Vision-Based Quality Control and Logistics Automation | Case Study

The customer needed a vision-based solution that would combine part quality inspection with automation support for palletizing and depalletizing, without compromising line performance.

The project covered multiple inspection and localization tasks across several stations. On the quality side, the system had to inspect pin dimensions and surface quality, including difficult defect types that were blurred, ambiguous, or hard to classify using conventional rule-based methods.

At the same time, the logistics process required metric positioning of bins on pallets under changing real-world conditions. This created additional complexity, especially because the line handled multiple bin and lid variants, including challenging black-on-black detection scenarios.

The implementation also had to account for surface variability, as well as installation constraints related to available space and lighting conditions. One of the key requirements was performance: the system had to complete defect imaging and classification in under 0.6 seconds.

We designed and implemented a multi-station vision system supporting both quality control and logistics automation within one integrated process.

For surface defect classification, we deployed a Cognex smart camera with Deep Learning (ViDi), enabling the system to identify difficult and ambiguous surface defects more reliably than with conventional inspection logic alone.

For palletizing and depalletizing operations, we implemented Cognex cameras for metric bin localization and for validation of product attributes in logistics processes. This allowed the system to provide accurate positional data to downstream automation while maintaining stable operation across multiple product variants.

The solution was developed to ensure:

• stable detection across changing conditions and multiple references,
• reliable operation in visually difficult applications such as black-on-black inspection,
• compatibility with robotic handling processes,
• readiness for future integration with traceability systems and MES.

During implementation and commissioning, the system demonstrated:

• reliable dimensional and surface inspection of pins,
• effective classification of difficult and ambiguous defect types,
• stable detection despite surface variability and constrained installation conditions,
• metric localization of bins on pallets under changing process conditions,
• support for multiple bin and lid variants on one line,
• inspection and defect classification performance below 0.6 seconds,
• robust data readiness for traceability and MES-oriented process architectures.

The implemented solution enabled:

• automated defect classification and improved product quality,
• earlier detection and elimination of nonconformities,
• fewer customer complaints,
• higher palletizing and depalletizing efficiency through precise positioning,
• more stable robot gripping and fewer process stoppages,
• improved intralogistics performance,
• better cooperation with AMR-based material handling processes.

The project delivered value in two critical areas at once: quality assurance and process automation.

By extending vision technology beyond inspection alone, the system helped stabilize the broader production and logistics process. Accurate quality verification reduced the risk of defective parts moving further downstream, while precise bin localization improved robotic handling reliability and overall process continuity.

Key business benefits included:

• improved product quality and reduced complaint risk,
• higher process stability,
• lower downtime risk in robotic operations,
• increased logistics efficiency,
• stronger readiness for digitally connected production environments.

This project confirmed that a well-designed vision system can do much more than detect defects.

In this case, vision technology supported both high-speed quality control and stable logistics automation, creating measurable value across the entire process. By combining deep learning-based inspection, precise localization, and multi-variant robustness, the solution improved quality, stabilized robotic handling, and increased the overall efficiency of palletizing and depalletizing operations.