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Inspecting Dinner Plates Using LabVIEW & Vision Integration

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Original Authors: Paul L. Falkenstein, Certified LabVIEW Developer, Sciotex

Edited by Cyth Systems

The Challenge

Providing a conveyor-based automated inspection system for visual assessment of dinnerware (plates and bowls).

The Solution

Using LabVIEW image acquisition and processing software to develop a system capable of visually inspecting the top and bottom finishes of more than 50 types of dinnerware plates and bowls.

The Story//Process

After manufacture, dinnerware including plates and bowls must be inspected for defects. There are several of these defects that must be measured during visual inspection to determine if the subject item is deemed a pass or a reject according to the manufacturer’s quality standards. Warp is defined as the variability of the plate height around its circumference. Trim defects include both "trim gouge,” where there is an indentation at one or more angular positions, and “trim bulge,” where the circumference bows out from a perfect circle. The other defect, glass adhesion, occurs when small molten glass balls adhere to the top or bottom surfaces in the region adjacent to the plate rim.

The system requirements called for detecting each of the four defect types when the defect size is >250 microns (0.01 in.) for dinnerware up to 12 inches in diameter. Figure 1 shows a highly magnified view of a plate with both trim bulge and glass adhesion defects that were detected and highlighted by PQIS. The system throughput is up to 60 plates per minute and inspection should run 24 hours per day, seven days per week, with minimal downtime.

Left: Conveyor-based automated vision inspection system. Right: 3D laser line scanning to produce a 3D rendering of the subject item for inspection.

The plates and dinnerware are visually inspected simultaneously from the top and bottom using 3D line scanners. These laser line scanners produce a 3D rendering of the subject item for inspection which then simplifies identifying possible defects. The automated conveyor moves the plates towards the laser line scanners and assists the dinnerware as it passes fully through the laser’s viewing window.

Detecting different types of defects requires different forms of visual inspection. Another form of this is transmitting light through the translucent plates which highlights defects in the glass. Illumination for imaging on the conveyors is provided by focused LED line lights angled off-axis from the cameras. Using off-axis lighting results in a dark image background outside of the plates allowing for high-edge contrasts. Adhered glass scatters the transmitted light and appears as a change in light intensity. Advanced processing of the light intensities yields a defect detection of greater than 95 percent with no human intervention.

The plate warp is measured at another station on one of the conveyors. A set of red laser lines is used to create a laser line incident on the bottom surface of the inverted plate or bowl. Vertical displacements of this line are viewed in respect to a reference reading and are measured using an area scan camera. Triangulation is used to generate a point cloud model of the sample as it passes under the camera. The software corrects for lens perspective and laser alignment to produce models with 100 µm height tolerances and cross-section resolution of less than 150 µm. The two lasers can generate more than 250,000 points per second. Point clouds are analyzed to determine sample warp. Figure 3 shows a typical warp model generated by PQIS (Plate Quality Inspection System).

Left: Vision inspection circling identified defect areas, Center: Real-Time 3D Model Generated for Warp Analysis. Right: Real-Time 3D Model Generated for Warp Analysis


The first generation PQIS has been operating for more than a year and the manufacturer has already seen a return on their investment because we eliminated the need to manually inspect each plate and improved product quality and yields. We recently installed the second-generation PQIS and validated its ability to detect defects with accuracy that is orders of magnitude superior to manual inspection. Developing the advanced image analysis routines employed in these systems would likely have been prohibitively expensive if we attempted to develop the software with any tools besides LabVIEW and the Vision Development Module.

Original Authors:

Paul L. Falkenstein, Certified LabVIEW Developer, Sciotex

Edited by Cyth Systems


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