What can machine vision do for quality?

During the First World War, manufacturing processes became more complex as the widespread introduction of mass production came into play. Ahead of his time, business magnate Henry Ford recognised the limitations of the methods being used in mass production and the subsequent varying quality of output. Here, Stephen Hayes, managing director of automation specialist Beckhoff UK, explains how machine vision will improve quality assurance (QA) across every stage of production.

Traditionally, quality control in manufacturing was maintained and carried out by human visual inspection. Individual machine inspectors, as they would be referred to as, would be positioned along a production line whereby it was up to their judgment to decide whether the product adhered to the finalised design standards and complied with the company's process parameters.


Considering the technological advancements made across industry today, visual inspection carried out by humans is no longer seen as the most effective method to uphold QA.

This is mainly down to the fact that quality is subjective and it is for this reason many companies are now integrating image processing technologies. Technologies like this are able to conduct optical inspections in a highly repeatable and deterministic manner.

For manufacturers, this means that even the tiniest measurements of parts, even down to the micrometre range, can be easily and thoroughly carried out. This is a task that would be impossible for the human eye to conduct accurately on a consistent basis.

Until recently, image processing in automation applications has typically been handled separately from other control systems and is either situated in a black box on a high-performance computer or implemented directly into specially configured smart cameras.

A disadvantage of having an integrated image processing solution hosted on a separate computer as we've described here is that even the smallest changes require input from a specialist or external system integrator, rather than a programmable logic controller (PLC) programmer. This is an avoidable drain on both time and finances.

In addition to this, traditional image processing methods and software are not able to guarantee an exact timing in image processing. This is because communication between the image processing and control system needs to be regulated, so that the results can reach the controller in the required time span, without external factors like the operating system affecting the transmission time.

So, what if manufacturers could not only eliminate the challenges of communication between image processing and control, but also have a system that allows the imaging processing and control components to directly communicate with one another? With Beckhoff's TwinCAT Vision software, manufacturers can combine both worlds into one integrated system and do exactly this.

TwinCAT Vision adds image processing to a universal control platform that incorporates PLCs, motion control, robotics, high-end measurement technology, Internet of Things (IoT) networks and human machine interfaces (HMIs). An advantage of combining all control functions into one tool is that it means everything is operating in one runtime environment.

Beckhoff has also designed the software with a Gigabit Ethernet interface in mind to create the GigE Vision communication standard, which offers reliable and fast transmission of image data from cameras. This function makes it possible for TwinCAT Vision to provide real-time data directly into the controller memory, which updates the user with any partial results as they become available.

By incorporating image processing into the main control system, manufacturers can improve machine efficiency by leveraging machine vision capabilities, like those offered by TwinCAT Vision, to enhance operations. Not only can this help companies retain a competitive advantage, but it can assist manufacturers in overcoming the challenges that come with vision tasks and achieve substantial cost savings at the same time.

QA aims to prevent anomalies and deficiencies, but defining quality in a manufacturing setting has historically been a difficult task.

Industry leaders like Henry Ford first emphasised the importance of standardising design and component standards to mitigate discrepancies long before the time of machine vision. Today, manufacturers can go one step further in satisfying tomorrow's marketplace demands to deliver the highest quality products by integrating image processing solutions like Beckhoff's TwinCAT Vision software.

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