Machine Vision

Machine vision is used to automatically inspect products, components and manufacturing processes. It can verify whether parts are present, correctly positioned or properly assembled, identify product variants, detect visible defects and confirm that operators have completed critical process steps correctly.

When connected to operator guidance, machine vision can also prevent the workflow from continuing until the required condition has been verified.

Digital work instructions tell the operator what to do, while machine vision verifies the result.

For example, an instruction may tell an operator to install a specific component. Once the action is completed, the vision system inspects the workstation or product. If the component is present and correctly positioned, the next instruction can be released automatically.

This creates a closed-loop process:

Guide → Perform → Verify → Continue

Depending on the camera, lighting and inspection technology, machine vision can detect errors such as:

• missing components;
• incorrect components;
• incorrect positioning or orientation;
• incomplete assemblies;
• incorrect colors or markings;
• visible surface defects;
• incorrect product variants.

The exact inspection capabilities depend on the application and how clearly the required condition can be identified visually.

Traditional machine vision generally uses predefined rules to inspect characteristics such as shape, position, dimensions, contrast or color.

AI machine vision learns visual patterns from examples and can be useful when defects or acceptable conditions are more variable and difficult to describe with fixed rules.

Rule-based vision is often suitable for predictable inspection tasks, while AI can add value in more complex applications involving visual variation or irregular defects.

No.

Many manufacturing applications can be solved effectively with conventional rule-based machine vision. Simple presence checks, positioning, color detection and other clearly defined inspections may not require AI.

AI is most valuable when the visual conditions are too complex or variable for reliable rule-based inspection.

The objective should always be to use the simplest vision technology that reliably meets the inspection requirement.

Machine vision can automate many repetitive visual inspections, but it does not automatically replace every manual quality check.

It works particularly well when inspection criteria can be defined visually and checked consistently by a camera system.

Human inspection may still be appropriate when the task requires judgement, tactile feedback or characteristics that cannot be reliably observed by the vision system.

In many applications, machine vision is therefore used to automate repetitive checks while operators focus on tasks that require human expertise.

Yes.

Machine vision can monitor the work area and verify that expected conditions are met after an operator performs an action.

For example, it can check whether the correct component has been installed, whether it is positioned correctly or whether an assembly step has been completed.

When an error is detected, the operator can receive immediate feedback and the production process can be prevented from moving forward until the issue has been corrected.

Yes. Machine vision systems can detect and track objects as they move through the camera's field of view.

This can be useful for dynamic manufacturing processes where products, components or operator actions do not always occur in exactly the same position.

The required camera, processing technology and tracking method depend on the speed and complexity of the application.

A reliable machine vision application depends on more than the camera itself.

Important factors include:

• appropriate lighting;
• camera resolution;
• lens and field of view;
• mounting position;
• image-processing software;
• stable inspection conditions;
• clearly defined acceptance criteria.

For AI-based applications, suitable training examples and model validation are also important.

Good application design is often more important than simply selecting the most advanced camera.