The strength risks of aluminum die-castings hide where the eye can't reach—internal porosity and inclusions. A parts plant used DaoAI deep learning to auto-read films and fit X-ray judgment into line cadence.
Aluminum die-casting is the mainstream process for chassis, steering and new-energy structural parts, but the process readily forms internal porosity, shrinkage and inclusions. These defects are entirely invisible on the surface yet directly weaken a part's strength and air-tightness. This plant used X-ray for internal inspection of castings, but manual film reading was slow and subjective, and fatigue from long reading sessions made tiny pores easy to miss—hard to match the high cadence of a die-casting line.
Letting AI Read the X-ray Films
DaoAI brought deep-learning AI-AOI into the X-ray inspection step. Trained on large volumes of annotated inspection images, the model learns the grayscale and morphological signatures of internal defects such as porosity, shrinkage and inclusions, locating them automatically and issuing a reject decision by size, count and location. Per image inference stays under 2 seconds, fitting directly into die-casting line cadence for full inline inspection rather than offline sampling.
- Under 2s per image: inference speed matches die-cast cadence, enabling full inline inspection
- Internal defect detection: porosity, shrinkage and inclusions auto-located and graded
- Unified reject criteria: quantified by size/count/location, removing subjective variance
- Line-deployable: integrates with X-ray equipment, ending offline manual film reading
Turn offline, experience-based, fatigue-prone manual film reading into online, quantified, tireless auto-judgment.
After deployment, internal defects in the plant's castings are screened fully inline, reading efficiency and consistency rose sharply, the risk of missed calls dropped notably, and the defect data gave quantified support for tuning die-casting parameters.
FAQ
What are the hazards of internal defects in aluminum die-castings?
The pores, shrinkage porosity and inclusions inside aluminum die-castings are invisible in appearance, but they directly weaken the strength and airtightness of the parts, affecting the performance and quality of components and bringing strength risks to products.
How does DaoAI solve the problem of manual X-ray film reading?
DaoAI introduces deep-learning AI-AOI into X-ray inspection. The model is trained with a large number of labeled inspection images. The single-image inference time is less than 2 seconds. It can automatically locate defects, unify rejection criteria, and be deployed on the production line, replacing offline manual film reading.
What are the effects after adopting the DaoAI solution?
After the implementation of the solution, online full inspection of internal defects in die-castings can be realized. The film-reading efficiency and consistency are greatly improved, the risk of missed judgment is significantly reduced, and defect data provides quantitative support for adjusting die-casting process parameters.