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Your vision system monitoring your production quality is finally validated and running 24/7, with the PQ behind you it’s time to relax. Or so you thought! It’s important that vision systems are not simply consigned to maintenance without an understanding of the potential automated inspection errors and what to look out for once your machine vision system is installed and running. These are three immediate ways to minimise inspection errors in your medical device, pharma or life science vision inspection solution.

1. Vision system light monitor.
As part of the installation of the vision system, most modern machine vision solutions in medical device manufacturing will have light controllers and the ability to compensate for any changes in the light degradation over time. Fading is a slow process but needs to be monitored. LEDs are made up of various materials such as semiconductors, substrates, encapsulants, and connectors. Over time, these materials can degrade, leading to reduced light output and colour shift. It’s important in a validated solution to understand these issues and have either an automated approach to the changing light condition through close loop control of grey level monitoring, or a manual assessment on a regular interval. It’s something to definitely keep an eye on.

2. Calibration pieces.
For a vision machines preforming automated metrology inspection the calibration is an important aspect. The calibration process will have been defined as part of the validation and production plan. Typically the calibration of a vision system will normally in the form of a calibrated slide with graticules, a datum sphere or a machined piece with traceability certification. Following from this would have been the MSA Type 1 Gauge Studies, this the starting point prior to a G R&R to determine the difference between an average set of measurements and a reference value (bias) of the vision system. Finally the system would be validated with the a Gauge R&R, which is an industry-standard methodology used to investigate the repeatability and reproducibility of the measurement system. So following this the calibration piece will be a critical part of the automated inspection calibration process. It’s important to store the calibration piece carefully, use it as determined from the validation process and keep it clean and free from debris. Make sure your calibration pieces are protected.

3. Preventative maintenance.
Vision system preventative maintenance is essential in the manufacturing of medical devices because it helps to ensure that the vision systems performs effectively over the intended lifespan. Medical devices are used to diagnose, treat, and monitor patients, and they play an important part in the delivery of healthcare. If these devices fail, malfunction, or are not properly calibrated, substantial consequences can occur, including patient damage, higher healthcare costs, and legal culpability for the manufacturer. Therefore, any automated machine vision system which is making a call on the quality of the product (at speed), must be maintained and checked regularly. Preventative maintenance of the vision systems involves inspecting, testing, cleaning, and calibrating the system on a regular basis, as well as replacing old or damaged parts.

Medical device makers benefit from implementing a preventative maintenance for the vision system in the following ways –
Continued reliability: Regular maintenance of the machine vision system can help identify and address potential issues before they become serious problems, reducing the risk of device failure and increasing device reliability.
Extend operational lifespan: Regular maintenance can help extend the lifespan of the vision system, reducing the need for costly repairs and replacements.
Ensure regulatory compliance: Medical device manufacturers are required to comply with strict regulatory standards (FDS, GAMP, ISPE) and regular maintenance is an important part of meeting these standards.

These three steps will ultimately help to lessen the exposure of the manufacture to production faults, and stop errors being introduced into the medical devices vision system production process. By reducing errors in the machine vision system the manufacturer can keep production running smoothly, increase yield and reduce downtime.

Automated inspection of pharmaceutical labels is a critical part of an automated production process in the pharmaceutical and medical device industries. The inspection process ensures that the correct labels are applied to the right products and that the labels contain relevant and validated information. These are generally a combination of Optical Character Recognition (OCR), Optical Character Verification (OCV), Print Quality Inspection and measurement of label positioning. Some manufacturers also require a cosmetic inspection of the label for debris, inclusions, smudges and marks. The use of vision inspection systems can significantly improve the efficiency and accuracy of the automation process, while also reducing the potential for human error.

Automated vision inspection systems can also help to ensure compliance with regulatory requirements for labelling, and provide manufacturers with a cost-effective and efficient way to improve the quality of their products. With increasing pressure to improve production efficiency and reduce costs, more and more pharmaceutical and medical device manufacturers are turning to automated vision inspection systems to improve their production processes and ensure quality products for their customers.

Over the last few years, more vision inspection systems for pharmaceutical label checks have been adapting to the use of deep learning and artificial intelligence neural networks, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs).

Using AI and deep learning for optical character recognition (OCR) can significantly improve the performance of automated inspection systems for pharmaceutical labels. Traditional OCR systems rely on pre-defined templates and rules to recognize and interpret text on labels, which can be limited in their ability to recognize text accurately in different fonts, sizes, and layouts.

AI-based OCR systems, such as those using deep learning, can be trained on a large dataset of labelled images, allowing them to learn and recognize different fonts, sizes, and layouts of text. This makes them more robust and accurate in recognizing text in real-world scenarios, where labels may have variations in their appearance. The deep learning machine vision systems can also be trained to recognize text that is partially obscured or distorted, which is a common problem in real-world scenarios. This allows the system to make educated guesses about the text, which can improve its accuracy and reliability.

On the flip side, while the level of recognition may improve for hard-to-recognise situations for traditional machine vision inspection, the system also must be validated to set criteria for FDA/GAMP validation. How can this be achieved for a neural deep learning system?

It is true that gathering data for training an AI-based vision inspection system can be a hassle, but it is a crucial step to ensure the system’s performance. One way to overcome this is by using synthetic, validated data, which can be generated using computer-generated images. This can reduce the need for natural images and allows a broader range of variations to be included in the dataset. These synthetic images could be tested and validated on a traditional machine vision set-up, before then transferred to the training set for the AI-based vision inspection. Another way is to use transfer learning, in which a pre-trained validated model is fine-tuned on a smaller dataset of images specific to the task. This can significantly reduce the amount of data and resources needed to train a new model.

In conclusion, validated industries such as medical devices and pharmaceuticals continue to adapt to new, robust methods for traceability and quality print checking. Deep learning is evolving to meet the unique validation requirements of these industries.

Semi-automatic (or Semi-automated) inspection machines for the pharmaceutical industry are intended for easy and effective examination of vials, ampoules, cartridges, and syringes carrying injectable liquids, powders, or freeze-dried products. This is the step up from a manual inspection approach which would typically be done by a trained inspector by rotating the vial in front of a black and white background for the analyses of certain defects, particles and particulates. The semi-automatic approach allows for higher volumes of pharmaceutical products to be inspected at greater speeds.

In simple terms, “Semi-Automatic” means the ability to rotate the product and present it with the correct lighting LUX level on a black and white background for the purpose of an operator to visually identify defects in the container or contents. This is typically in the form of a stand-alone semi-automated machine or part of an in-feed or in-line production system. Therefore, this shouldn’t be confused with fully automated visual inspection systems.

The key documents relating to the inspection of pharmaceutical vials and ampoules for contamination are driven by the United States Pharmacopeia (USP) Chapter 1 Injections and Implanted Drug Products (Parenterals). Product Quality Tests states that injectable drug preparations should be designed to exclude particulate matter as defined in USP Chapters “787” Subvisible Particulate Matter in Therapeutic Protein Injections, “788” Particulate Matter in Injections, and “789” Particulate Matter in Ophthalmic Solutions. Each final container should be inspected for particulate matter, as defined in Chapter “790” Visible Particulates in Injections. Containers that show the presence of visible particulates must be rejected.

Particulates can take all forms, from contamination and debris particulates, coating fragments, air bubbles or inconsistencies, visual defects in primary containers, polymer particles through to glass & metallic particles and microscopic cracks or inclusions in the glass or polymer. All these levels of defects need to be identified and rejected.

Every product needs to undergo a visual inspection to determine whether or not it contains any particle matter. It is possible to automate, manually perform, or partially automate the initial 100% check. The subsequent inspection to ensure that the acceptable quality level (AQL) has been met must be carried out manually. The presumed value for the lower limit of the visible range is 100µm; however, this value can shift based on the product container, the nature of the drug product, and the qualities of the particulate matter (shape, colour, refractive index etc.).

The sample in its primary container (vial, prefilled syringe, and glass cartridge) is rotated under standardised conditions for semi-automated visual inspection (roughly above 50-100 µm). The liquid in the spinning and/or subsequently stopped container can then be viewed to confirm compliance to quality levels. This is the way to reach regulatory compliance by applying semi-automatic inspection.

Although the regulation of particulate matter in pharmaceutical products is as detailed above, there is no regulatory guidance on either the limits of particulate matter in principal packaging components. Instead, the specifications are determined through collaboration between the customers and the providers. To lessen the amount of particulate matter in finished drug products, pharmaceutical manufacturers and packaging suppliers must work together to achieve this goal. In particular, this can be accomplished by making use of components that have minimal levels of loose, embedded, and adhered particulates. But no matter what contingencies are put in place, the production process should include a level of semi-automatic inspection to allow rouge particulates to be rejected from the final product.

Human inspectors are adaptable and can provide a response to something they have never seen before or something that “doesn’t look right”. They are also able to more easily withstand typical variations in containers, particularly those generated by moulding, which results in a reduction in the frequency of wrongly rejected good products. This compares to fully automated visual inspection, which has rigid boundaries for a pass and fails. However, people are restricted in the number of inspections that can be performed (i.e., the number of containers per minute or hour that they can inspect). Therefore, semi-automatic inspection is the solution to allow a high volume of product to be inspected at speed by an operator while still complying with the relevant regulatory levels.

Operators are also prone to weariness and require numerous pauses in order to work at a high level for extended periods of time. All of these constraints contribute to a greater degree of variation in the outcomes of manual inspections; however, this variation can be reduced by semi-automated inspection, combined with receiving enough training and adhering to standard operating procedures.

Manufacturers should conduct a 100% inspection during the stage at which there is the greatest likelihood that visible particulates will be detected in the final container (for example, before labelling to maximise container clarity). This is the stage at which the likelihood that visible particulates will be detected in the final container is highest. The equipment and the physical environment in which the visual inspection will be performed should both be designed by the manufacturers to reduce the amount of unpredictability in the inspection process while simultaneously increasing the amount of information that can be gleaned from it.

After 100% semi-automated inspection is completed, a manual inspection based on ISO 2859-1/ANSI/ASQ Z1.4 should be performed. The size of the AQL sampling depends on the batch size. Inspection Level II should be used.

Overall, Semi-automatic inspection of pharmaceutical products provides a route to regulatory compliance prior to the adoption of fully automated visual quality control.

What’s the secret to a perfect vision system installation? That’s what all industrial automation and process control engineers want to know. The engineering and quality managers have identified a critical process requiring automated inspection, so how do you guarantee that the machine vision system has been set up to the best possible engineering standard. And how can you apply this knowledge to refine the current vision systems on the line? Let’s look at some of the critical parts of the vision inspection chain and see the vital elements and what needs to be addressed. IVS has been commissioning machine vision systems for over two decades, so we understand the core elements inside and out of vision system installs. Use some of our tricks of the trade to improve your vision system. These 3 simple steps to improve your machine vision system will help you understand the immediate changes you can make to improve a vision system.

Step 1 -Lighting and filters.
What wavelength of light are you using, and how does this compare to the sensitivity of the camera, coupled with the colours on the part requiring inspection? An intelligent approach to how segmentation of the scene will take place should be based on the correct lighting set-up, the angle of the machine vision lighting and how the contrast is created based on the colour of the part against the light, either reflecting to or away from the camera. Coloured illumination may intensify or suppress defined colours in monochrome imaging. This way, the contrast helps recognise relevant features, which is decisive for an application-specific and optimally matched vision solution. For example, blue light cast onto a multi-colour surface will only be reflected by the blue content. The more blue content in the object, the more light is reflected, so the brighter the object will appear. In contrast, red content illuminated in blue appears exceptionally dark.

Combining, a filter mounted on the end of the lens allows blocking all unwanted ambient light and passing only the necessary wavelength of light required, increasing contrast and resolution. The choice of which filter to use depends on the type of result you are trying to achieve. But selecting an appropriate filter may improve your current system’s quality if ambient light is a problem for you.

Bandpass filters are frequently one of the simplest methods to significantly improve image quality. They have a Gaussian transmission curve and are ideal for investigating the effects of monochromatic imaging. But what are the different filters available?

Multi Bandpass Filters
Multi-Bandpass Filters use a single filter to transmit two or more distinct wavelength bands. They provide natural colour reproduction during the day and near-infrared illumination at night, resulting in accurate, high-contrast pictures. As a result, these are more suited for applications including intelligent traffic solutions, security monitoring, and agricultural inspection.

Longpass Filters
Longpass Filters enable a smooth transition from reflection to transmission and are available in various wavelengths based on the system’s requirements. Longpass filters are an efficient method for blocking excitation light in fluorescent applications and are best employed in a controlled environment where several wavelengths must be passed.

Shortpass/NIR Cut Filters
Shortpass filters offer an unrivalled shift from transmission to reflection and superb contrast. They work best in colour imaging to ensure natural colour reproduction and prevent infrared saturation.

Polarising Filters
Polarising filters minimise reflection, increase contrast, and discover flaws in transparent materials. They’re ideally suited for tasks requiring a lot of reflection, such as analysing items on glossy or very shiny surfaces.

Neutral Density Filters
Neutral Density Filters are known in the machine vision industry as “sunglasses for your system” because they diminish light saturation. Absorptive and reflective styles are available and are ideally suited for situations with excessive light. They offer excellent methods for controlling lens aperture and improving field depth.

So how do you match the filter to the light? Well, the LED’s nanometers (nm) wavelength typically relates to the band pass rating for the filter. So if you work in UV at 365 or 395nm, you would use an equivalent rated filter – BP365. If you’re using a green LED at 520nm, this would be a 520 band pass filter, and so forth. According to the filter rating, most filters have a curve chart to see where the light is cut-off.

Step 2 Camera resolution and fields of view (fov)
A vision system’s spatial resolution refers to the sensor’s active available pixels. When reviewing an improvement in your set-up and understanding where the fault may lie, it’s important to consider how many pixels you have available for image processing relative to the size of the defect or computation you are trying to achieve. For example, if you are trying to defect a 100 micron (0.1mm) x 100 microns (0.1mm) inclusion on the surface of a product, but the surface is 4m x 4m in size, by using a standard 5MP camera (say 2456 x 2054 pixels) you will only be able to resolve a single pixel to at best 4000/2054 ~ so 1.9mm. This is nowhere near the level of detail you need for your imaging system. So to improve your machine vision system, see what size of a defect you need to see and then work out your field of view (fov) relative to the pixels available. But bear in mind having a single-pixel resolution for your measurement is not good enough, as the system will fail the Measurement Systems Analysis (MSA) and Gauge R&R studies (These are tests used to determine the accuracy of measurements).

In this case, you need to be at a minimum of 20 pixels across the measurement tolerance – so 0.2/20 = 0.01mm. Therefore, you will need 10 microns per pixel for accurate, repeatable measurement – over 4m; this is 400,000 pixels. So the 5MP pixel camera original specified will never inspect one shot – the camera or the object will have to be moved in small fields of view or larger resolution cameras specified for the system to hit the inspection criteria.

Step 3 Vision algorithm optimisation
One of the major steps for improving a machine vision system is a thorough review of the vision system software and the underlying sequence of inspection for the image processing system. If the system is a simple, intelligent vision sensor, it might be a matter of reviewing the way the logical flow of the system has been set. Still, for more complex PC-based vision applications, there will be some fundamental areas which can be reviewed and checked.

1. Thresholding. Thresholding is a simple look-up table that places pixel levels into a binary 1 and 0 depending on where they sit above, below or in-between two levels. The resulting image is a pure black and white image, then used to segment an area from the background from another object. Dynamic thresholding is also available, which intuitively sets the threshold level based on the pixel value distribution. But because it uses the fundamental pixel grey level to determine the segmentation, it can be easily affected by changes in light, as this will ultimately change the pixel value and lead to changes required to the threshold level. This is one of the most common faults in a system which has not been set up with a controlled lighting level. So if you are reviewing a threshold level change, you should also check the lighting and filters used on the system, as seen in Step 1.

2. Calibration. Perhaps not calibration in the true sense of the word, but if the camera on your vision system has been knocked out of place (even slightly), it may have detrimental effects on the inspection outcome and the yields of the vision system. Re-calibration may need to be done. This could initially be checked, as most modern systems have a cross-hair type target built into the software to allow a comparison image from the original system install to be compared with a live image. Check the camera is in-line, and the field of view is still the same. Focus and light should also be consistent. Some measurement-based machine vision systems will come shipped with a calibration piece or a Dot and Square Calibration Target. These generally come with a NIST Traceability Certificate, so you know that the artefact is traceable to national standards. This calibration target should be placed, and the system checked against it.

So these 3 simple steps to improve your machine vision system may just help you increase your vision system’s yield and stop the engineering manager from breathing down your neck. This will ultimately enhance your production productivity and advance the systems in use. And remember, you should always look at ways to reduce false failures in a vision system; this is another way to boost your vision system performance and improve yield.

What does every medical device manufacturer crave? Well, the delivery of the perfect part to their pharmaceutical customer, 100% of the time. One of the major stumbling blocks to that perfect score and getting a Parts Per Million (PPM) rate down to zero are the scratches, cracks and inclusions which can appear (almost randomly) in injection moulded parts. Most of the time, the body in question is a syringe, vial, pen delivery, pipette or injectable product which is transparent or opaque in nature, which means the crack, inclusion, or scratch is even more apparent to the end customer. Here we drill down on the top six defects you can expect to see in your moulded medical device and how vision systems are used to automatically find and reject the failure before it goes out of the factory door.

1. Shorts, flash and incomplete junctions.

Some of the failures are within the tube surface and are due to a moulding fault in production. For example, incomplete injections in mouldings create a noticeable short shot in the plastic ends of the product. The forming of junctions at the bottom end of the tube creates a deformation that must be analysed and rejected. Flash is characterised as additional material which creates an out of tolerance part.

2. Scratches and weld lines

These can be present on the main body, thread area or top shaft. They often have a slight depth to them and can be evident from some angles and less so from others. These could also be confused with weld lines which can run down the shaft or body of a syringe product, but this is also a defect that is not acceptable in production.

3. Cracks

Cracks in a medical device can have severe consequences to the patient and consumer; this could allow the pharmaceutical product to leak from the device, change the dosage or provide an unclean environment for the drug delivery device. Cracks could be in the main body or sometimes in the thread area, or around the sprue.

4. Bubbles, Black Spots and Foreign Materials

Bubbles and black spots are inclusions and foreign material, which are not acceptable on any product, but on a drug delivery device that is opaque or clear in nature, they stand out a lot! These sort of particulates are typically introduced through the moulding process and need to be automatically rejected. Often, the cosmetic nature for these sorts of inclusions is more the issue than the device’s functional ability based on the reject.

5. Sink marks, depressions and distortions

Sink marks, pits, and depressions can cause distortion in the components, thus putting the device out of tolerance or off-specification from the drawing. These again are caused by the moulding process and should be automatically inspected out from the batch.

6. Chips

Chips on the device have a similar appearance to short shots but could be caused on exit from the moulding or via physical damage through movement and storage. Chips on the body can cause out of tolerance parts and potential failure of the device in use.

But how does this automatic inspection work for all the above faults?

Well, a combination of optics, lighting and filters is the trick to identifying all the defects in medical plastic products. This combined with the ability to rotate the product (if it’s cylindrical in nature) or move it in front of many camera stations. Some of the critical lighting techniques used for the automated surface inspection of medical devices are:

Absorbing – the backlight is used to create contrasts of particulates, bubbles and cosmetic defects.
Reflecting – Illumination on-axis to the products creates reflections of fragments, oils and crystallisation.
Scattering – Low angle lighting highlights defects such as cracks and glass fragments.
Polarised – Polarised light highlights fibres and impurities.

These, combined with the use of telecentric optics (an optic with no magnification or perspective distortion), allows the product to be inspected to the extremities, i.e. all the way to the top and to the bottom. Thus, the whole medical device body can be examined in one shot.

100% automated inspection has come a long way in medical device and pharmaceutical manufacturing. Utilising a precision vision system for production is now a prerequisite for all medical device manufacturers.