SKF Group’s Post

🔦 AI meets roller #bearings! 📸 SKF Group shows how #AI-supported image recognition is revolutionizing human inspection. Even in real and uncontrolled conditions, the system delivers impressive results and detects damage with precision. A look into the #future of bearing #maintenance! 🛠

Can computer vision be implemented into existing manufacturing processes? The answer is yes. In the video there is a simplified diagram of how computer vision works in recognizing defects on objects passing on the conveyor belt. How does it work? 1. The camera records objects passing on the conveyor belt 2. Objects pass on the conveyor belt without slowing down, even at high speeds 3. If there are defects in the object, the software identifies them 4. Send the signal to carry out the action which can be a simple signal on the management system or the activation of gates, actuators, or robotic arm for removal If you want to learn more watch https://lnkd.in/eHjRqbiP and let me know what you think or how you would implement it. #industry40 #manufacturing #computervision #ai #opencv

The Future of Quality Control with AI-Powered Defect Detection Despite potential complications, consumers rarely see the manufacturing process of the products they consume, but they're always able to spot when there is a defect in those products. The reality of modern manufacturing is unexpected errors are repeated more than most recognize, and often, companies are not aware of critical defects in products until it is too late. Manufacturers can eliminate these errors with the proper development of real-time defect detection of both known and unanticipated issues on manufacturing lines. Here we discuss how artificial intelligence solutions can be used for manufacturing defect detection. How AI used in Defect Detection? There are tons of solutions for defect detection in manufacturing using cameras for numerous applications, but AI and image processing go beyond the camera. Image processing assembles all image data into a cloud database where tens can evaluate it to hundreds of machine learning algorithms to pinpoint defects or fluctuations and provide course-correcting measures. Results are analyzed to provide risk assessments of the line's process and the overall program. By the time a product is ready for production, all potential kinks are worked out. Once the product is in production, the AI-powered defect detection process continuously monitors the production line to divert any additional defective products to the correct location to be fixed. #maintainance #AI #laser #innovation #qualitycontrol #technique #machinelearning #surface #vision

We are excited to share our latest project, which is an #AI and #ML based application that can identify the presence or absence of a copper washer during the assembly of a differential gearbox in a large #automotive #manufacturing plant. This is a crucial step in ensuring the quality and efficiency of the production process, as a missing washer can cause serious problems and delays in the later stages of the assembly. The application consists of three main components: a machine vision camera, a trigger/push button, and a large monitor. The camera is mounted on a fixed position above the assembly line, where it can capture images of the differential gearbox as it being assemble on the table below. The trigger/push button is used to start the camera and initiate the image analysis. The large monitor displays the final result of the analysis, which is either "washer present" or "washer absent", along with a confidence score. The application uses a deep learning model that has been trained on thousands of images of differential gearboxes with and without washers. The model can detect the washer with high accuracy and speed, even in challenging lighting and background conditions. The application also has a user-friendly interface that allows the operator to view the image history and export the analysis results. This project demonstrates the power and potential of AI and ML in industrial applications, where they can improve the quality, safety, and productivity of manufacturing processes. #VisionSystem, #ComputerVision #machinelearning #deeplearning, #Poka-Yoke

Discover the latest AI-driven productivity improvements in Solid Edge 2024, designed to supercharge your productivity, allowing you to work smarter with your design data and elevating product development. Explore real-world examples, from the Supfina RACENEO automated super-finishing machine to precise assembly techniques and AI-powered component replacement. Let Solid Edge replace parts in an assembly for you automatically and intelligently with new artificial intelligence (AI) assembly relationships. Additionally, many of your everyday workflows are streamlined and improved with new commands and user-driven enhancements. Learn more about Solid Edge 2024: https://oal.lu/dTNiC #SolidEdge2024 #CAD #AI

SACMI investigates the invisible: Quality Control with AI and Polarized Light At SACMI we like to experiment with the power of technology. Above all, we like to explore the boundaries of next-gen tech: an exploration that can reveal unexpected opportunities. Our PVS inspection systems - a seamless combination of polarized light and AI - offer a perfect example. Initially designed to detect abnormal stress in plastic preforms, these systems have demonstrated much greater potential. 🕵️♂️ 👀 With standard lighting, preforms might look identical. But with the addition of polarized light, a whole new world opens up: even the slightest discontinuity in the barrier layer becomes visible, revealing defects that previously escaped the human eye! 💡 Wondering whether invisible defects in your preform production might be escaping detection? Automatic inspection with polarized light and AI could be the key to finding them. ✅ Our PVS2, PVS3 and PVS10 systems are all equipped with these innovative features. Ready to transform your quality control? 💬 Contact us so we can find out, together 👉 https://lnkd.in/ej6PhPGr #SACMI #visionsystems #AI #preforms 

Integration of AI into telematic devices for monitoring CPCB IV+ diesel generator is Possible ? How it's beneficial to the customers? AI can be implemented in Predictive Maintenance: AI algorithms can analyze historical data from the Genset to predict potential failures before they occur. This involves: Data Collection: Continuously collecting data on engine parameters such as temperature, pressure, vibration, and fuel consumption. Pattern Recognition: Using machine learning models to recognize patterns associated with component wear and tear or operational issues. Anomaly Detection: Identifying deviations from normal operating conditions that could indicate impending failures. Maintenance Scheduling: Suggesting optimal times for maintenance based on predicted needs, reducing downtime and maintenance costs.

𝐋𝐀𝐓𝐓𝐄𝟑𝐃: 𝐋𝐚𝐫𝐠𝐞-𝐬𝐜𝐚𝐥𝐞 𝐀𝐦𝐨𝐫𝐭𝐢𝐳𝐞𝐝 𝐓𝐞𝐱𝐭-𝐓𝐨-𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝𝟑𝐃 𝐒𝐲𝐧𝐭𝐡𝐞𝐬𝐢𝐬 Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming optimization that can take up to an hour per prompt. Amortized methods like ATT3D optimize multiple prompts simultaneously to improve efficiency, enabling fast text-to-3D synthesis. However, they cannot capture high-frequency geometry and texture details and struggle to scale to large prompt sets, so they generalize poorly. We introduce #LATTE3D, addressing these limitations to achieve fast, high-quality generation on a significantly larger prompt set.  👉 Paper https://lnkd.in/dew2i_KC #ai #genai NVIDIA ♻️𝑰𝒇 𝒚𝒐𝒖 𝒇𝒐𝒖𝒏𝒅 𝒕𝒉𝒊𝒔 𝒉𝒆𝒍𝒑𝒇𝒖𝒍, 𝒌𝒊𝒏𝒅𝒍𝒚 𝒓𝒆𝒑𝒐𝒔𝒕♻️

🚘✨ 𝗤𝘂𝗶𝗰𝗸 𝗜𝗻𝘀𝗶𝗴𝗵𝘁: 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗮𝗺𝗮𝗴𝗲 𝗘𝘀𝘁𝗶𝗺𝗮𝘁𝗶𝗼𝗻 Automotive repairs are entering a new phase with Vision AI and Collision Estimatics at the helm. Together, they're changing the game by making damage assessments and repair estimates faster and much more precise. It's a big move away from the old ways of doing things, bringing tech smarts directly into the repair process. 🎥 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗶𝗻 𝗢𝘂𝗿 𝗩𝗶𝗱𝗲𝗼: 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻: Follow Alex's journey, showcasing how AI elevates damage assessments to be more precise. 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Delve into AI's role in capturing and analyzing detailed images for unmatched accuracy. 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Discover how technician expertise complements AI insights to craft flawless estimates. 👀 Discover how Addenda Vision and Quote combine technology with technician insight for unbeatable accuracy.

Problem Statement In air filter manufacturing, ensuring the quality of the product is crucial. One critical aspect is accurately measuring the filament hold count, which directly impacts the filter's effectiveness. However, the current manual inspection process is time-consuming, prone to errors, and lacks efficiency. Current Procedure Currently, the filament hold count of air filters is determined through manual inspection. Operators visually inspect the filters, counting the number of filaments held within the filter material. This method is labor-intensive, subjective, and can lead to inconsistencies in measurement due to human error. Our Solution To address these challenges, we propose an AI-based inspection system for measuring the filament hold count of air filters. This system utilizes computer vision and machine learning techniques to automate the inspection process and improve accuracy For any query or requirement please mail at 👉 sales@cvit.in To know more about our application please visit our website 👉 www.cvit.in Kunal Wankhede I Nakul Ingole I Sanket Bhosale #AI #qualitycontrol #manufacturinginnovation #computervision #machinelearning #automation #deeplearning #industry40