SolVisionCase Study
Metal Gasket Defect Detection Using AI
Customer
The customer is a professional CNC manufacturer specializing in producing high-quality metal components, including motor shafts and other critical hardware parts. Their commitment to precision and quality aligns with the need for effective defect detection in metal gaskets.
Case
Essential Functions of Metal Gaskets in Machinery
Metal gaskets play a critical role in mechanical equipment and engineering structures. They provide essential sealing, support, and cushioning functions, ensuring that components do not suffer damage from close contact. By reducing friction between metal parts, gaskets help extend equipment lifespan. Additionally, they prevent the leakage of liquids, gases, and other substances, thereby reinforcing the overall performance and safety of the product. Given their critical functions, detecting gasket defects is essential for maintaining product integrity and reliability.

Challenge
Effective Defect Detection for Metal Gaskets
Metal gaskets are critical components that must perform flawlessly; even minor gasket defects—such as dents, cracks, or deformations—can compromise sealing, leading to leakage and decreased product safety. These defects can also disrupt pressure distribution, resulting in concentrated stress on bolts and contact surfaces, which may undermine stability and accelerate wear. Consequently, precise defect detection is essential. However, the small size and intricate surfaces of metal gaskets make it challenging to visually identify these gasket defects or other irregularities. Traditional inspection methods often prove inadequate, presenting significant challenges for gasket manufacturers in ensuring product quality.
Solution
Enhancing Defect Detection with SolVision
SolVision AI visual inspection system excels at detecting gasket defects, including burrs, dents, and surface irregularities on metal gaskets. With its ability to operate efficiently with only a few image samples, SolVision leverages deep learning to deliver accurate defect detection. Additionally, it streamlines gasket classification during inspections, significantly reducing inspection times and enhancing product yield.