case study
Surface inspection
Scratches, cracks, inclusions, porosity: automated vision inspection detects surface defects invisible to the naked eye on metal or plastic parts, however complex their geometry.
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Frequently asked questions about our vision solutions.
High-speed inspection of numerous elements, on lines handling a wide variety of formats and designs, with minimal camera adjustments.
Vision for can inspection
optics
Optical specifics
- Cans have complex optical properties that must be taken into account for defect detection
- Glossiness: strong light reflection, requiring polarizers on the lens and on the lighting
- Sharpness: in a front view of a cylindrical shape, depth of field must be maximized to stay sharp from the center (near point) to the edges (far points)
- Maximizing depth of field means minimizing the aperture, which lengthens the exposure time (risk of motion blur), hence the value of strobe lighting
integration
Station integration
- For cylindrical cans, rotating them on their own axis is possible, but mainly suited to low volumes (one rotation station per product)
- If rotation is not an option, or for block-shaped cans, a 360° inspection station is required
- This station relies on 4 cameras each covering 90°, with a minimum spacing between cans equal to one can diameter
- Example: for cylindrical cans 100 mm in diameter, allow for a station footprint of about 800 mm
case study
Crack and micro-defect detection
Non-destructive testing by machine vision identifies cracks and micro-defects on welds and stressed parts in the nuclear and defense sectors, where zero tolerance is a must.
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Leak detection on pouches
Pressure, deformation, fill level: machine vision detects leaks and sealing defects on food pouches at the end of the line, to reject non-compliant units before packaging and protect product traceability.
Learn moreFrequently asked questions about our vision solutions.
Go to the FAQCanWhich defects on cans can a vision system detect?
It detects seam defects and dents, as well as labels that are torn off, ripped, creased or swollen with an air bubble. Each one is best seen from a specific angle: the seam and a torn-off label from above, a rip and a dent from the side. The camera setup is therefore chosen according to the defects you want to catch.
CanHow small a seam defect can vision detect?
It depends on the camera resolution and the area observed. For a defect to be represented by at least 2 pixels, a 5-megapixel camera on a cylindrical can 100 mm in diameter can see a seam defect down to 0.1 mm. The finest seam defects, spikes just a few hundredths of a millimeter in size, require a higher resolution or a narrower field of observation.
CanHow many cameras are needed to inspect cans for dents?
A front-facing camera is often enough to see a dent, because it breaks the contrast. On the sides, it is harder to spot, especially on an unlabeled can with a shiny surface. If the dents to detect are small, up to six cameras may be needed, spaced every 60° around the can. The exact number is set during the feasibility study.
CanHow can a label crease be distinguished from a design on the can?
By teaching the system what your cans look like. A crease is harder to see than a tear and can be mistaken for a printed pattern: the system must therefore be shown several production orders, so that it can tell the difference between a crease or an air bubble and a design that looks similar. A tear is easier to spot when an area of the can is left unlabeled, because the contrast there is strong.
CanCan unlabeled or shiny cans be inspected?
Yes, but it is more demanding. On a shiny, unlabeled can, reflections make dents hard to see on the sides. The choice of lighting and the number of cameras compensate for this; they are validated on your actual cans before any hardware commitment.
CanCan a single vision station handle multiple can formats and designs?
Yes, that is one of the goals of the solution: inspecting a wide variety of formats and designs at high speed with minimal camera adjustments. Each new design is learned from production examples, which is what keeps the system from mistaking a pattern for a defect.
psycle solutions
A defect on your bottle line must not reach the consumer.
Machine vision inspects the cap, label, marking and body at production speed.