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From an algorithmic perspective, most current AI detection models are built upon a general defect feature library. However, injection mold processing produces diverse products with varying structural complexity, surface textures, and material properties. When encountering molds with unique structures, the algorithm struggles to accurately distinguish normal process marks from genuine defects, leading to misjudgments and the rejection of qualified products.

Insufficient training data is also a key contributing factor. The performance of AI systems highly depends on the comprehensiveness and accuracy of training data. However, some injection mold processing companies provide insufficient training data samples when deploying AI detection systems, lacking samples covering various qualified product characteristics. This results in blurred boundaries for the algorithm's recognition of qualified products, easily leading to the misclassification of standard-compliant products as defective ones during the detection process.

Furthermore, insufficient adaptability to the production environment exacerbates misjudgments. Environmental factors such as light, temperature, and humidity fluctuate significantly in injection mold processing workshops. Some AI detection equipment lacks a robust environmental compensation mechanism; changes in light can interfere with the visual sensor's acquisition, causing image recognition errors and ultimately resulting in the wrongful rejection of qualified products.