Injection Molding Defect Detection for Medical Components

The text outlines a method for automatically detecting injection molding defects using a Roboflow RF-DETR model, which is trained on images annotated with defect labels such as cracks, breaks, and surface contamination. Once trained, the model is deployed in a Roboflow Workflow, where it identifies defect regions and produces annotated images. A subsequent integration with Gemini 2.5 Pro allows for AI-powered inspection observations, resulting in concise inspection reports that are overlaid on the images. The workflow, originally designed for general plastic components, can be adapted for use with medical components like syringes and IV connectors by retraining the model with medical-specific data. This system aims to reduce material waste and production costs associated with defects in the injection molding process, which can amount to significant financial losses industry-wide. The text further explains that the workflow can be automated using Roboflow Agent, which constructs the pipeline based on a textual description of the desired process, thus facilitating its application in different manufacturing contexts, including medical device production.

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