In modern factories, machine vision is no longer just an inspection tool, but a strategic component in the automation of complex processes. Beyond identifying defects or verifying parts, its true value lies in its ability to generate automatic decisions, unit-level traceability, and continuous improvement of the production process.
In this article, we explore how to implement advanced machine vision systems directly on the production line, with real-world examples and key technical tips for effective deployment.
From inspection to process control using artificial vision systems
Unlike an isolated quality control station, integrated in-line vision systems allow real-time action: from rejecting a defective part to adjusting process parameters or guiding a collaborative robot based on the information captured by the cameras.
This shift from “post-production control” to “in-process control” is key to improving operational efficiency and reducing waste. For example, on an electronics assembly line, vision can detect millimeter deviations in the position of a component and, based on that information, automatically recalibrate the robot.
Common challenges in implementing artificial vision
Integrating machine vision into an industrial line is no trivial task. Often, standard solutions do not solve real factory problems, and it is necessary to design custom stations. Some of the most common challenges we encounter at I-MAS:
- Variable lighting conditions: Natural light, glare from metal surfaces, or dust in the air can cause erroneous readings. Controlled, enclosed lighting systems should be designed.
- Parts with complex geometries or critical tolerances: It is common to find components that require 3D inspections or inspections from multiple angles. This is where stereo cameras, depth sensors, or synchronized multi-camera systems come into play.
- High line speed: On fast production lines, capture and processing systems must work in milliseconds. This means choosing cameras with high refresh rates and processors capable of executing AI inferences in real time.
- Integration with PLCs and MES: Vision cannot operate independently. It must communicate with the rest of the automation system (PLC, SCADA, MES), whether to trigger an actuator, modify a variable, or generate traceability by serial number.
Deep learning and AI vision: the new standard
One of the most transformative advances has been the incorporation of neural networks trained to detect defects with complex variability. At I-MAS, we have implemented deep learning models that enable:
- Detect scratches or impurities on surfaces that were previously invisible to traditional algorithms.
- Classify products according to aesthetic or finish criteria.
- Automatically learn to recognize new defects based on historical production data.
These solutions require a good data acquisition and labeling strategy, as well as continuous training to maintain the accuracy of the system.
I-MAS: we design industrial solutions, not just vision systems
Our approach always starts with specific industrial needs. We design solutions that solve real problems in factories. We integrate machine vision with robotics, process control, haptic sensors, and industrial software to offer complete and scalable solutions.
We work with companies seeking to automate complex processes or improve product quality with minimal tolerances, from component inspection to electronic system validation.
Want to learn more about our services? Contact us or visit our projects section!
