Tag: Vision Tips

This month, we are discussing one of the main, and sometimes underutilised, elements of vision systems – lighting. Lighting in machine vision quality control is perhaps one of the most important aspects to get right. It’s critical because it enables the camera to see necessary details. In fact, poor lighting is one of the major causes of failure of machine vision system installations and poor yield performance of a vision system. Therefore, it should be a top priority in determining the correct lighting approach for a machine vision application. While the latest generation AI deep learning vision systems can compensate to some extent for fluctuations in lighting, nothing will replace the ability to light a product accurately to allow defective areas to be seen by the camera. Getting these fundamentals right makes the job of the software image processing much more manageable.

For every vision quality control application, there are common lighting goals:

1. Maximising feature contrast of the part or object to be inspected.
2. Minimising contrast on features not of interest.
3. Removing distractions and variations to achieve consistency.

In this respect, the positioning and type of lighting are key to maximising the contrast of inspected features and minimising everything else. Think of how a manual operator would inspect a part for quality. Typically, they would turn it in front of the light while moving their head to see how the light reflects off of specific areas, slowly covering the whole part or necessary detail as required. Sometimes, even holding it up to the light to create a background to see contamination or features. A human does all this instinctively, but for a digital vision system, this somehow has to be replicated – and at high speed!

Lighting position

The first thing to consider is the lighting position. The position of the lighting will determine the brightness, shadowing, contrast, and what element of light will be seen by the camera system. The overall classification of machine vision lighting positions can be seen below.

Description	Characteristics	Features
Front illumination – the light is positioned on the same side as the camera/optics	Useful for inspecting defects or features on the surface of an object
Back illumination – the object being illuminated blocks light on its way to the camera	Useful for highlighting the object silhouette, measuring outline shape and detecting presence/absence of holes or gaps, as well as enhancing defects on transparent objects

Types of machine vision lighting

Within machine vision, the two most common types of lighting used are fibre optics and LED. Fibre optics allows the lighting source to be positioned away from the scene, with a fibre optic cable delivering the light to the vision system scene. This can also allow a higher lux light level to be concentrated into a smaller area.

Fibre Optic	Deliver light from halogen, tungsten‐halogen or xenon source. Bright, shapeable & focused.
LED lights	By far the most commonly used because: High speed Pulsing – Switched on and off in sequence, only using when necessary Strobing useful for freezing the motion of fast moving objects, to eliminate ambient light influence Flexible positioning into variety of shapes. Directional or diffused Long lifetime Higher Stable Output Colours – Available in visible and non visible
Dome lights	Designed to provide illumination coming from virtually any direction Avoid reflection and provide even light when inspecting surface features on an object with complex curved geometry
Telecentric lighting	Accurate edge detection and defect analysis through silhouette imaging Reflective objects High accuracy & precise measurement
Diffused light	Filters create contrast to lighten or darken object features Avoid uneven illumination on reflective surfaces
Direct light	Delivers lighting on the same optical path as the camera

Other key elements to consider when designing lighting for a machine vision environment include the direction, brightness and colour/wavelength compared to the target’s colour.

Lighting angle/direction

The position and angle of the LEDs have a direct effect on the ability of the vision system camera to see contrast in certain areas. This allows lighting setups to be designed to provide the optimum lighting conditions to highlight key areas of the product. This makes the image processing of the machine vision system image more consistent and makes it easier to provide a robust solution. There are several different techniques to think about in this area.

Bright field – Light reflected by the object being illuminated is inside of the optics/camera range	Brightfield front lighting – Light reflected by the flat object being illuminated is collected by the optics/camera. Produces dark characteristics on a bright background
	Brightfield back lighting – light is either stopped or if the material is transparent or opaque it will be transmitted through the surface
Dark field – Light reflected by the object being illuminated is outside of the optics/camera range	Dark field front lighting – reflected light is not collected by the optics/camera. Enhances the non-planar features of the surface as brighter characteristics on a dark background
	Dark field backlighting – light transmitted by the object and scattered by non-flat features will be enhanced as bright on a dark background
Co-axial illumination – The front light is positioned to hit the object surface at 90°	Can additionally be collimated so that rays are parallel to the optics/camera axis and is useful for achieving high levels of contrast when searching for defects on highly reflective surfaces

Colour/wavelength

Another aspect to think about when designing machine vision lighting is the filters which can be deployed on the front of the lens. These typically screw onto the front of the lens. They can limit the transmission of specific wavelengths of light, allowing the image to be changed to suit a particular condition that is being identified in the machine vision process.

Filters

Create contrast to lighten or darken object features. Like colour filters lighten (i.e. red light makes red features brighter) and opposite colour filters darken (i.e. red light makes green features darker). Polarizers are types of filters used to reduce glare or hot spots. They enhance contrast, so entire objects can be recognised.

Of course, the parameters outlined in the preceding tables are used in varying combinations and are key to achieving the optimal lighting solution. However, we also need to consider the immediate inspection environment. In this respect, the choice of effective lighting solutions can be compromised by access to the part or object to be inspected. Ambient lighting such as factory lights or sunlight can also have a significant impact on the quality and reliability of inspection and must be factored into the ultimate solution.

Finally, the interaction between the lighting and the object to be inspected must be considered. The object’s shape, composition, geometry, reflectivity, topography and colour will all help determine how light is reflected to the camera and the subsequent impact on image acquisition, processing, and measurement.

Due to the obvious complexities, there is often no substitute other than to test various techniques and solutions. It is imperative to get the best lighting solution in place before starting the development of the image processing requirements in any machine vision quality control application.

Choosing the most appropriate combination of components needed for a machine vision system requires expert understanding of application requirements and technology capabilities. From this perspective in this month’s blog post we thought we would provide you with enough knowledge and understanding to have richer more rewarding conversations about machine vision and vision systems technology. This quick guide will help you understand the basics of machine vision and vision systems, including components, applications, return on investment and potential for use in your business.

The first thing to understand are the basic elements of a machine vision system. We’ve covered this before in this post, but it’s good to re-iterate it here. The basics of a machine vision system are as follows:

• The machine vision process starts with the part or product being inspected.
  When the part is in the correct place, a sensor will trigger the acquisition of the digital image.
• Structured lighting is used to ensure that the image captured is of optimum quality.
• The optical lens focuses the image onto the camera sensor.
• Depending on capabilities, this digitising sensor may perform some pre-processing to ensure the correct image features stand out
  The image is then sent to the processor for analysis against the set of pre-programmed rules.
• Communication devices are then used to report and trigger automatic events, such as part acceptance or rejection.
• What can machine vision be used for?

The applications for machine vision vary widely between industries and product environments. However, to aid understanding, they can be broken down into the following five major categories. As outlined previously, in the section on ‘processor’, these usually combine a number of algorithms and software tools to achieve the desired result.

1. Location, guidance & positioning

When combined with robotics, machine vision offers powerful application options. By accurately determining the position of parts to direct robots, find fiducials, align products and provide positional adjustment feedback, guided robots enable a flexible approach to feeding and product changes. This eliminates the need for costly precision fixtures, allowing different parts to be processed without changing tools. Some typical applications include:

• Locating and orientation of parts to compare them to specific tolerances
• Finding or aligning parts so that they are positioned correctly for assembly with other components
• Picking and placing parts from bins
• Packaging parts on a conveyor belt
• Reporting part positioning to a robot or machine controller to ensure correct alignment

 

2. Recognition, identification, coding & sorting

Using state-of-the-art neural network pattern recognition to recognize arbitrary patterns, shapes, features and logos, vision systems enable the comparison of trained features and patterns within fractions of a second. Geometric features are also used to find objects and easily identify models that are translated, rotated and scaled to sub-pixel accuracy. Typical applications include:

• Decoding 1D & 2D symbols, codes and characters printed on parts, labels, and packages.
• Direct Part Marking (DPM) a character, code or symbol onto a part or packaging
• Identifying parts by locating a unique pattern or based on colour, shape, or size.
• Optical character recognition (OCR) and optical character verification (OCV) systems read alphanumeric characters and confirms the presence of a character string.

3. Gauging & measuring

Measuring in machine vision is used to confirm the dimensional accuracy of components, parts and sub-assemblies automatically, without the need for manual intervention. A key benefit with machine vision is that it is non-contact and so does not contaminate or damage the part being inspected. Many machine vision systems can also measure object features to within 0.0254 millimetres and therefore provide additional benefits over the contact gauge equivalent.

4. Inspection, detection & verification

Inspection is used to detect and verify functional errors, defects, contaminants and other product irregularities.

Typical applications include:

• Verifying the presence/absence of parts or components
• Counting to confirm quantities
• Detecting flaws
• Conformance checking to inspect products for completeness.

5. Archiving & Tracking

Finally, machine vision can play an important role in archiving images and tracking parts and products through the whole production process. This can be particularly valuable in tracking the genealogy of parts in a sub-assembly that make up a finished product. Data recorded can be used to drive better customer support, improve production processes and protect brands against costly product recalls.

What are the benefits of machine vision?

The economic case for investing in machine vision systems is usually strong due to the two key following areas:

1. Cost savings through reducing labour, re-work/testing, removing more expensive capital expenditure, material and packaging costs and removing waste

2. Increased productivity through process improvements, greater flexibility, increased volume of parts produced, less downtime, errors and rejections

However, just viewing the benefits from an economic perspective does not do justice to the true value of your investment. Machine vision systems can add value in all the additional following ways. Unfortunately due to the intangible nature of some of these contributors it can be difficult to put an actual figure on the value but that shouldn’t stop attempts to include them.

• Intellectually
  • By freeing staff from repetitive, boring tasks they are able to focus thinking in ways that add more value and contribute to increasing innovation. This is good for mental health and good for the business.
  • By reducing customer complaints, product recalls and potential fines machine vision can help to build and protect your brand image in the minds of customers
  • Building a strong image in the minds of potential business customers through demonstrating adoption of the latest technology, particularly when they come and visit your factory!
  • Through the collection of better data and improved tracking, machine vision can help you develop a deeper understanding of your processes
• Physically
  • The adoption of machine vision can help to complement and even improve health and safety practice
  • Removing operators from hazardous environments or strenuous activity reduces exposure to sickness, absence, healthcare costs or insurance claims
• Culturally
  • Machine vision can contribute and even accelerate a culture of continuous improvement and lean manufacturing
  • Through increased competitiveness and improving service levels machine vision helps build a business your people can be proud of
• Environmentally
  • Contributing to a positive, safe working environment for staff
  • Through better use of energy and resources, smoother material flow and reduced waste machine vision systems can help reduce your impact on the environment

The costs of machine vision systems?

Costs can range from several hundred for smart sensors and cameras, up to half a million for complex systems. Of course this will depend on the size and scope of your operations and may be more or less.

However, even in the case of high levels of capital investment it should be obvious, from the potential benefits outlined above, that a machine vision system can quickly pay for itself.

Sometimes, we’re tasked with some of the most complex machine vision and automated inspection tasks. In addition to our standard off-the-shelf solutions in the medical device, pharmaceutical, and automotive markets, we often get involved with the development of new applications for machine vision inspection. Usually, this consists of adapting our standard solutions to a client’s specific application requirement. A standard machine vision camera or system cannot be applied due to either the complexity of the inspection requirements, the mechanical handling, part surface condition, cycle time needs or simply the limitation to how the part can be presented to the vision system. Something new is needed.

This complex issue is often discussed in customer and engineering meetings, and the IVS engineering team often has to think outside the box and perhaps use some “magic”! This is when the optics (machine vision lenses) need to be used in a unique way to achieve the desired result. This can be done in several ways, typically combining optical components, mirrors, and lighting.

So what optics and lenses are used in machine vision?
The primary function of any lens is to collect light scattered by an object and reproduce a picture of the object on a light-sensitive ‘sensor’ (often CCD or CMOS-based); this is the machine vision camera head. The image captured is then used for high-speed industrial image processing. When selecting optics, several parameters must be considered, including the area to be imaged (field of view), the thickness of the object or features of interest (depth of field), the lens-to-object distance (working distance), the intensity of light, the optics type (e.g. telecentric), and so on.

Therefore, the following fundamental parameters must be evaluated in optics.
Field of View (FoV) is the entire area the lens can see and image on the camera sensor.
Working distance (WD) is the distance between the object and the lens at which the image is in the sharpest focus.
Depth of Field (DoF) is the maximum range over which an object seems to be in acceptable focus.
In addition, the vision system magnification (ratio of sensor size to field-of-view) and resolution (the shortest distance between two points that can still be distinguished as separate points) must also be appraised.

But what if the direct line of the site cannot be seen due to a limitation in the presentation or space available? This is when the magic happens. Optical path planning is used to understand the camera position (or cameras) relative to the part and angle of view required. Combining a set of mirrors in the line of site makes it possible to see over a longer working distance than possible without. The same parameters apply (FoV, WD, DoF etc), but a long optical path can be folded with mirrors to fit into a more compact space. This optical path can be folded multiple times using several mirrors to provide one continuous view through a long path. Cameras can also be mounted closely with several mirrored views to offer a 360-degree view of the product. This is especially true in smaller product inspections like pill or tablet vision inspection checks. Multiple mirrors allow all sides and angles of the pill to be seen, providing a comprehensive automated inspection for surface defects and imperfections for such applications. This technique is also applied in the subtle art of needle tip inspection (in medical syringes) using vision systems, with mirror systems to allow for a complete view of the tip from all angles, sometimes providing a single image with two views of the same product. The optical paths, the effect of specific machine vision lighting and the mechanical design must all be considered to create the most robust solution.

So when our engineers say they will be using “magic” optics, I no longer look for a wand to be waved but know that a very clever application of optics and mirrors will be designed to provide a robust solution for the automated vision system task at hand.

This month, we’re talking about the print you see on medical devices and in-vitro products used for the traceability and serialisation of the product – called the UDI or Unique Device Identification mark. In the ever-evolving world of healthcare, new niche manufacturers and big medical device industrial corporations find themselves at a crossroads. Change has arrived within the Medical Device Regulation, and many large and small companies have yet to strike the right chord, grappling with the choices and steps essential to orchestrate compliance to the latest specifications. Automated vision systems are required at every stage of the production and packaging stages to confirm compliance and adherence to the standards for UDI. Vision systems ensure quality and tracking and provide a visible record (through photo save) of every product stage during production, safeguarding medical devices, orthopaedic and in-vitro product producers.

What is UDI?

The UDI system identifies medical devices uniformly and consistently throughout their distribution and use by healthcare providers and consumers. Most medical devices are required to have a UDI on their label and packaging, as well as on the product itself for select devices.

Each medical device must have an identification code: the UDI is a one-of-a-kind code that serves as the “access key” to the product information stored on EUDAMED. The UDI code can be numeric or alphanumeric and is divided into two parts:

DI (Device Identifier): A unique, fixed code (maximum of 25 digits) that identifies a device’s version or model and its maker.

Production Identifier (PI): It is a variable code associated with the device’s production data, such as batch number, expiry date, manufacturing date, etc. UDI-DI codes are supplied by authorised agencies such as GS1, HIBCC, ICCBBA, or IFA and can be freely selected by the MD maker.

Adding to the regulatory landscape, the In-Vitro Diagnostic Regulation (IVDR) 2017/746 debuted on May 26th, 2022. This regulation seamlessly replaced the previous In-Vitro Diagnostic Medical Devices (IVDMD) regulation, further shaping the intricate framework governing in-vitro diagnostics. The concurrent implementation of MDR and IVDR ushers in a new era of compliance and adaptability for medical devices and diagnostics stakeholders.

What are the different classes of UDI?

Risk Profile	Notify or Self-Assessment	Medical Devices		In-Vitro Diagnostic Medical Devices
High Risk to Low Risk	Notified Body Approval Required	Pacemakers, Heart Valves, Implanted cerebral simulators	Class III	Class D	Hepatitis B blood-donor screening, ABO blood grouping
		Condoms, Lung ventilators, Bone fixation plate	Class IIb	Class C	Blood glucose self-testing, PSA screening, HLA typing
		Dental fillings, Surgical clamps, Tracheomotoy Tubes	Class IIa	Class B	Pregnancy self-testing, urine test strips, cholesterol self-testing
	Self-Assessment	Wheelchairs, spectacles, stethoscopes	Class I	Class A	Clinical chemical analysers, specimen receptacles, prepared selective culture media

Deadlines for the classification to come into force

Implementing the new laws substantially influences many medical devices and in-vitro device companies, which is why the specified compliance dates have been prioritised based on the risk class to which the devices belong. Medical device manufacturers must adopt automated inspection into their processes to track and confirm that the codes are on their products in time for the impending deadlines.

The Act recognises four types of medical devices and four categories of in-vitro devices, which are classified in ascending order based on the degree of risk they provide to public health or the patient.

	2023	2025	2027
UDI marking on DM	Class III		Class I
Direct UDI marking on reusable DM	Class III	Class II	Class I
UDI marking on in-vitro devices (IVD)	Class D	Class C & B	Class A

The term “unique” does not imply that each medical device must be serialised individually but must display a reference to the product placed on the market. In fact, unlike in the pharmaceutical industry, serialisation of devices in the EU is only required for active implantable devices such as pacemakers and defibrillators.

The “visible” format of the UDI is the UDI Carrier, which must contain legible characters in both HRI and AIDC formats. The UDI must be displayed on the device’s label, primary packaging, and all upper packaging layers representing a sealable unit.

These rules mean medical device manufacturers need reliable, traceable automated vision inspection to provide the track and trace capability for automatic aggregation and confirmation of the UDI process.

Optical Character Recognition (OCR)

Optical character recognition is based on the need to identify a character from the image presented, effectively recognise the character, and output a string based on the characters “read”. The vision system has no prior knowledge of the string. It therefore mimics the human behaviour of reading from left to right (or any other orientation) the sequence it sees.
You can find more information on OCR here.

Optical Character Verification (OCV)
Optical character verification differs from recognition as the vision system has been told in advance what string it should expect to “read”, and usually, with a class of characters it should expect in a given location. Most UDI vision systems will utilise OCV, as opposed to OCR, as the master database, line PLC (Programmable Logic Controller), cell PLC and laser/label printer should all know what will be marked. Therefore, the UDI vision system checks that the characters marked are verified and readable in that correct location (and have been marked). This then allows for the traceability through the process through nodes of OCV systems throughout the production line.
You can find more information on OCV here.

Print Quality Inspection (PQI)

Print quality inspection methods differ considerably from the identification methods discussed so far. The idea is straightforward: an ideal template of the print to be verified is stored; this template does not necessarily have to be taken from a physically existing ‘‘masterpiece’’, it could also be computer-generated. Next, an image containing all the differences between the ideal template and the current test piece is created. Simply, this can be done by subtracting the reference image from the current image. But applying this method directly in a real-world context will show very quickly that it will always detect considerable deviations between the reference template and test piece, regardless of the actual print quality. This is a consequence of inevitable position variations and image capture and transfer inaccuracies. A change in position by a single pixel will result in conspicuous print defects along every edge of the image. So, print quality inspection is looking for the difference between the print trained and the print found. Therefore, PQI will pick up missing parts of characters, breaks in characters, streaks, lines across the pack and general gross defects of the print.

You can find more information on PQI here.

What about Artificial Intelligence (AI) with OCR/OCV for UDI?

Where we’ve been discussing OCR and OCV, we’ve assumed that the training class is based either on a standard OCR font (a unique sans-serif font created in the early days of vision inspection to provide a consistent font, recognisable by humans and vision systems), or a trainable, repeatable fixed font. OCR with AI is a comprehensive deep-learning-based OCR (or OCV) method. This innovative technology advances machine vision towards human reading. AI OCR can localise characters far more robustly than conventional algorithms, regardless of orientation, font type, or polarity. The capacity to automatically arrange characters enables the recognition of entire words. This significantly improves recognition performance because misinterpreting characters with similar appearances is avoided. Large images can be handled more robustly with this technique, and the AI OCR result offers a list of character candidates with matching confidence ratings, which can be utilised to improve the recognition results further. Users benefit from enhanced overall stability and the opportunity to address a broader range of potential applications due to expanded character support. Sometimes, this approach can be used. However, it should be noted that it can make validation difficult due to the need for a data set prior to learning, compared to a traditional setup.

Do all my UDI vision inspection systems require validation?

Yes, all the vision systems for UDI inspection must be validated to GAMP/ISPE levels. Having the systems in place is one thing, but the formal testing and validation paperwork is needed to comply fully with the legislation. You can find more information on validation in one of our blog articles here.

Industrial vision systems allow for the 100% inspection of medical devices at high production speeds, providing a final gatekeeper to prevent any rogue product from exiting the factory gates. These systems, in combination with factory information communication and track & trace capability, allow the complete tracking of products to UDI standards through the factory process.

This month, we are talking about automated visual inspection of cosmetic defects in contact lens inspection, a major area of expertise in the IVS portfolio. Contact lens production falls between medical device manufacturing and pharmaceuticals manufacturing, but it does mean that it falls within the GAMP/ISPE/FDA validation requirements when it comes to manufacturing quality and line validation.

The use of vision technology for the inspection of contact lenses is intricate. Due to the wide variety of lens profiles, powers, and types (such as spherical, toric, aspherical etc.), manufacturers historically relied on human operators to visually verify each lens before it was shipped to a customer. Cosmetic defects in lenses range from scratches and inclusions, through to edge defects, mould polymerisation problems and overall deformity in the contact lens.

The Challenge: Manual Checks are Prone to Human-Error and are Slow!

The global contact lens industry must maintain the highest standards of quality due to the specialist nature of its products. Errors or faults in contact lenses can lead to negative recalls, financial losses, or a drop in brand loyalty and integrity – all issues that could otherwise be very difficult to rectify. However, manually checking lenses has become extremely time-consuming, inefficient, and error-prone. Due to the brain’s tendency to rectify errors naturally, human error is a common cause of oversights. This makes it next to impossible to spot faults by hand, therefore many errors are missed, and contact lens defects can be passed to the consumer.

Coupled with this, many global contact lens manufacturing brands run their production at high speed, which means using human inspectors simply becomes untenable based on the volume and speed of production; so in the last twenty-five years, contact lens manufacturers have moved to high-speed, high-accuracy automated visual inspection, negating the need for manual checks.

To ensure their products’ quality, accuracy, and consistency, many of the world’s top contact lens manufacturing brands have turned to IVS to solve their automated inspection and verification problems. This can be either wet or dry contact lens inspection, depending on the specific nature of the production process.

The Solution: Minimize Errors and Ensure Quality Through IVS Automated Visual Inspection

IVS contact lens inspection solutions have been developed over many years based on a deep and long heritage in contact lens inspection and the required FDA/GAMP validation. The vision system drills down on the specific contact lens defects, allowing finite, highly accurate quality checks and inspection coupled with the latest generation AI vision inspection techniques. This allows the maximum yield achievable, coupled with full reporting and statistical analysis, to give the data feedback to the factory information system, providing real-time, reliable information to the contact lens manufacturer. Failed lenses are rejected, allowing 100% quality inspected contact lenses to be packed and shipped.

IVS have inspection systems for manual load, automated and high-speed contact lens inspection, allowing our solutions to cover the full range of contact lens manufacturers – from small start-ups to the major high-level production volume producers. Whatever the size of your contact lens production, we have validated solutions to FDA/ISPE/GAMP for automated contact lens inspection.

Contact lens manufacturers now have no requirement for manual visual inspection by operators; this can all be achieved through validated automated visual inspection of cosmetic defects, providing high-yield, high-throughput quality inspection results.

More details: https://www.industrialvision.co.uk/wp-content/uploads/2023/05/IVS-Lens-Inspection-Solutions.pdf

Cosmetic defects are the bane of manufacturers across all industries. Producing a product with the correct quality in terms of measurement, assembly, form, fit, and function is one thing – but cosmetic defects are more easily noticed and flagged by the customer and end-user of the product. Most of the time, they have no detrimental effect on the use or function of the product, component or sub-assembly, they just don’t look good! They are notoriously difficult for an inspection operator to spot and are therefore harder to check using traditional quality control methods. The parts can be checked on a batch or 100% basis for measurement and metrology checks, but you can’t batch-check for a single cosmetic defect in a high-speed production process. So for mass production medical device manufacturing at 300-500 parts per minute, using vision systems for automated cosmetic defect inspection is the only approach.

From medical device manufacturers, cosmetic defects might manifest themselves as inclusions, black spots, scratches, marks, chips, dents or a foreign body on the part. For automotive manufacturers, it could be the wrong position of a stitch on a seat body, defects on a component, a mark on a sub-assembly, or the gap and flush of the panels on a vehicle.

Using vision systems for automated cosmetic inspection is especially important for products going to the Japanese and the Asian markets. These markets are particularly sensitive to cosmetic issues on products, and medical device manufacturers need to use vision systems to 100% inspect every product produced to confirm that no cosmetic defects are present on their products. This could be syringe bodies, vials, needle tips, ampules, injector pen parts, contact lenses, medical components and wound care products. All need checking at speed during the manufacturing and high-speed assembly processes.

How are vision systems applied for automated cosmetic checks?

The key to applying vision systems for cosmetic visual inspection is the combination of camera resolutions, optics (normally telecentric in nature) and filters to bring out the specific defect to provide the ability to segment it from the natural variation and background of the product. Depending on the type of cosmetic defect that is being checked, it may also require a combination of either linescan, areascan or 3D camera acquisition. For example, syringe bodies or vials can be spun in front of a linescan camera to provide an “unrolled” view of the entire surface of the product.

Let’s drill down on some of the specific elements which are used, using the syringe body as a reference (though these methods can be applied to other product groups):

Absorbing defects

These defects are typically impurities, particulates, fibers and bubble. This needs a lighting technique with linescan to provide a contrasting element for these defects:

Hidden defects

These defects are typically “hidden” from view via the linescan method and so using a direct on-axis approach with multiple captures from an areascan sensor will provide the ability to notice surface defects such as white marks (on clear and glass products) and bubbles.

Cracks and scratch defects

A number of approaches can be taken for this, but typically applying an axial illuminiation with a combination of linescan rotation will provide the ability to identify cracks, scratches and clear fragments.

Why do we use telecentric optics in cosmetic defect detection?

Telecentric lenses are optics that only gather collimated light ray bundles (those that are parallel to the optical axis), hence avoiding perspective distortions. Because only rays parallel to the optical axis are admitted, telecentric lens magnification is independent of object position. Due to this distinguishing property, telecentric lenses are ideal for measuring and cosmetic inspection applications where perspective problems and variations in magnification might result in inconsistent measurements. The front element of a telecentric lens must be at least as large as the intended field of view due to its construction, rendering telecentric lenses insufficient for imaging very large components.

Fixed focal length lenses are entocentric, catching rays that diverge from the optical axis. This allows them to span wide fields of view, but because magnification varies with working distance, these lenses are not suitable for determining the real size of an item. Therefore, telecentric optics are well suited for cosmetic and surface defect detection, often combined with collimated lighting to provide the perfect silhouette of a product.

In conclusion, machine vision systems can be deployed effectively for 100% automated cosmetic defect detection. By combining the correct optics, lighting, filters and camera technology, manufacturers can use a robust method for 100% automated visual inspection of cosmetic and surface defects.

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.