INDUSTRY

Automotive / parts

Automotive electronics and parts — inline full inspection of assembly and dimensions.

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Inline checks for assembly presence, mis/missing parts and dimensional deviation.

Real-time glue-path correction: ±0.5mm positioning accuracy, 100% inline inspection, missed/broken beads near zero.
Random bin-picking of loose parts: 99%+ recognition, 4–8 seconds per pick, 3D-vision guided with no fixtures needed.
Weld and stamped-surface inspection: 97% combined detection, 0.2% false calls, 0.3mm dimensional-deviation precision.
End-to-end coverage from stamping to final assembly: missing parts, mis-assembly and dimensional deviation, no blind spots.

Recommended systems: 3D AI AOI · Robot Vision · AOI software

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APPLICATION SCENARIOS

10 real-world scenarios, one at a time

Assembly Missing Parts & Misassembly

AI Vision Inspection for Vehicle Assembly Errors & Missing Parts

Engine assembly involves dozens of elements — bolts, gaskets, sensors — and any omission can create a safety risk.

Connector & Terminal Inspection

Connector & Terminal Poor-Contact Detection

Connector and terminal contact reliability is directly tied to the stability of a vehicle's electrical system.

Paint: Sagging / Orange Peel / Color Mismatch

Automotive Paint Surface Defect Detection

Body paint quality is not just cosmetic — defects like sagging and orange peel can also degrade the paint's protective performance.

Assembly Error-Proofing (Robot Vision)

Automotive Assembly Error-Proofing: Robot Vision Reduces Misassembly Rate

Bolt misassembly and missing bolts are common quality risks in assembly, and manual checks or traditional vision have limited ability to catch them.

Random Bin Picking: 6D Pose Guidance

Random Bin Picking: 6D Pose-Guided Grasping for Featureless Parts

Feeding parts sounds simple, but for randomly stacked, smooth, featureless components, traditional automation often falls short.

Fastener Tightening Cycle Time & Full Inspection (Robot Vision)

Fastener Tightening Cycle Time & 100% Full Inspection

Fastener tightening needs to hit high cycle-time targets while still meeting 100% inspection requirements — traditional approaches struggle to deliver both.

Glue Dispensing Inline Inspection & Path Correction

100% Inline 3D Inspection & Path Correction for Automotive Glue Dispensing

Glue seal quality directly affects a vehicle's water and dust resistance; broken beads, overflow, and gaps are hard to catch with 100% inline coverage using traditional methods.

Stamped Parts: Scratches / Dents / Burrs

Stamped Sheet-Metal Surface Defect Detection

The surface quality of stamped sheet-metal body panels directly shapes vehicle appearance and a user's first impression.

Threads / Hole Position Dimensions

Fastener Tightening Visual Inspection

The tightening state of fasteners on engine blocks and transmission housings directly relates to vehicle safety.

Weld Seams & Solder Joint Quality

Deep-Learning Inspection of Body-in-White Weld Seams

The quality of hundreds of weld seams on a body-in-white directly determines structural strength and crash safety.

Case studies

Automotive parts · see, pick, inspect

From adhesive guidance and random bin picking to weld seams and stamped-surface defects — 3D robot vision plus AI-AOI take on what conventional vision cannot.

Scene · in-line adhesive guidance

A tier-1 supplier · real-time adhesive-path correction

ChallengeDispensing robots run pre-programmed paths blind; once the part shifts you get skips, neck-down and misplacement.

Solution3D robot vision measures and corrects as it dispenses (brain-eye-body loop) with in-house 3D cameras and sub-millimetre hand-eye coordination.

100%Inline 100%

±0.5mmBead accuracy

0Skips/misses

Scene · random bin picking

A parts plant · feeding randomly stacked components

ChallengeA bin of randomly stacked, feature-less, reflective parts is simply beyond conventional vision.

Solution3D robot vision estimates 6D pose, locates without features, and guides picking through the brain-eye-body loop.

99%+Pick success

4–8sCycle/part

3DReflective bins

Scene · welds & stamped surfaces

A body-panel plant · weld and stamping inspection

ChallengeWeld shapes vary widely and stamped surfaces carry 0.3 mm scratches and dents; manual reading is inconsistent and customer escapes run high.

SolutionAI-AOI surface inspection plus the world model covers cracks, under-fill, scratches and dents across many classes.

97%Accuracy

0.2%Escapes

0.3mmMin defect

Cases are anonymised industry scenarios; the figures are typical ranges achievable with WeLinkirt DaoAI solutions.

In-depth cases · IN-DEPTH

Automotive / Parts · in-depth cases

Each case breaks down the path to deployment in a real scenario — industry context, pain points, the DaoAI solution and the results.

Industrial robotic arm guided by machine vision

Automotive · 2026-06-06

100% Inline 3D Sealant Inspection: A Brain-Eye-Body Closed Loop Stops Every Break for a Tier-1 Supplier

A Tier-1 supplier's door-panel sealing line was plagued by breaks and skips, driving downstream water-leak rework. DaoAI 3D robotic vision delivers 100% inline inspection with real-time robot path correction in a brain-eye-body loop, catching every break and skip while holding bead width to ±0.5mm.

Automotive parts manufacturing and AI inspection

Automotive · 2026-06-04

Random Bin Picking: 6D Pose Drives 99%+ Pick Success on Featureless Parts

A parts plant long relied on workers hand-picking metal parts from bins; chaotic poses and featureless surfaces repeatedly blocked automation. DaoAI 3D robotic vision uses 6D pose estimation for random bin picking, holding success above 99% at a 4–8s cycle, finally enabling unmanned loading.

Automotive · 2026-06-02

Body-in-White Weld Seam Inspection via Deep Learning: No Hiding for Cracks or Lack of Fusion

A body-shop plant relied on manual visual checks of body-in-white weld seams, where cracks and lack of fusion slipped through and standards varied by inspector. DaoAI AI-AOI introduced deep-learning seam inspection that detects cracks, lack of fusion and undercut with high accuracy, unifying criteria with traceable data.

Automotive component AI visual inspection

Automotive · 2026-05-31

X-ray Porosity and Inclusion Inspection for Aluminum Die-Castings: Line-Speed Judgment Under 2s per Image

A parts plant needed X-ray to detect internal porosity and inclusions in aluminum die-castings, but manual film reading was slow, subjective and fatigue-prone. DaoAI deep-learning AI-AOI auto-judges X-ray images in under 2s each, deployable at line cadence for 100% inline internal-defect screening.

Collaborative robots in automated production

Automotive · 2026-05-29

0.3mm-Class Surface Defect Inspection for Stamped Sheet Metal: Escape Rate from 2.8% to 0.2%

A body-shop plant faced small, low-contrast scratches and dents on stamped sheet metal, with manual visual escape rates as high as 2.8%. DaoAI AI-AOI delivers 0.3mm-class surface inspection at 97% accuracy, cutting the customer escape rate to 0.2% for stable, controllable appearance-part quality.

FAQ

Automotive / Parts · AI Visual Inspection FAQ

What inspection challenges can AI vision solve for automotive parts?

It covers stamped sheet-metal surface defects, weld-seam quality, sealant bead continuity, X-ray porosity in aluminum die-casting, and 6D pose for part picking. DaoAI handles metallic glare and complex curved surfaces.

Strong metallic glare causes misses in traditional vision, how is that solved?

DaoAI combines multi-angle lighting with feature-cognition deep models to suppress glare, reliably detecting scratches and dents on highly reflective, low-contrast surfaces.

Can weld and sealant inspection achieve 100 percent inline coverage?

Yes. DaoAI 2D / 3D AI-AOI inspects weld formation and sealant continuity on every part within cycle time, replacing sampling and catching skips and broken beads in real time.

How does robotic vision improve loading and part sorting?

DaoAI robotic vision provides 6D pose and bin-picking, guiding robot arms to grasp randomly placed parts precisely for loading and unloading, raising automation throughput.