At-line, in-line, and over-line vision inspection technologies are built to offer critical measurements quicker and more consistently than manual measurements, while also allowing for the measurement and quantification of product attributes that are nearly impossible to calculate manually (e.g., volume, surface area, etc.).
The use of these systems, such as the Sightline vision inspection system from KPM Analytics, eradicates the need for manual data entry and charting, resulting in more reliable and repeatable quality assurance measurements. The advantages include automatic rejection of defective or out-of-spec products, fast and convenient product sampling, automated reporting and more data for less effort.
- The average color of top
- The average color of the bottom (toast/dark marks ignored)
- Holes, tears, folds, tails, bites
- Irregular edges, foreign objects
- Burn marks and dark spots
Image Credit: KPM Analytics
- Total number
- Color analysis
- Roundness/shape verification
- Minimum, Maximum, Average diameter
- Area measurements
- Edge roughness
- Tails, bites, folds
- Blowout detection
Image Credit: KPM Analytics
Easy detection of holes and transparent areas. Image Credit: KPM Analytics
KPM Analytics vision inspection technology can quantify virtually any food product, either directly during the production process (Over-Line/In-Line) or with a Benchtop Inspection System (Off-Line).
A few of the measurements available, notably for tortilla bread, are listed below.
Table 1. Overhead 2D Analysis. Source: KPM Analytics
||The minimum and maximum diameters of the object as measured through the center of the object.
||The average of 180 diameters of the object as measured every one degree through the center of the object.
||Roundness/Ovality. The comparison measurement of the product to a proper circle.
||Identified based on user-defined color specifications. Determine total number, % of area affected (distribution), voids, largest or smallest toast mark, and color information.
||Identification of holes, the area of each hole and the location of the holes.
||The overall area of the object. Used to find doubles and small products.
||The length and locations of straight segments/folds anywhere on the perimeter of the product.
||The maximum standard deviation of contiguous radii around the circumference of the object.
||Detection of shape deviations, mainly in the form of tears, bites and tails.
||The average color of the product with all marks ignored for the calculation.
(*bottom analysis is optional)
Table 2. Package Analysis. Source: KPM Analytics
||Inspection of the seal area for abnormal areas based on color and size. Analysis of heat seal pattern for integrity and abnormalities.
||Readability and verification of bar codes, date codes, lot codes, etc.
|Regions of Interest
||Color analysis of specific regions of the package. Used to determine presence of product, proper placement of label, package color anomalies, etc.
||Validate specific text elements, such as product names, compliance with regulated statements, etc.
Actual geometry data extracted from live tortilla production run displayed on the included HMI monitor at the line. Image Credit: KPM Analytics
Example of unwanted fold found during a production run. Image Credit: KPM Analytics
Toast marks identified by user-defined color specifications. Image Credit: KPM Analytics
Thermal inspection of the package seal. Image Credit: KPM Analytics
This information has been sourced, reviewed and adapted from materials provided by KPM Analytics.
For more information on this source, please visit KPM Analytics.