Optimization of the Defect Detection Process for Forged Components

Traditional defect detection methods for forged components rely on basic testing techniques and suffer from inherent blind spots. Consequently, they fail to meet the stringent quality inspection requirements of high-precision component manufacturing.

To address practical detection challenges across diverse application scenarios, this paper proposes a targeted optimization of the forged component detection process.

For micro-scale surface cracks, an integrated solution combining machine vision technology and magnetic particle inspection (MPI) is implemented to achieve comprehensive, high-precision defect screening of workpieces.

For hidden internal defects such as porosity and shrinkage cavities, a hybrid detection method integrating phased array ultrasonic testing (PAUT) with industrial CT re-inspection is deployed, which significantly improves the identification accuracy of internal flaws.

Furthermore, a multimodal detection workflow is optimized to effectively identify inclusions and folding defects. Additionally, 3D laser scanning technology is introduced to replace conventional manual measurement methods, enabling precise control over dimensional errors and deformation deviations in forged components.

These optimized processes establish an efficient, highly accurate intelligent detection system for forged components. This system effectively eliminates the missed detections and false alarms prevalent in traditional inspection workflows, thereby reducing production costs associated with scrapping and rework.