, colors, and materials poses an even larger challenge, but by deploying deep learning algorithms, it is possible to find and match objects that do not conform to one single geometrical description but belong to a general class defined by examples, according to Andrea Pufflerova, Public Relations Specialist at Photoneo.
“A well-trained convolutional neural network (CNN) can recognize and classify mixed and new types of objects that it has never come across before,”
Vision Cameras Inspect Disk Drive Assemblies
Once manufactured, an HDD is carefully fitted and sealed in a metal or plastic case. The case ensures that all drive components are perfectly secured in place and their mechanics work well over the lifetime of the product. It also protects the sensitive disks from dust, humidity, shock and vibration.
An HDD case must be defect-free and have perfectly machined thread holes to perform these functions, according to Somporn Kornwong, a manager at Flexon. In 2019 his company developed Visual Machine Inspection (VMI) for a manufacturer so it can quickly and thoroughly inspect each case it produces.
Simplify Deep Learning Systems with Optimized Machine Vision Lighting
Deep learning cannot compensate for or replace quality lighting. This experiment’s results would hold true over a wide variety of machine vision applications. Poor lighting configurations will result in poor feature extraction and increased defect detection confusion (false positives).
Several rigorous studies show that classification accuracy reduces with image quality distortions such as blur and noise. In general, while deep neural networks perform better than or on par with humans on quality images, a network’s performance is much lower than a human’s when using distorted images. Lighting improves input data, which greatly increases the ability of deep neural network systems to compare and classify images for machine vision applications. Smart lighting — geometry, pattern, wavelength, filters, and more — will continue to drive and produce the best results for machine vision applications with traditional or deep learning systems.
Tilling AI: Startup Digs into Autonomous Electric Tractors for Organics
Ztractor offers tractors that can be configured to work on 135 different types of crops. They rely on the NVIDIA Jetson edge AI platform for computer vision tasks to help farms improve plant conditions, increase crop yields and achieve higher efficiency.
AI Vision for Monitoring Applications in Manufacturing and Industrial Environments
In traditional industrial and manufacturing environments, monitoring worker safety, enhancing operator efficiency, and improving quality assurance were physical tasks. Today, AI-enabled machine vision technologies replace many of these inefficient, labor-intensive operations for greater reliability, safety, and efficiency. This article explores how, by deploying AI smart cameras, further performance improvements are possible since the data used to empower AI machine vision comes from the camera itself.
Tools Move up the Value Chain to Take the Mystery Out of Vision AI
Intel DevCloud for the Edge and Edge Impulse offer cloud-based platforms that take most of the pain points away with easy access to the latest tools and software. While Xilinx and others have started offering complete systems-on-module with production-ready applications that can be deployed with tools at a higher level of abstraction, removing the need for some of the more specialist skills.
How the USPS Is Finding Lost Packages More Quickly Using AI Technology from Nvidia
In one of its latest technology innovations, the USPS got AI help from Nvidia to fix a problem that has long confounded existing processes – how to better track packages that get lost within the USPS system so they can be found in hours instead of in several days. In the past, it took eight to 10 people several days to locate and recover lost packages within USPS facilities. Now it is done by one or two people in a couple hours using AI.
Hyperspectral imaging aids precision farming
Remote sensing techniques have exponentially evolved thanks to technological progress with the spread of multispectral cameras. Hyperspectral imaging is the capture and processing of an image at a very high number of wavelengths. While multispectral imaging can evaluate the process with three or four colors (red, green, blue and near infrared), hyperspectral imaging splits the image into tens or hundreds of colors. By using the technique of spectroscopy, which is used to identify materials based on how light behaves when it hits a subject, hyperspectral imaging obtains more spectra of data for each pixel in the image of a scene.
Unlike radiography, hyperspectral imaging is a non-destructive, non-contact technology that can be used without damaging the object being analyzed. For example, a drone with a hyperspectral camera can detect plant diseases, weeds, soil erosion problems, and can also estimate crop yields.
John Deere and Audi Apply Intel’s AI Technology
Identifying defects in welds is a common quality control process in manufacturing. To make these inspections more accurate, John Deere is applying computer vision, coupled with Intel’s AI technology, to automatically spot common defects in the automated welding process used in its manufacturing facilities.
At Audi, automated welding applications range from spot welding to riveting. The widespread automation in Audi factories is part of the company’s goal of creating Industrie 4.0-level smart factories. A key aspect of this goal involves Audi’s recognition that creating customized hardware and software to handle individual use cases is not preferrable. Instead, the company focuses on developing scalable and flexible platforms that allow them to more broadly apply advanced digital capabilities such as data analytics, machine learning, and edge computing.
F-16s Are Now Getting Washed By Robots
The Wilder Systems solution actually leverages technology previously developed for robotic drilling in commercial aircraft manufacturing and converts these components and subsystems into an automated washing system. The main changes have involved the development and addition of robot end-effectors to provide the water and soap spray, waterproofing of the robots themselves, and a robot motion path, which is dependent on the type of aircraft to be cleaned.
Machine learning optimizes real-time inspection of instant noodle packaging
During the production process there are various factors that can potentially lead to the seasoning sachets slipping between two noodle blocks and being cut open by the cutting machine or being packed separately in two packets side by side. Such defective products would result in consumer complaints and damage to the company’s reputation, for which reason delivery of such products to dealers should be reduced as far as possible. Since the machine type upgraded by Tianjin FengYu already produced with a very low error rate before, another aspect of quality control is critical: It must be ensured that only the defective and not the defect-free products are reliably sorted out.
Tractor Maker John Deere Using AI on Assembly Lines to Discover and Fix Hidden Defective Welds
John Deere performs gas metal arc welding at 52 factories where its machines are built around the world, and it has proven difficult to find defects in automated welds using manual inspections, according to the company.
That’s where the successful pilot program between Intel and John Deere has been making a difference, using AI and computer vision from Intel to “see” welding issues and get things back on track to keep John Deere’s pilot assembly line humming along.
Harvesting AI: Startup’s Weed Recognition for Herbicides Grows Yield for Farmers
In 2016, the former dorm-mates at École Nationale Supérieure d’Arts et Métiers, in Paris, founded Bilberry. The company today develops weed recognition powered by the NVIDIA Jetson edge AI platform for precision application of herbicides at corn and wheat farms, offering as much as a 92 percent reduction in herbicide usage.
Driven by advances in AI and pressures on farmers to reduce their use of herbicides, weed recognition is starting to see its day in the sun.
Analysing fruit data in the supply chain has never been more important for business efficiency
Fruit and production data can be used in ways that it has never been done before to improve a company’s efficiency and boost profits, according to global packhouse equipment and automation supplier Tomra Food.
He added that there are several different useful data types at play in a packhouse; production and traceability level data, performance level data, quality data and auditing data. This data can be used to optimise the supply chain and can be used to make decisions and directions in terms of the next big thing that needs to be done. But consumer trends will constantly change the requirements of automation.