Designed for concrete blocks, paving stones, and other concrete products, the system detects surface defects and supports automatic handling of non-conforming products. High-resolution imaging and 3D surface measurement are used to inspect product appearance and geometry, helping identify cracks, chipped edges and corners, color variations, surface sticking, unevenness, and other surface irregularities.
AI deep learning algorithms analyze inspection data to support defect classification. Based on the inspection results, vision-based positioning guides a KUKA industrial robot equipped with vacuum gripping equipment to pick and remove defective products and replace them with qualified products.
The system combines inspection, decision-making, robotic execution, and quality data management in one integrated solution. It helps reduce manual inspection and handling, improve inspection consistency, and support a higher level of production line automation.
Inspection results can be displayed and stored automatically for historical queries and quality statistics. Depending on production requirements and line layout, the system can be installed after forming or after curing.
By connecting product inspection with automated sorting and data recording, the system provides manufacturers with a structured approach to identifying defects, handling non-conforming products, and reviewing quality results.
| Parameter | Specification |
| Surface Flatness Inspection Accuracy | ≤1 mm |
| Single-Scan Field of View | 1.5 m |
| Minimum Detectable Edge or Corner Damage | ≥10 mm |
| Minimum Detectable Surface Crack Size | 10 × 1 mm |
| Minimum Detectable Color Variation or Surface Sticking | ≥5 mm |
| Industrial Robot | KUKA |
| Maximum Picking Load per Operation | ＜200 kg |
The system combines high-resolution 2D imaging with 3D surface measurement to evaluate concrete products from both visual and geometric perspectives.
The inspection system identifies common defects such as:
•Surface cracks.
•Chipped edges and corners.
•Color variations.
•Surface sticking.
•Unevenness.
•Other surface irregularities.
The combined vision approach supports inspection of both visible surface characteristics and surface geometry, providing a broader basis for product quality assessment.
AI deep learning algorithms are trained and optimized using defect samples to accommodate variations in block types, colors, and surface textures.
Compared with inspection methods relying solely on fixed rules, the system is designed to recognize more complex defect patterns and help reduce false positives and missed detections.
3D vision technology measures product surface geometry and supports quantitative assessment of flatness and surface irregularities.
The supplied technical specifications include a surface flatness inspection accuracy of ≤1 mm and a single-scan field of view of 1.5 m. These parameters provide defined reference points for evaluating the system's inspection capabilities.
After defects are identified, vision-based positioning guides the KUKA industrial robot to the target product.
Using vacuum gripping equipment, the robot automatically removes defective blocks and replaces them with qualified products. This process reduces the need for manual sorting and handling at the inspection stage.
Profinet Industrial Ethernet enables communication and coordinated control between the vision system, pallet lifting mechanism, and industrial robot.
The coordinated equipment supports positioning, picking, handling, and placement, helping connect quality inspection with downstream product handling.
The system displays inspection results in real time and supports automatic data storage, historical queries, and quality statistics.
These functions provide a record of inspection results that can be reviewed for quality management and process evaluation.
The system can be installed after forming or after curing, depending on the production process and line layout.
Inspection and sorting configurations can be customized according to product dimensions, conveying methods, and available installation space.
The QGM Visual Quality Inspection & Intelligent Sorting System combines machine vision, AI-based defect recognition, robotic sorting, and quality data management.
By linking inspection results with automated product handling, it helps manufacturers identify visible defects, remove non-conforming products, and maintain records of inspection results. Its configurable installation options also allow the system to be evaluated for different concrete product manufacturing layouts.
Contact QGM to discuss your product types, inspection requirements, production line layout, and preferred installation position.
Address
Zenith-Straße 1 57290 Neunkirchen
By sending the contact form, you agree that your data may be used to process your request (further information and revocation notices can be found in the data protection declaration).
