Windows and doors
Catch frame scratches, seal misalignments, glazing gaps, and hardware errors before windows and doors leave the assembly line.
Automated quality inspection for window and door manufacturing, running on a refurbished iPhone alongside your cutting, welding, glazing, and packing stations.
What is automated quality inspection for window and door production?
AI defect detection for windows and doors uses a camera and a vision model to watch every frame, sash, and door leaf as it leaves the cutter, the corner welder, the glazing station, and the packing line, and to flag non-conforming units before they reach the dispatch yard. Instead of a line operator at the wrap station or rigid rule-based vision, the model learns the profile geometry, foil colour, seal compound, and hardware layout of your SKU portfolio, and applies a consistent visual checkpoint across shifts, line speeds, and product changeovers.
Windows and doors are particularly hard to inspect at line speed because the foiled and lacquered surfaces show scratches differently across woodgrain and solid colours, the corner-weld bead reads differently across PVC chemistries, and the hardware mounting that ruins a smooth latch action looks identical to a normal screw seat under shop lighting. Rule-based vision built around a single profile breaks the moment you swap to a different colour, a different hardware set, or a different glazing thickness. AI-led inspection handles those variations because the model learns from real production frames rather than from a fixed threshold.
The result is an automated visual checkpoint that complements your end-of-line sample and gives you a unit-by-unit image record. When an installer query comes back six weeks later, you can pull the frames from the exact production window and either confirm the defect or push back with evidence.
Defects we catch on window and door production lines
Frame scratches and surface marks
Surface scratches show up as fine cuts, drag marks, and scuffs on foiled, lacquered, or anodised profiles, caused by transfer rollers, packaging contact, or operator handling between stations. The worst offenders sit under the protective film and only surface when the installer peels the wrap on site. Operators at the wrap line catch the obvious cases but miss the borderline marks under shop-floor lighting. The AI model learns the in-spec surface signature for each foil and finish and flags any local deviation that crosses your tolerance, with the frames available so you can adjust transfer roller maintenance or handling between stations before the next batch ships.
Sealing-tape misalignment
Sealing tapes and gaskets are the rubber and foam compounds pressed into the profile groove during assembly, and a misalignment means a draft, a leak, or a sound bridge. Causes include uneven feed pressure, profile burr at the cut, and operator error during fitting. Manual operators catch the obvious gaps but miss the lifted tape ends and the local pinches that pass the wrap and fail at first installation. The AI model holds the visual signature of a properly seated seal for each profile and flags lift, gap, and pinch as soon as the local pattern deviates from spec.
Glazing-bead gaps and seating
Glazing beads are the snap-in profile pieces that retain the glass, and a poorly seated bead leaves a visible gap on the inside or the outside of the frame. Causes include bead-end mismatch, profile dimension drift, and operator force at the seating step. The defect ruins the watertightness of the glazing seal and shows up as a visible line across the frame. The AI model learns the in-spec bead seating for each profile and flags gaps and uneven seating at the glazing-station exit so the operator can correct the assembly before the unit reaches the wrap.
Corner-weld bead defects
On PVC frames the four corners are joined by a hot-plate weld that produces a small bead at the corner. A clean bead is a strong joint, while a starved or burnt bead signals a temperature, time, or pressure deviation that ruins the corner strength. Operators sample bead geometry by eye but cannot inspect every corner of every frame on a high-throughput line. The AI model learns the in-spec bead signature for each colour and profile and flags starved, burnt, or skewed beads at the welder exit, with the frames available so you can adjust the weld parameters before a full batch goes through.
Hardware fit and screw errors
Hardware errors include missing screws, wrong handle position, swapped left and right hinges, and over-tightened mounting plates that warp the profile. The defects ruin the latch action and show up at first install. Manual operators check the visible hardware but miss the back-set screws and the swapped hinges that pass the wrap. The AI model can be set up to read the hardware face directly and flag missing screws, swapped components, and warped mounting plates at the assembly exit.
Profile colour and foil drift
Foil colour drift is the gradual deviation of the surface foil shade caused by foil-batch variation, hot-laminator temperature drift, or solvent drying inconsistency. The worst cases survive the QC sample because they sit between the four corners the operator inspects, and a frame installed from a drifted batch shows a visible mismatch with the rest of the elevation. The AI model holds a learned reference shade per range and flags drift as soon as the local colour delta exceeds your spec, giving the line a chance to correct upstream conditions.
The lighting setup that makes this work on a window and door line is a diffuse overhead light over the corner welder and the glazing station to read foil and bead, plus a low-angle ring light at the wrap line to read glass and seal. An iPhone Pro with macro and wide-angle lenses handles the seven defect families from a single inspection station per critical control point. We synchronise the rig with the conveyor encoder so that flagged units drive a downstream divert or hold decision. We spec the optics with you during onboarding.
How Enao runs on a window and door line
The full hardware rig costs less than €1,000 and consists of a refurbished iPhone Pro, a diffuse overhead light with an optional low-angle ring light for glass inspection, a USB-C cable, and a mount that clamps over the corner welder, the glazing station, or the wrap line. PLC integration is not required for the first deployment, the rig fits in a flight case, and the line keeps running while you set it up.
Onboarding is self-serve. Your line team mounts the rig, opens the Enao app, and starts collecting reference frames at the next changeover. Day one returns 80% accuracy without any prior labelling, and by day fourteen the model is operating above the manual inspector on the defect families it has seen, improving with every flagged unit that the line confirms or rejects.
Each line teaches its own model what its profile colours, foil ranges, and hardware sets look like. When you swap to a different range on the same line, the model adapts in a single shift. When you bring a sister line online with a similar product family, the second model starts from the first model's experience and the marginal effort drops sharply.
Out-of-spec units stop reaching the wrap, scrap is logged at the inspection point rather than at the QC office, and your operators get back the hours of attention they need for the parts of the job that still need a human, including welder setup, glazing fit, and customer complaints.
How Enao compares to manual inspection and traditional machine vision
For window and door producers the comparison sharpens around five dimensions.
Setup time on a window and door line. — Manual visual inspection: hours of training per operator, ongoing labour. Traditional machine vision: three to nine months of integration with a system integrator, plus a rule set per profile. Enao: deployed in a week by your own team, day one at 80% accuracy.
Hardware cost per line. — Manual visual inspection: none upfront, ongoing labour cost. Traditional machine vision: €40,000 to €200,000 per line for industrial cameras, structured lighting, and integration. Enao: under €1,000 per line with a refurbished iPhone Pro, lamp, and mount.
Handling new colours, profiles, and hardware sets. — Manual visual inspection: re-train operators for every new SKU. Traditional machine vision: rewrite the rule set per recipe, often outsourced to the integrator. Enao: re-teach the model on new profiles and hardware in a single shift, no code to touch.
Detection accuracy on subtle scratches and bead drift. — Manual visual inspection: high at shift start, drops measurably after three hours. Traditional machine vision: strong on dimensional checks, weak on subtle scratch detection and corner-weld bead drift. Enao: learns surface and weld signatures from reference frames and holds accuracy across shifts and runs.
Who runs it. — Manual visual inspection: trained operator at the wrap line. Traditional machine vision: system integrator or a specialised vision engineer. Enao: your line team, no external specialist required.
Builders, installers, and merchants change vendors over the cost of a scratched batch, and the cost of a chargeback or a quiet specification swap sits well above the cost of an iPhone-based inspection rig. Enao is built for that gap.
