AI Visual Inspection for Manufacturing Quality Control.
Quality Inspection AI is automated visual inspection software for manufacturing quality control.
Video transcript
In manufacturing, quality inspection is critical, and time-consuming. Human inspection is imperfect: Sandia National Laboratories notes that error rates of 20 to 30 percent are frequently quoted. Defects slip through, causing returns, rework and reputational damage. Quality Inspection AI by aQb Solutions is AI-powered inspection for manufacturing quality control, cutting inspection time and catching defects consistently. Upload a part image and it is compared to a golden reference. Machine vision and an AI vision model detect, measure and grade every defect. Each part gets one decision: accept, rework, reject or hold. Ask for a likely root cause, or an AQL sampling plan under ISO 2859-1. Results roll up into a defect Pareto, first-pass yield and scrap rate. Inspectors review every decision. This demo uses simulated flange images. Quality Inspection AI by aQb Solutions: inspect faster, escape less, prove every decision.
Quality Inspection AI by aQb Solutions is AI visual inspection software for manufacturing that compares each part with a golden reference, grades every defect and recommends accept, rework, reject or hold. Catch defects before your customers do: surface and feature defects are found and graded, and an inspector verifies each decision, for faster inspection and less rework and scrap. Shown on simulated flange images.
Impact you can measure
Illustrative targets, to be confirmed in a pilot on your own data.
15 min → 30 secTarget inspection time per part
20–30% → <3%Target inspection error rate, down from frequently quoted human rates (not yet validated)
60%+Target reduction in manual inspection effort
Hours → MinutesTarget time to identify and classify defects
Days → Same shiftTarget time to generate inspection reports
100%Target: every inspection recorded and traceable
What it does
Golden reference comparison
Every part image is compared with a known-good golden reference, so each part is checked the same way, shift after shift.
Automated visual inspection
Machine vision and an AI vision model find cracks, scratches, dents, chips, porosity and stains. An image-based gauge, calibrated on the golden reference, checks features such as bore diameter against your drawing checks, and bolt-hole presence.
Defect classification
Each defect is graded critical, major or minor against an editable acceptance-criteria table, so decisions follow your own quality rules.
Accept, rework, reject or hold
Every part gets one clear decision with the evidence shown, drawn on the image so inspectors see exactly where the issue is.
Root cause and sampling plans
Likely root causes and AQL sampling plans under ISO 2859-1, with accept and reject numbers, help teams act on what the inspection finds.
Quality analytics and traceability
Results roll up into a defect Pareto, first-pass yield and scrap rate, with a traceable record of every decision and inspectors in control.
Key Questions Answered
01 / 04
What can it inspect?
Parts compared with a golden reference image, such as the flange shown. It looks for visible surface defects (cracks, scratches, dents, chips, porosity and stains) and feature checks such as bore diameter and bolt-hole presence. Defects that cannot be seen in the image are out of scope.
02 / 04
What inputs are required?
Part images uploaded to the system, resized to a maximum of 2048 px, plus a golden reference image and drawing checks for each part number. Keep angle, distance and lighting close to the reference, and set a new reference if the camera, scale or lighting changes. Unknown parts run in a generic AI-vision mode.
03 / 04
What does it produce, and what needs review?
An annotated image with each defect marked, a critical, major or minor grade against your acceptance criteria, and an accept, rework, reject or hold recommendation, plus reports. The output is a recommendation: an inspector verifies each decision and confirms the final disposition.
04 / 04
What has been validated?
The film uses simulated flange images. Missed defects and false alarms on real parts are measured in a pilot on your own parts, and no measured results are published on this page yet.
How it works
01Capture Part
02Upload / Scan
03AI Inspection
04Defect Detection
05Quality Decision
06Human Verification
07Inspection Report
Technology stack
Computer Vision & Deep Learning
Object Detection & Image Classification
Python
FastAPI
Vue.js
React.js
Laravel
MySQL
PostgreSQL
Use cases
01One inspection standard on every shift
01Cast and machined components
One inspection standard on every shift
Compare every part with a known-good reference and grade each defect against your own acceptance criteria.
02Faster lot decisions
02Incoming and outgoing quality
Faster lot decisions
Get a standards-based sampling plan and an accept, rework, reject or hold recommendation, with an inspector confirming every decision.
03The defects that cost the most
03Quality and plant management
The defects that cost the most
Turn inspection results into a defect Pareto, first-pass yield and scrap rate, and fix the process causes behind recurring defects.
Want to see Quality Inspection AI on your own parts?