World’s Leading Electronics Manufacturers Adopt NVIDIA Generative AI and Omniverse to Digitalize State-of-the-Art Factories
More than 50 manufacturing giants and industrial automation providers — including Foxconn Industrial Internet, Pegatron, Quanta, Siemens and Wistron — are implementing Metropolis for Factories, NVIDIA founder and CEO Jensen Huang announced during his keynote address at the COMPUTEX technology conference in Taipei.
Supported by an expansive partner network, the workflow helps manufacturers plan, build, operate and optimize their factories with an array of NVIDIA technologies. These include NVIDIA Omniverse™, which connects top computer-aided design apps, as well as APIs and cutting-edge frameworks for generative AI; the NVIDIA Isaac Sim™ application for simulating and testing robots; and the NVIDIA Metropolis vision AI framework, now enabled for automated optical inspection. NVIDIA Metropolis for Factories is a collection of factory automation workflows that enables industrial technology companies and manufacturers to develop, deploy and manage customized quality-control systems that offer a competitive advantage.
Techman Robot Selects NVIDIA Isaac Sim to Optimize Automated Optical Inspection
Techman developed robotic AOI solutions by using Isaac Sim to simulate, test and optimize its state-of-the-art collaborative robots (cobots) while leveraging NVIDIA AI and GPUs for model training in the cloud and inference on the robots themselves. By developing and optimizing the robot programs in simulation, Techman was able to save programming time and generate more efficient inspection routines.
Automated Optical Inspection
Deep learning-based automatic optical inspection system empowered by online multivariate autocorrelated process control
Defect identification of tiny-scaled electronics components with high-speed throughput remains an issue in quality inspection technology. Convolutional neural networks (CNNs) deployed in automatic optical inspection (AOI) systems are powerful for detecting defects. However, they focus on individual samples but suffer from poor process control and lack of monitoring and providing the online status regarding the production process. Integrating CNN and statistical process control models will empower high-speed production lines to achieve proactive quality inspection. With the performance of the average run length for a certain range of the shifts, the proposed control chart has high detection performance for small mean shifts in quality. The proposed control chart is successfully applied to an electronic conductor manufacturing process. The proposed model facilitates a systematic quality inspection for tiny electronics components in a high-speed production line. The CNN-based AOI model empowered by the proposed control chart enables quality checking at the individual product level and process monitoring at the system level simultaneously. The contribution of the present study lies in the proposed process control framework integrating with the CNN-based AOI model in which a residual-based mixed multivariate cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control chart for monitoring online multivariate autocorrelated processes to efficiently detect 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.