Machine vision’s role in the IIoT

The Industrial Internet of Things (IIoT) describes a factory environment where machines of all types are constantly connected, monitoring and transmitting performance data for non-stop optimization and seamless production. Many manufacturers are striving to implement IIoT capabilities in their facilities piece by piece and machine vision is playing a big role in this.

While we may be several years away from the full potential of IIoT, early applications are showing signs of promise. Many different factors have arisen that impact the adoption of machine vision for IIoT, but there are challenges too.

Challenges to machine vision in IIoT

The IIoT demands that any operation can be monitored, analyzed and controlled from anywhere on the globe, making bandwidth and latency major machine vision issues in IIoT implementation. Hardware, software and communications interfaces that can handle these tasks are expensive and difficult to implement.

Also, as more and more connected machine vision systems are introduced in the factory environment, the cyber security risks rise exponentially. Connected devices increase the number of entry points for hackers and can mitigate any gains made from IIoT implementation.

Factors driving machine vision adoption for IIoT

There are a number of reasons that machine vision is being adopted so quickly for IIoT applications all across the world. Chief among them are the increasing affordability of machine vision components and systems, a wider range of solutions, better hardware and AI-based software for deep learning capabilities.

Many machine vision companies are focusing on robotics, 3D imaging and deep learning to continue pushing the boundaries of what’s possible to automate with machine vision technology. These constantly improving capabilities are also a major reason why machine vision is being adopted for IIoT use.

Machine vision technology is well positioned to see rising rates of adoption as the IIoT becomes a reality in the modern factory settings. With IIoT still seeing early but promising applications, it’s safe to expect quick growth for highly connected, smart machine vision systems.

While there are challenges to machine vision deployment for IIoT applications, the upsides far outweigh the downsides. The technology is close to overcoming key technical hurdles and realizing widespread adoption.

This article originally appeared on the AIA website. The AIA is a part of the Association for Advancing Automation (A3). A3 is a CFE Media content partner. Edited by Chris Vavra, production editor, Control Engineering, cvavra@cfemedia.com.

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Original content can be found at www.visiononline.org.

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