QualiSense

What will manufacturing facilities look like in 2044? The landscape of manufacturing will dramatically change with the rise of collaborative robots, predictive maintenance, digital twins, automated warehouses, and AI-driven visual inspection. For instance, collaborative robots, already seen in companies like Nissan Motor Corporation and AUDI AG, will increasingly handle complex tasks, while predictive maintenance will shift factories from reactive to proactive machinery upkeep. Digital twins will optimise processes virtually before implementation, and automated warehouses will streamline storage and distribution, exemplified by Amazon's fulfilment centres. AI visual inspection will revolutionise quality control with faster, more accurate defect detection. So what? These advancements will lead to unprecedented efficiency, safety and precision in manufacturing, while also redefining the role of human workers as overseers in a highly automated environment. This is augmented AI. Read more about the future of manufacturing on our blog. #AI #Manufacturing #Innovation #Robotics #Industry40

What might the future of predictive maintenance look like? We think that AI and Big Data will revolutionise maintenance practices and product design. Predictive maintenance, such as using sensor data to anticipate when maintenance is needed, is already reducing costs and optimising schedules. Companies like Augury exemplify this innovation by using sensor data to predict machine health. And it’s possible to go further. By integrating quality inspection data with predictive maintenance, we can improve product quality and inform design choices. This approach requires vast data but offers the potential to prevent defects and optimise production processes. For a demonstration of how data-driven insights can transform manufacturing, visit qualisense.ai/technology #AI #PredictiveMaintenance #BigData #Industry40 #ManufacturingInnovation

Thanks Connectivity 4IR for featuring our article! How important is domain-specific data for AI applications in quality inspection? While general datasets are useful, industrial applications like quality inspection demand proprietary data from a production environment. This data is often difficult to access due to companies' reluctance to share proprietary information. Strategic partnerships, like our partnership with Johnson Electric, enable access to essential domain-specific data, enhancing the accuracy and efficiency of AI models in manufacturing. Read the full article about this challenge and its solutions here: https://lnkd.in/exE-eYMt #AI #QualityInspection #Manufacturing #DataScience #Innovation

Did you see Zohar Kantor’s article in i4.0 Today ? Investing in AI for quality inspection can transform manufacturing processes. In the article, Zohar discusses how companies can enhance their production lines by integrating AI visual inspection systems early in the process, beyond the traditional "firewall strategy". While the firewall strategy—deploying AI at the end of the production line—prevents defective parts from leaving the plant, implementing AI earlier in the process can reduce material waste and optimise production. This proactive approach identifies defects at the source, enabling quality managers to address and prevent recurring issues, significantly improving overall efficiency. For industries like automotive and aerospace, where the cost of defects is high, such strategic implementation of AI not only enhances product quality but also delivers substantial cost savings. Learn more about the advantages of early AI integration in quality inspection in the full article here: https://lnkd.in/eABg8Tyx #AI #QualityInspection #Manufacturing #Innovation 

The use of AI in visual inspection is advancing rapidly, but variability in production environments presents a significant challenge. Even minor changes in lighting, shadows, or conveyor setups can affect AI model performance. Compared to humans, AI struggles more with these variations, making it a critical hurdle to overcome. Proper pre-processing and data augmentation techniques are essential for training AI models to handle this variability. By simulating worst-case scenarios and introducing image noise during training, AI models become more resilient and effective in real-world applications. Addressing these challenges is critical to near-zero error rates in defect detection. #AI #VisualInspection #QualityControl

Thanks to Plant & Works Engineering Magazine for featuring our article on their front cover! Data and AI are both hot topics right now. Miron Shtiglitz asks what will happen with all the data gathered by AI inspection systems? While it might first be used to optimise processes, Miron forecasts a future where this data might also be used to shape the design of the product itself. #Quality #BigData #AI

Cutting-edge augmented AI is helping to enhance quality assurance in manufacturing. Manual quality control tends to have an error rate of ten per cent, leading to costly issues down the line. Automating the process with AI-powered machine vison significantly reduces errors. But using AI in manufacturing doesn’t mean humans will be unnecessary. Augmented AI technology is enhanced by quality managers, using their expertise to streamline the AI learning process, creating scalable models without extensive data preparation. This ensures fast, secure and easy deployment that’s tailored to specific production needs and can adapt and change as time passes. For a video on how the system works, visit www.QualiSense.ai #AI #QualityAssurance #MachineVision

AI for visual inspection is making strides in industries like automotive, metals and machinery manufacturing. Using AI and machine vision can improve defect detection rates by an order of magnitude compared to manual checks by humans alone. But reaching the next goal of near-zero error rates in defect detection is a major challenge. One hurdle is balancing image quality. High-resolution images are essential for accurate feature extraction, which in turn improves defect detection and classification. However, excessive resolution can slow down processing speeds. And factors like colour depth, contrast and sharpness all play vital roles in distinguishing defects from normal features. Defining the right balance in these elements is key to optimising AI models for production environments. #AI #Manufacturing #QualityControl 

Thanks Metrology News for featuring our article! Our VP for product and delivery, Miron Shtiglitz, shares how active learning is transforming AI models for quality inspection, making them more efficient and accurate. Active learning is a game-changer. Instead of using random data, it focusses on the most informative samples, which means higher accuracy with fewer labelled examples. This is crucial in defect detection, where labelling data can be costly and time-consuming. What does this mean for the industry? Implementation of active learning in quality inspection means more efficient, cost-effective, and robust AI models. This advancement is set to enhance production standards and reduce waste. Read the article here: https://bit.ly/3UHN57i #AI #MachineLearning #QualityInspection #Manufacturing

Have you seen the latest issue of Control Engineering Europe? Zohar Kantor explains how companies are currently using AI inspection systems. This involves the “firewall strategy” which looks to catch defects at the end of the production line. However, Zohar argues that in the long run many smart manufacturers will decide to install AI inspection systems at earlier stages of the production process. By spotting defects earlier, AI will increase yield as well as improving quality. Make sure you pick up the magazine to see the full article: https://lnkd.in/e_q_3nRE #AugmentedAI #DefectDetection #Manufacturing #ArtificialIntelligence