September- 20199MANUFACTURINGTECHNOLOGYINSIGHTSdisplayed to the operator so that it can be corrected. Each step completed can be verified and recorded to provide data that can be used for assembly work analysis and traceability.Adding Vision to the Production LineUsing vision inspection on a manufacturing or packaging line is a well-established practice. Systems range from single-point self-contained smart cameras that carry out an inspection task and deliver a pass/fail result to the control system, to PC-based systems that may feature multiple cameras and/or multiple inspection stations. Vision systems can be retrofitted to existing lines or designed into new ones. Vision inspection can also be used in conjunction with statistical process control methods to not only check critical measurements but to analyze trends in these measurements. In this way, interventions can be made to adjust the process before any out-of-tolerance product is produced. This is probably the closest forerunner to the requirements of Industry 4.0.Vision-guided RobotsIndustrial robots are already used extensively and with the emergence of collaborative robots and rapid developments in 3D image processing, they are being used much more in combination, particularly for vision-guided robotics. The vision system identifies the precise location of the object and these coordinates are transferred to the robot. Massive strides in vision-robot interfaces make this process much easier. One of the most popular uses for 3D robotic vision is in pick and place applications.Embedded VisionThe availability of small, embedded processing boards, usually based on ARM architecture, offers great potential for the development of vision systems embedded into other equipment and manufacturing processes. Many of the leading image processing libraries and toolkits can now be ported to these platforms, offering a wider range of vision solutions in this format. Combining these processing capabilities with low cost cameras, including board level cameras, means that vision systems could be incorporated into a wide variety of products and processes with comparatively small cost overheads.Machine and Deep LearningThere has been a lot of hype about deep learning in machine vision, which uses convolutional neural networks (CNNs) to carry out classification tasks by identifying characteristics learnt from a set of training images. However the challenge remains that in industrial applications the number of available training images is limited while the tools, training time and processor resources remain high. Other machine learning approaches are rapidly becoming recognised as a cheaper and simpler to implement alternative to deep learning for industrial applications. This is likely to find traction for high-performance, flexible vertical solutions that will even run on inexpensive embedded systems, making extremely cost-effective systems possible.Onwards to Industry 4.0The essence of the smart factory of the future is to optimize the process using big data analytics based on the feedback from many different types of sensors that are monitoring the process. These, of course, will include simple and smart vision sensors as well as more sophisticated vision subsystems or systems. Critically, Industry 4.0 requires a common communication protocol for all sensor types in order to allow data transfer and sharing. One standard which is proving popular in this area is the OPC UA platform-independent, open standard for machine-to-machine communications. Recently the VDMA (the Mechanical Engineering Industry Association in Germany) has announced OPC UA Companion Specifications for Robotics and Machine Vision which will provide compatibility with this standard for robots and vision systems respectively. The building bocks are beginning to come together. Critically, Industry 4.0 requires a common communication protocol for all sensor types in order to allow data transfer and sharingMark Williamson
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