Benefits of scalable machine vision systems

The challenge for machine vision systems remains the ease of configuration and processing, which affects scalability. This needs to improve as the technology becomes more common.

By Stephen Hayes September 11, 2020

Visual inspection is an increasingly important aspect of modern industrial processes. Whether it is used for assisting in sorting bulk materials in pharmaceutical processing, inspecting the quality of produce in food manufacturing or informing the positioning of a delta robot in packaging, machine vision significantly increases the efficiency and productivity of various industrial processes.

It is for this reason the latest figures from MarketsandMarkets estimate the global machine vision market to be valued at $9.6 billion in 2020, with expectations of growth to $13 billion by 2025. Even this figure is likely overshadowed by the value it adds to industrial businesses annually, by virtue of increasing throughput, minimizing faulty products and reducing labor time for inspection tasks.

However, there remains one consistent challenge with machine vision systems – the ease of configuration and processing. This, in turn, affects scalability. Typically, machine vision systems will be connected to a specialized, high performance industrial computer that is separate to a plant’s other automation systems. A setup like this means that specialist knowledge is needed to adjust system parameters and configure cameras, in an environment that is often unfamiliar to many automation engineers.

The specialism required presents not only a limitation to the scalability of machine vision systems in terms of the availability of skills to program and manage the processing system itself, but also a barrier to true efficiency.

For example, imagine a food processing plant where one vision inspection system is required for raw material sorting, another for raw material defect identification, a third MV system for quality assurance post-processing and finally an machine vision equipped robotic system for packaging. In this scenario, a change to the production process would require quick adjustment of multiple systems, and any issues would need to be addressed by a small number of skilled technical staff. For large operations, it’s a high cost option that simply isn’t scalable.

There is also the matter of latency. If the image processing is completed on a separate system to the motion control and automation, then that data needs to be sent to the relevant systems, where it is actioned accordingly.

The logical solution is to integrate machine vision processing and management into the same system as the motion control and automation, to increase responsiveness and to support more engineers to adjust the vision processing software. TwinCAT Vision, for example, brings image processing into a single platform alongside programmable logic controllers (PLCs), human-machine interface (HMI), motion control and high-end measurement technology. With everything all in one platform, machines can respond to vision input data in real-time, eliminating delays in motion systems.

The PLC environment also means that the system uses PLC programming languages and the same configuration tools as used for fieldbuses. This means that adjusting machine vision systems is an easier task for automation engineers, allowing easy configuration and calibration of cameras, as well as supporting an instant review of any changes made.

Machine vision systems have come a long way from the optical sorting systems of the 1930s, with advanced functionality and increased importance. Just as the physical systems continue to develop, it’s important we also look at new ways of advancing processing software to ensure these systems continue to provide efficiency for years to come.

Stephen Hayes is managing director of Beckhoff Automation UK.

This article originally appread on Control Engineering Europe’s website.

Author Bio: Stephen Hayes is managing director of Beckhoff Automation UK.

Related Resources