INDUSTRY 4.0


g


part, especially networking video data and interconnecting various protocols,’ Hou said. ‘One major challenge today are the numerous standard protocols across different devices.’ He suggested GigE Vision as one standard


that can be used to transmit video data reliably. ‘Manufacturers need to look at how to start bridging networked video like GigE Vision with other network protocols, such as OPC UA and MQTT, that are more commonly used for machine-to-machine communication in the factory to drive full “lights out” automation.’ At the moment manufacturing


infrastructure is primarily a closed network, he said, with proprietary protocols or gated infrastructure requiring licensed protocols from vendors. Tese approaches make it difficult to scale a system, or it locks the end- user to a specific vendor. Hou is an advocate for Ethernet, which he said ‘is common across manufacturing sites and provides a reliable, secure, and time-sensitive approach to unify processes and equipment across a network’. In terms of vision, GigE transmits vision


data reliably, with low latency, and in real time using the IEEE 1588 Precision Time Protocol. ‘Tere’s now an opportunity to extend that networking expertise into other areas of the manufacturing floor as end- users seek ways to unify all systems,’ Hou said. Pleora is deploying a hybrid approach


for its clients in manufacturing through its AI Gateway product. Te device allows manufacturers to upgrade PC-based infrastructure to one where a machine


to look at how to bridge networked video like GigE Vision with other network protocols such as OPC UA’


learning plug-in can be added to existing edge devices, without disrupting the infrastructure or end-use processes. It means factories can retain existing computer vision algorithms and overlay AI on top. One potential advantage is to reduce false positives during quality control checks, saving products that would otherwise be wasted. Overlaying AI on existing inspection


processes ‘provides an an easier way for large enterprises to adopt AI to improve existing systems and re-use existing infrastructure, including cameras and PCs that are already in place,’ Hou said. Hou emphasised edge devices, because


working in the cloud is not suitable for real- time vision processing at high speed, where latency is critical. He said it is not feasible to get video data


into the cloud and have a decision back without some kind of lag. Where the cloud can play a role in


Industry 4.0 is standardising processes across different manufacturing sites. A multinational will often have different equipment or different inspection tolerances depending on the factory, which


26 IMAGING AND MACHINE VISION EUROPE OCTOBER/NOVEMBER 2020


‘Manufacturers need


leads to variability in the quality of product. Te role of the cloud is to get some


consistency in the production environment. ‘A manufacturer might use the cloud to


store profiles and train AI models that would be consistent, training neural networks on a global set of data from all factories,’ Hou explained. ‘Te model can then be deployed consistently across different sites. Building an AI infrastructure and trying to improve that AI over time requires a lot of data, so training the model in the cloud is a good way to do it.’ Te cloud can also be used to manage


edge device settings, as well as for analysing the output from edge devices. ‘It’s okay to be catching defects, but ultimately the manufacturer wants to understand root causes and why it is getting these defects,’ Hou said.


Future factories When is the work by the VDMA OPC Vision group, and now the IDTA, going to come to fruition? Heinol-Heikkinen said: ‘It’s hard to say how long this will take to implement in the real world, but the pace of work is double, or even triple, what it was when we started five years ago, because the community is much more powerful.’ Work began on the OPC UA companion


specification for machine vision five years ago in a cluster with 10 vision companies. Now, the founding members of the IDTA include companies like Bosch, Siemens, Volkswagen and Kuka. ‘It has gained momentum, with more companies working on Industry 4.0,’ Heinol-Heikkinen concluded. O


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