Plastic syringe parts are critical components of a syringe for ensuring accurate dosing and ease of plunger movement. This application focuses on plastic syringe component inspection after they’re injection molded and before they’re assembled with the needle into the final syringe.
Defects can occur during the injection molding process due to a variety of factors such as material issues, improper mold designs, mold wear and damage, incorrect process parameters, machine wear and tear, and human error. Common defects include physical damage– cracks and stress marks on syringe components that can lead to leaks and contamination, rendering the syringe unusable.
These defects can compromise syringe functionality and effectiveness, ultimately putting patient safety at risk. Quality escapes can lead to costly recalls, legal issues, and damaged reputation with healthcare providers and patients. It is crucial for manufacturers to rigorously inspect syringe components to prevent escapes. And by inspecting components after injection molding and identifying issues early, manufacturers reduce waste and improve efficiency.
However, these defects can be difficult to detect– components are typically made from clear plastic, which is challenging to inspect as transparent parts don’t always produce enough contrast and can reflect and refract light in unpredictable ways, creating glare or distortion.
Components can also vary in size and type, each requiring different inspection criteria. Traditional machine vision requires programming hundreds of hand-crafted rules, causing them to fail to detect new or variable parts or defects that don’t match their programmed parameters.
And in high-volume manufacturing environments, syringe components must be inspected quickly to keep up with production rates. Traditional machine vision products may fail to keep up with required cycle times.
UnitX’s AI-powered inspection effectively detects syringe component plastic injection molding defects where other solutions fail.
First, the OptiX imaging system illuminates and images plastic syringe components. Then, the CorteX Central AI platform is trained on plastic injection molding defects. Lastly, those AI models are deployed to the CorteX Edge inference system to detect and classify defects in-line.
OptiX provides superior images that minimize reflectivity while maximizing defect visibility. It has 32 independently controllable lighting sources that can be optimized for various transparent syringe component plastic surfaces and defects via software. And its lighting dome design supports a very acute incidence angle of projected light, causing even very tiny defects to cast shadows which increase their visibility.
CorteX accurately detects random, complex defects. It automatically normalizes for variability in positions and orientations and recognizes defects down to the pixel-level. It reduces false positives that lead to scrap and wasted product.
CorteX supports fast AI model development, deployment, and iteration. CorteX AI models are sample efficient– they only require a few images to train on new defect types.
UnitX optimizes yield. In CorteX, can tune quality criteria and visualize the impact on yield before rolling those changes to production. All inspection data is referenceable in one central platform for manufacturers to analyze and identify areas for process improvements.
UnitX provides rapid, 100% inline inspection. OptiX has bright LEDs and fast fly capture speeds of 1m/s for high speed imaging. And CorteX Edge supports high inference speeds (up to 100 MP) to quickly output an OK/NG decision, seamlessly communicating that decision via integration to all major PLC, MES, and FTP systems.
Manufacturers who use UnitX to automate syringe component injection molding inspection are able to:
In this example, we inspected syringe components– specifically the barrel and plunger– after injection molding and before final assembly to catch defects early and minimize waste. We specifically looked for physical damage and contamination.
Imaging
First, we used OptiX to capture images of the syringe components, making sure we captured both defective and OK parts. We imaged both the front and back of the components.
Training
Next, we used CorteX Central to train our models. We created labels for three defects: scratch, crack, and hair (indicating contamination). We then labeled those defects in the images we captured from OptiX, using only 8 images of NG parts and 5 images of OK parts.
Because of CorteX’s user friendly interface and the low number of images it requires to train its AI models, it only took us 4 minutes and 45 seconds to label the defects in the images.
Detection
We then deployed those AI models to CorteX Edge to detect defects on new syringe components parts, resulting in the accurate detection and classification of syringe scratches, cracks, and contamination.
