Machine Vision in production and automation

From inspection to an integrated part of machines and processes

In many production environments, machine vision only comes into focus when variation becomes visible. Products that are not assessed consistently, deviations that are difficult to detect, or processes that remain dependent on human inspection.

The first step is often practical: a vision system is needed.

Historically, this usually meant that an external specialist was brought in. The system was designed, built and integrated as a relatively closed solution. For the end user, but also for the machine builder or automation engineer, the system often functioned afterwards as a black box. Products went in, and a decision came out.

That worked, as long as the process remained stable and the requirements did not change.

But in practice, production changes continuously. Product variants are adjusted, tolerances shift and conditions vary. At that point, friction arises. Not because the system does not work, but because it does not easily move along with the process.

That is where an important development of recent years lies.

Machine vision has shifted from a specialist technology to an integrable building block within machines and production lines. Not only for vision specialists, but especially for machine builders, automation engineers and technical end users.

That does not mean the technology has become simpler. But it does mean that its operation has become easier to understand and influence.

From looking to reproducible decisions

A camera can look, but a machine vision system must decide.

That seems like a small difference, but in practice it determines the entire design. An image that is good once has little value. In a production environment, it is about thousands of products per hour, under conditions that are never exactly the same.

A vision system must therefore not only see what you want it to see, but must also continue to see it in exactly the same way when:

  • products vary slightly in position 
  • surfaces change 
  • environmental conditions are not constant 

That is where the real challenge arises. Not in detecting a deviation, but in consistently continuing to detect that same deviation.

Systems that are set up as a black box often show their limitations here. The logic is not transparent and adjustments are difficult. As soon as variation increases, the behaviour becomes less predictable.

When the operation of the system is clear, this changes. Then it becomes clear why something is or is not detected, and which parameters influence that decision.

That makes it possible not only to use the system, but also to understand and adjust it.

Machine vision as part of the design

For machine builders and automation engineers, this development means that machine vision is increasingly being included from the very beginning in the design of a machine or line.

It is no longer an addition afterwards, but a functional part of the system.

Mechanical guidance, positioning and product handling are aligned with reproducibility. Lighting is integrated instead of added later. Triggering and timing become part of the control system.

Instead of an external system that is “placed somewhere”, an integrated solution emerges in which vision, mechanics and control work together.

In practice, this leads to systems that:

  • operate more stably 
  • are less dependent on corrections in software 
  • show more predictable behaviour 

For integrators, this means that vision is not a disruptive factor, but a reliable input within the total system.

Visibility as a shared design parameter

Whether you are designing a machine, integrating a line or optimizing a process, one principle remains central:

what is not stably visible cannot be assessed stably either

This makes visibility a shared responsibility.

The way a product is positioned determines how it is visible. Timing determines when it is visible. Lighting determines what contrast is created and optics determine how this is captured.

When these factors are not aligned, the complexity shifts to software. This often leads to systems that are sensitive to variation and difficult to maintain.

When visibility is physically well designed, the opposite occurs. The image becomes stable and predictable, making interpretation easier.

In practice, this means that many problems are not solved by more complex algorithms, but by better imaging.

Machine vision at multiple points in the process

Where machine vision used to be applied mainly as a final inspection, systems are now being used at multiple points in a process.

At the start of a product flow, for example, with incoming goods. In the food industry, this is a very common application. Ingredients supplied loose or in bulk are checked for contamination. Think of detecting foreign objects such as pieces of blue plastic in unpackaged raw materials. Here it is not enough to see something once. The system must reliably distinguish between product and contamination, despite variations in shape, structure and loading.

Further along in the process, machine vision is used to check process steps. During assembly, for example, it can be determined whether components are correctly present and positioned before the next operation takes place.

Here the role of vision shifts from inspection to prevention. Errors are not only detected, but prevented because the process is being monitored.

At the end of the line, vision remains important as a final verification, but often no longer as the only checkpoint.

In practice, this creates a network of measurement points, where vision provides information about the behaviour of the entire process.

From components to system behaviour

A vision system is often described in terms of components: camera, lens, lighting and software. In reality, it is mainly the relationship between these parts that determines how the system behaves.

A choice for a higher resolution camera seems like a direct improvement, but it has consequences. Smaller pixels make it possible to capture more detail, but at the same time reduce the signal per pixel. To maintain the same image quality, other parameters must be adjusted, such as light intensity or exposure time. This means that a choice in the camera immediately shifts the requirements for lighting and optics.

The same applies to the lens. More magnification provides more detail, but reduces the depth of field. In a production environment, where products are not perfectly positioned, this can make the system more sensitive to variations. What becomes visible here is that a machine vision system is always a balance. Not a collection of optimal components, but a coherent whole in which choices influence each other.

Software as an adjustable decision model

With the development of modern software platforms, the way vision systems are configured has changed.

Where systems used to depend heavily on programming work, tools are now available with which functionality is configurable. Parameters are visible and decision criteria can be adjusted.

This makes it possible to tune systems to:

  • product variations  
  • tolerances  
  • changing process conditions 

But this flexibility only works when it is clear what the relationship is between image and decision.

Adjusting a parameter without understanding how the image is created remains trial and error. When the underlying relationships are clear, it becomes targeted optimization.

This is where an important advantage arises. Not because everything becomes simpler, but because the system is easier to understand and control.

Vision as “eyes” for robots

In automation, you increasingly see machine vision used as direct input for robots.

In pick and place applications, the vision system determines where objects are located and how they are oriented. The robot depends on this information to perform movements.

In simple situations, this can be done with 2D vision. But as soon as objects are positioned randomly, for example in a bin, 3D vision becomes necessary.

In applications such as bin picking, the system does not only look at an image, but at the spatial structure of objects. This makes it possible to:

  • locate objects 
  • determine their orientation 
  • select suitable gripping points 

Here it becomes clear that vision and robotics form one system. The quality of the detection directly determines the reliability of the robot action.

Machine vision in logistics and tracking

In addition to production, machine vision also plays an increasingly important role in logistics processes. Systems are used for tracking and identifying products and packages. Think of reading barcodes, data matrix codes or text.

In these applications, the emphasis is less on detailed inspection and more on reliability and speed. Products move continuously and conditions change.

The challenge here lies in reliably recognizing information under variable conditions.

Overlap with other applications

You see the same principles in other domains as well, such as traffic and access control.

In applications such as license plate recognition in parking garages, systems are used to identify vehicles and grant access. Variable conditions also play a major role here.

Although the context is different, the core remains the same: visual information is converted into a decision that must be reliable.

Accessibility and knowledge

Machine vision has become more accessible. Hardware is standardized, software is more user friendly and tools are more readily available. This makes it possible for machine builders, integrators and end users to better understand and apply systems themselves. But accessibility does not mean that the underlying principles have become less important.

The success of a system still depends on:

  • how the image is created 
  • how stable that image is 
  • how clearly differences are visible 

What has changed is that this knowledge has become more applicable in practice.

Where do you go from here?

When machine vision becomes part of your machine or process, the need naturally arises to go deeper into the choices that come with it. Not as separate components, but as a coherent system.

You can then look further at:

These insights help to design and apply systems that not only work, but also remain predictable.

Finally

In a short time, machine vision has changed from a specialist solution into a technology that has gained a permanent place within machine building, automation and production. Not because it has become simpler, but because it has become easier to understand and integrate. This creates a different way of working.

Systems no longer need to be a black box. They can become part of the design, the process and the optimization. And that is exactly where the value lies.

Not in the image itself, but in the ability to use that image to make processes more stable, more transparent and easier to control.