Injection-molded appearance defects span geometric (flash, sink) and visual (color, flow marks) types, shifting with mold, material lot and color. Rather than coding rules per defect, let the model learn what good looks like and catch every deviation in one pass.
An injection-molding plant supplies housings and structural parts for appliances, consumer electronics and household goods, with hundreds of molds and multiple colors and material lots per product. Common appearance defects include flash, sink marks, shrinkage, flow marks, silver streaks, color variation, black specks and contamination; defect forms fluctuate with process parameters, and manual inspection is subjective with hard-to-unify standards—color judgment especially leans on individual experience.
The plant faces the classic high-mix, low-volume bind: molds and colors switch frequently, and defect samples are hard to collect fully for every combination; conventional vision codes a rule per defect and must be retuned for every mold swap, with maintenance cost so high it cannot keep pace with changeover.
DaoAI solution: good-sample baseline plus APDT positive-sample learning for one-pass multi-defect detection
DaoAI combines a good-sample baseline with APDT positive-sample learning, using a few good samples to set the inspection standard for each mold/color combination; the system identifies flash, sink, flow marks and color variation uniformly in one flow, without enumerating samples per defect. Changeover simply recalls the matching combination's good-sample baseline.
- APDT positive-sample learning models from a few good samples, covering flash, sink, flow marks, silver streaks, color variation and black specks
- Geometric and visual defects are identified in one unified flow, with standardized, reproducible color judgment
- 5-minute zero-code changeover lets line operators switch mold/color on site
- Inspection baselines archived by mold and color, so high-mix small batches go live fast
However many molds and colors, there is one standard: does it look like a good sample of this combination.
After deployment, injection-molded appearance inspection moved from individual experience to a unified, reproducible standard, giving contentious defects like color variation and sink marks an objective verdict. Changeover became a 5-minute zero-code switch, hundreds of molds and many color combinations go live fast, misses and false calls fell together, and the line holds stable appearance quality across high-mix, small batches.
FAQ
What are the characteristics of the appearance defects of injection-molded parts, and what problems exist in traditional detection methods?
The appearance defects of injection-molded parts include geometric and visual types, which change with molds, etc. Traditional methods set rules for each defect, need to be readjusted when changing molds, with high maintenance costs and can't keep up with the change-over rhythm. Manual inspection is subjective and hard to standardize.
How does the DaoAI solution solve the problem of appearance inspection of injection-molded parts?
DaoAI combines the good-product benchmark with APDT positive-sample learning. It uses a small number of good products to establish inspection standards, identifies multiple types of defects uniformly, doesn't need to exhaust samples, enables 5-minute zero-code change-over, and quickly launches small-batch and multi-variety production.
What effects does the DaoAI solution have after implementation?
After implementation, the appearance inspection of injection-molded parts has a unified and reproducible standard. There are objective judgments for controversial defects. Change - over is fast, missed and false detections decrease, and the production line maintains stable appearance quality for small-batch and multi-variety production.