AI enables machine learning by using a variety of training models to simulate and infer the status or appearance of objects.
Vision analytics on the factory floor adds intelligence to factories design and process. Today's technologies automate the collection, storage, retrieval, and decision making across multiple factories and factory sub-systems at the edge.
The FLEX-BX200 and TANK-XM811AI dev. kit are AI hardware ready system ideal for deep learning inference computing to help you get faster, deeper insights into your customers and your business. IEI’s FLEX-BX200 and TANK-XM811AI dev. support graphics cards, Intel® FPGA acceleration cards, and Intel® VPU acceleration cards, and provides additional computational power plus end-to-end solution to run your tasks more efficiently. With the Intel®OpenVINO toolkit, it can help you deploy your solutions faster than ever.
Agricultural products are valued by their appearance. The color indicates parameters like ripeness, defects, etc. The quality decisions vary among the graders and often inconsistent. Machine vision technology offers the solution for all these problems.
The FLEX series designed for machine vision market has four PCIe 3.0 expansion slots for installing motion controller cards, GPU/FPGA/VPU cards and the PoE Ethernet card which is developed by IEI and has four GbE Power over Ethernet (PoE) ports compliant with IEEE 802.3af for direct connection to CCTV cameras without needing separate power.
During the manufacturing process, defects could be introduced and harmful to the quality. It is necessary to classify the defects detected by AOI machine appropriately especially appearance defects. The higher accuracy to classify defects, the less cost spent on review and repair station.
The TANK AIoT Dev. Kit features rich I/O and dual PCIe x8 signals to support add-ons like the Acceleration cards (Mustang-F100-A10 & Mustang-V100-MX8) or the PoE to enhance the defects detected performance.
Mustang series solutions help enable intelligent factories to be more efficient on work order schedule arrangements. In today's production line, sticking to manufacturing schedules is becoming more and more important for business efficiency. From raw material storage to fabrication and complete products, all information from factory such as manufacturing equipment process time and warehouse storage status are essential to achieve production goals.
Solutions based on AI technology can produce more detailed, accurate, and meaningful digital models of equipment and processes for product management.
Intel® Distribution of OpenVINO™ toolkit is based on convolutional neural networks (CNN), the toolkit extends workloads across multiple types of Intel® platforms and maximizes performance.
It can optimize pre-trained deep learning models such as Caffe, MXNET, and ONNX Tensorflow. The tool suite includes more than 20 pre-trained models, and supports 100+ public and custom models (includes Caffe*, MXNet, TensorFlow*, ONNX*, Kaldi*) for easier deployments across Intel® silicon products (CPU, GPU/Intel® Processor Graphics, FPGA, VPU).
