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High-Speed Automated Quality
Assurance Analytic Technologies
Developers’ Portal
- Electromagnetic Measurements of Food and Beverages: an Information Retrieval problem
- Near-Field 3D
- Physics of Triggering
- Fill level inspection tech: a TCO point of view
- What Detectors detect ?
- Electronic Inspectors’ nonclassic components
- What Today Is a "Root Cause"
- Measures in a Decohering Environment
- FIFO: Bottling Quality Control applications of an idea born to manage the highest production performances
Empty Bottle Inspection Systems
- EBI Classification
- Linear and Rotary EBIs
- High Frequency residual Liquid control
- IR Residual liquid control
- Base inspection, opaque defects
- Base inspection, transparent defects
- Finish inspection, crown cork
- Finish inspection, broken Thread
- External sidewall inspection, for opaque defects
- Inner sidewall inspection for opaque defects
- Mineral ring inspection
- Scuffing inspection
- Infeed checks
- Colour inspection
- Closure inspection with digital photoscanners
Full Container Inspection Systems
Fill Level Inspection Technologies
Closure Inspection Technologies
- Ultrasound bottle and cap leakage inspection
- With 1 camera
- Optic closure check with 2 cameras
- Optic closure presence inspection, with analog photo scanner
- Slanted cap, with laser
- Inductive closure & lid inspection
- Inner Pressure Inspection with Analog Inductive sensors
- Ultrasonic fobbing systems
- Cap colour inspection
- Caps inspection and water drops
Label Inspection Technologies
![Label position inspection in-the-Conveyor with cameras. Introduction
In the fast Labeller Machines >50000 bottles-per-hour the missing plus malpositioned Collars account typical rates as high as ~80 % of the total rejects, with missing and malpositioned Body and Black labels summing up to the remaining ~20 % of the detected defects (image credit CARLSBERG Group, 2014)
Label Inspection
Flash animation, 49 MB, ZIP
After download click index.html
70 pages, 37 MB
A real jump-up on Label Inspection performances is obtained when changing technology from Photoscanners to Machine Vision. Reason is the much greater amount of Information about the label properties that it results possible to register with a camera. Amount established correlating a label with hundredths of thousands (or, millions) of individual photo-detectors, rather than just one. The figure above shows an example of the equipments’ for final inspection using CCD- or CMOS-technology cameras to inspect the labels’ presence and position. At left side, a typical example of labelling where Machine Vision becomes nearly unavoidable.
What performances
The performances to expect depend on:
the optic configuration,
amount of cameras,
eventual adoption of Artificial Intelligence algorithms,
amount of CPUs devoted to the task or, availability of GPUs.
CMOS industrial camera 32
In the fast Labeller Machines >50000 bottles-per-hour the Collars missing and mal-positioned, account typical rates as high as ~80 % of the total rejects, with missing and malpositioned Body and Black labels summing up to the remaining ~20 % of the detected defects ( CARLSBERG Group, 2014)
As an example, for the final inspection equipment depicted above, they are close to:
(1.0 ± 0.1) mm malpositioned labels, detected at >96 % with associated false reject ratio < 0.01 %,
missing- or flagging-labels, detected at >99.5 % with associated false reject ratio < 0.01 %.
To detect the entire external sidewall of the container, means to use two CCD- or CMOS- cameras equipped with some megapixels each. Notoriously, each pixel emulates an elementary (passive-only, no light source) analog photoscanner, whose dynamical range following its model ranges today (4096 - 65535) grey levels. By these digits, millions of individual analog photo detectors, and the fact that from each one we are capable to discriminate thousands of grey levels, it is easy to infer why the performances jump-up from the mere presence or absence of a label detectable with a single photoscanner, to positions whose accuracy level rounds 0.1 mm. What is a “good Label inspection” ?
The correlated Label-Pixel State
To establish a relation between a label (“Label”) and a Photoscanner (“Photoscanner”), both differentially related with the Environment (“Environment1”, Environment2”), is necessary Time. Time to transform the previous state, in which all possible kinds of correlation of the Photoscanner coexist, in a following state in which the Photoscanner is “aware” to be correlated to a label, because having recorded eigenvalues for the eigenfunction ΦiS1 describing a cap. Quantomechanical explanation of the measurement process, unaffected by the circularities implicit in the classic explanations. The vertexes represent interferences
As we saw with plenty of details elsewhere in this web site, as an example we’ll try to determine if a bottle is labelled by mean of a system like a camera, including millions of Pixels. Pixels accumulate one-by-one the photons reflected by the sidewall, neck and closure of the bottle. Later discharging the potential accumulated to an A/D converter whose outfeed is sequentially read by following circuits to deduce what to a level of illumination has been subjected each individual pixel during the limited exposure Time. [There are particularly fast Electronic Inspectors, adopted to inspect plastic empty bottles, having the exposure Time of the Pixels in their cameras forcedly limited to tens of microseconds…] Whatever physical system, label included, is represented by its wave function or state vectors. The physical meaning of the state vector becomes apparent when making a measurement. Then the state of the system assumes one of the eigenstates, with probability given by the Born rule, and the result of the measurement is the corresponding eigenvalue. To definetely perceive a correlated Label-Pixel State, they are necessary:
Time, to transform the previous state, in which all possible kinds of correlation of the pixel coexist, in a following state in which a camera inspection is “aware” to be correlated to a Label, because having recorded eigenvalues for the eigenfunction ΦiS1 describing a Label. The correlation between the two systems, camera inspection and Label, is progressively established during interaction and proportional to the natural logarithm (ln t) of the interaction time t. An ideal correlation, one corresponding to a maximised information of the inspection about the Label, can only be reached allowing an infinite time. The fact we cannot wait for an infinite time causes the measurements’ fluctuations, a synonimous of the spectrum of the eigenvalues, resulting in the Electronic Inspector's false positives (false rejects). Time, for what ? To transform the previous state, in which all possible kinds of correlation (superpositions) of each one of the many pixels composing a camera sensor coexist, in a following state in which the inspection is aware to be correlated to a Label, because having recorded eigenvalues for the eigenfunction ΦiS1 describing a Label by the camera pixels;
Interaction between the systems such that the Information in the marginal distribution of the object inspected is never decreased. In a probability distribution deriving by two random variables, we remember that marginal distribution is where we are only interested in one of them. Otherwise, we’d have forced a reduction in the sample space of one of the random variables and then, we could not have any more repeatability of the following measurements. As an example, this should be the case if to interact with the Label we’d erroneously try to use a beam of high energy neutrons, rather than strobo flashers’ or LED-matrices's low energy photons. The neutrons should modify the molecular structure of the Label, modifying its eigenstates and then the eigenvalues we expected to derive by the measurement.](_Media/label_position_inspection_i_128_hr.jpeg)
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