How to use semi-automatic inspection of pharmaceutical vials and syringes for regulatory compliance.

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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.

3 simple steps to improve your machine vision system

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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.

How to automate Critical to Quality (CTQ) metrology checks

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You spend your days loading and unloading parts into the CMM and checking every dimension on the part. In orthopaedic joint and medical device manufacturing, this can be a time-consuming and sometimes necessary requirement for validated processes. Increasingly, though, manufacturers are looking at what the true value is to this, and if they should be inspecting only for the Critical to Quality (CTQ) dimensions, rather than 100% inspection of every dimension. This reduces the time needed for inspection, decreases the need for a costly individual CMM’s, and allows for multiple parts to be inspected on a single fixture at speed.

What is Critical to Quality?
Critical to quality (CTQ) is the quality of a product or service in the eyes of the voice of the customer (VOC). It is a useful to identify the critical to quality parameters as they relate to what is important to the customer overall. In general, the two most important factors are the very specific end-product parameters and the associated process parameters that determine the quality of the end-product. Once these parameters are identified, the quality department will want to monitor, control and continuously improve upon these CTQ parameters, but not necessarily every dimension on the product. Businesses may better understand and meet consumer wants and expectations – as well as improve the product quality and service they provide – by listening to the voice of the customer. To promote customer satisfaction and loyalty, these VOC programs recognise and respond to the voice of the consumer. Ultimately, the VOC leads down to the CTQ requirements in manufacturing.

Automating metrology inspection
CTQ dimensions can be specifically singled out, and vision metrology applied for automatic measurement and evaluation of components, rather than the traditional CMM route. Measurement programs for both roughness measurement and form measurement can be automated quickly and easily with vision systems. Vision inspection enables the automatic and user-independent measurement of micro-precision parts or smallest component features on large surfaces. Measurement of shape (distances, shape deviation, positional relationships…) and roughness parameters as well as of cutting edge parameters (radius, contour, angle…). This allows multiple nested products to be inspected at speed compared to the single CMM inspection route, allowing higher throughput and releasing engineers from the costly time for mounting and demounting products from a CMM. Machine vision measuring is used to autonomously confirm the dimensional accuracy of components, parts, and sub-assemblies without the need for operator intervention. Machine vision has the advantage of being non-contact, which means it does not contaminate or damage the part being inspected.

Factory information interfaces enable networking and communication with existing production and quality management systems according to the smart manufacturing production concept, thus all data and traceability is immediately available to engineering and quality. Allowing trends and spikes in quality to be seen quicker.

So in the future, don’t continue to mount a single product in a CMM and check products one by one – drill down on the CTQ and the VOC, and automate your metrology checks using automated vision inspection instead – to increase throughput, reduce overhead and ultimately be more productive.

The best way to check your return on investment when implementing machine vision.

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We’ve looked previously at the operational benefits of using machine vision but today we’re going to drill down on the economic benefits and how they fit in the payback you can expect from installing an automated visual inspection machine or system.

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 of 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. These five pillars of payback are critical in understanding the economic benefits of installing vision systems and are the best way to assess how machine vision can impact your organisations bottom line.

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 automated inspection 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
Costs can range from several hundred pounds for smart sensors and cameras, up to hundreds of thousands of pounds for complex IVS automated inspection machines. Of course, this will depend on the size and scope of your operations and specification – 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.

How to choose a camera for a machine vision application

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For the new year we thought it would be useful to go back to basics, to understand how to choose the correct camera for a machine vision application. With ever increasing options we want to drill down on the choices available and to understand why we use certain IVS cameras in our machines.

A camera’s ability to capture a correctly-illuminated image of the inspected object depends on the direct relationship between the sensor and the lens. The sensors job is to convert light (photons) from the lens into electrical signals (electrons). Typically, it will do this using either charge coupled device (CCD) or complementary metal oxide semiconductor (CMOS) technology which digitize the electrons into an image consisting of pixels. The image can then be sent to the processor for analysis. Low light creates dark pixels and bright light creates brighter pixels.

Parameters such as part size and inspection tolerances will help inform the required sensor resolution and it’s very important to ensure the right one is chosen for the required application. Higher degrees of accuracy in measurement will require higher levels of sensor resolution.

Depending on application requirements machine vision systems often consist of a number of cameras. These can be monochrome and/or colour, and capture, interpret and signal with the control system to deliver the required solution.

Line Scan (1D sensors)

Line scan cameras collect single lines of images and are typically used for inspecting moving objects which are transported past the camera. They can also be useful for moving, very large objects or cylindrical objects which can be rotated. Where colour information is important this can also be achieved with line scan cameras.

Since the camera is only capturing one line it can come with certain limitations. For example, illumination needs to be extremely precise and because the camera aperture needs to remain open most of the time, reducing the depth of field, it can make capturing objects at different distances more problematic.

Area Scan (2D sensors)

In area scan cameras the sensor has a large matrix of image pixels generating a two-dimensional image in one exposure cycle. Because area scan cameras are capturing a rectangular area it is typically easier to install high performance lighting with this set-up as opposed to line scan cameras.

How to choose a camera for a machine vision application.

Area scan cameras also make it possible to take short exposure images using strobe lighting that brings a lot of light to the sensor in a short period of time. As such area scan cameras are used in the majority of applications for image acquisition.

2D & 3D Imaging

3D machine vision helps to overcome complex, high accuracy, real-time challenges where the practical limitations of 2D vision means it simply can’t be used. In particular 2D Vision is limited in applications where:
● shape information is critical to performing a task
● there is sensitivity to lighting issues
● it is difficult to achieve high contrast levels
● object movement may impair image accuracy
● imaging three-dimensional shape, form or volume is a necessity
Of course, for many simple applications these issues are of no consequence and therefore 2D vision, using either line scan or area scan cameras, is perfectly acceptable.

How to choose a camera for a machine vision application.

However, 3D imaging is growing in importance within the machine vision industry. As outlined in previous posts, although it is more time, processor and software intensive, rapid advances in technology, algorithms and software mean that these systems are now more than capable of keeping up with production line throughput requirements.

How to choose a camera for a machine vision application.

Because they capture extra third dimension data reliably, 3D machine vision systems are immune to the aspects of lighting, contrast, and distance to object suffered by their 2D counterparts.

As such some of the key applications of 3D imaging include:
● Measurement of volume, height, thickness, holes, curves and angles
● Robot guidance, bin picking for placing, packing or assembly
● Quality control where 3D CAD models have been used
● Where understanding of 3D space and dimensions is required
● Inspecting low contrast objects

There are four separate commonly used methods for 3D imaging:
1. Scanning based triangulation describes the angular relationship between the object being scanned, the laser and the camera. The approach involves the projection of a laser onto the surface of an object which is then viewed by the camera from a different measured angle. Any deviations of the line represent shape variations and lines from multiple scans can be assembled into a depth map or point cloud, representing the 3D image. Often multiple cameras are used, tracking laser lines from different angles and then merging data sets into a single profile. This helps to overcome any issues with ‘shadowing’ a situation where a single laser line is blocked from passing through parts of an object, by other parts of the same object.
2. Stereo vision, as the name suggests, is based on using two cameras, much the same as a pair of human eyes. Using the triangulation technique the two captured 2D images are then used to calculate a 3D image.
3. Time of flight 3D cameras measure the time which a light pulse takes to reach the object and return for each image point. As a result they have limitations in respect to the distance they can be used from an object and resolution meaning they are only suitable for specific applications.
4. Structured light uses sophisticated light techniques to create structured patterns that encodes 3D information direct to the camera scene.

How to choose a camera for a machine vision application.

Whatever the task, it’s important to understand how all the elements of the machine vision system interact to create a robust and reliable solution. Starting with the camera is the best approach, and building up the system architecture around it.

11 ways machine vision is used in electric vehicle battery production

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Here at IVS our vision system solutions are utilised for the inspection of electric vehicle battery production. We thought it would be interesting in this post to drill down a little more of how machine vision is used in this fast-developing industry sector.

How are electric vehicle batteries made?

Carbon or graphite, a metal oxide, and lithium salt are all used to make lithium-ion batteries for electric vehicles. Positive and negative electrodes are made up of these elements, which when mixed with electrolyte form an electric current that allows the battery to work to power a car. It’s also the same type of battery that’s used in common devices like cell phones and computers, but on a far larger scale.

The materials used to make EV batteries come from many different countries and sources. Subterranean ponds are the most common source of lithium. The ponds liquid is drained out and left to dry in the sun. The Andes Mountains, which span through Chile, Argentina, and Bolivia, provides a large portion of the lithium used in electric car batteries. There are additional rock-mined deposits in China and the United States. The cobalt used in electric vehicle battery production mostly comes from mines in the Democratic Republic of Congo. Nickel is largely gathered in Indonesia and the Philippines. Lithium is converted to lithium carbonate, which is subsequently processed at a battery plant. The batteries are assembled at the production factory and then installed in an electric vehicle with zero emissions.

EV’s employ a pack, which is made up of thousands of separate Li-ion cells that work together, rather than a single battery like a phone. Electricity is utilised to make chemical changes inside the car’s batteries while it is charging. These adjustments are reversed when it’s on the road to generate electricity.

So how is machine vision used in a battery plant?

Machine vision is used in the complete electric vehicle (ev) manufacturing cycle, providing quality and consistency to the production across all areas. But getting the quality right on battery production for electric vehicles is critical for safety, life cycle and achieving greater energy density – and to prevent degradation and to minimise waste. Therefore, machine vision provides the eyes on quality in electrical vehicle manufacturing, providing 100% inspection, around the clock. From work we have done we can drill down on the 11 critical areas that machine vision is used in electric vehicle battery production for in-line quality control.

1. Coating quality inspection. During the initial coating process, linescan vision inspection is used to check for defects such as scratches, dents, dints, craters, bubbles, inclusions and holes on electrode sheets.

2. End face profile measurements. The end profiles can be continually monitored to quality assess the black electrode coating process and raise alarms in case of faults identified.

3. Coating width measurement. The anode and cathode coating has to be extremely consistent and to measurement specification. Therefore, surface inspection combined with gauging width and edge profiles helps to build up an inspection profile for the continuous coated product.

4. Electrode tab position and surface verification. During vacuum drying, a separator and electrode are brought together in cell construction. Cathode and anode cells are wrapped, rolled, or stacked together. The folded cells have lead tabs attached to them. When the cells have been loaded with electrolytes, vacuum-sealed, and dried, the procedure is complete. This process is monitored by vision inspection for anomalies and out of tolerance product. Critical to quality (CTQ) parameters are assessed in real-time.

5. Battery module defect detection. Each battery module will generally contain a number of cells (typically twelves). The modules are joined together and a cooling fluid pipe is attached. Checks for verification for module integrity, assembly characteristics and component verification are all completed using machine vision.

6. Stacking alignment and height. As modules and battery slices are built up into a complete battery pack, vision sensors measure the profile of the slice displacement and positioning to provide accurate feedback control for precision stacking.

7. Tab inspection. The tabs on the edge of each slice and subsequent modules are checked for debris, chips and cracks. Any small burr, edge deviation or dent can cause issues for the final assembled battery unit.

8. Connector Inspection. The main entry and exit to the battery module is via a high-voltage connector. The battery is charged through this connection, and electricity is delivered to the electric motor. Inspection of the main characteristics of the connector assembly are critical to provide a final check for edge deviations, male/female connector profiles and no cracks or dents in the connector profile.

9. Pouch surface inspection. Automated cosmetic inspection for inclusions, surface debris, scratches, dents and dints ensures that the lithium-ion cells are checked prior to becoming an EV battery.

10. Code reading. Codes on the battery modules need to be read for traceability and to track each element through the production process, allowing the manufacturing to trace where an EV cell is finally installed, from the individual production plant, down to the individual vehicle.

11. Final assembly verification. The final battery pack is checked for completeness to specification, all necessary assembly parts are available and verification of optical character recognition of codes for full traceability of the pack when sent to the customer for installation into the electric vehicle (EV).

For further details on IVS automotive industry solutions see: https://www.industrialvision.co.uk/industries/automotive

A guide to the different lenses used in industrial vision systems

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Optical lenses are used to view objects in imaging systems. Vision system lenses are imaging components used in image processing systems to focus an image of an examined object onto a camera sensor. Depending on the lens, they can be used to remove parallax or perspective error or provide adjustable magnifications, field of views, or focal lengths. Imaging lenses allow an object to be viewed in several ways to illustrate certain features or characteristics that may be desirable in certain applications. Alongside lighting, lens selection is paramount to creating the highest level of contrast between the features of interest and the surrounding background. This is fundamental to achieving the greatest possible image quality when working with vision systems.

In this respect the lens plays an important role in gathering light from the part being inspected and projecting an image onto the camera sensor, referred to as primary magnification (PMAG). The amount of light gathered is controlled by the aperture, which is open or closed to allow more or less light into the lens and the exposure time, which determines how long the image is imposed onto the sensor.

The precision of the image depends on the relationship between the field of view (FOV) and working distance (WD) of the lens, and the number of physical pixels in the camera’s sensor.

FOV is the size of the area you want to capture.

WD is the approximate distance from the front of the camera to the part being inspected with a more exact definition taking into account the lens structure.

The focal length, a common way to specify lenses, is determined by understanding these measurements and the camera/sensor specifications.

The performance of a lens is analysed in reference to its modulation transfer function (MTF) which evaluates resolution and contrast performance at a variety of frequencies.

Types of lens

In order to achieve the highest resolution from any lens/ sensor combination the choice of the former has become an increasingly important factor in machine vision. This is because of the trend to increasingly incorporate smaller pixels on larger sensor sizes.

An ideal situation would see the creation of an individual lens working for each specific field of view, working distance and sensor combination but this highly custom approach is impractical from a cost perspective.

As a result there are a multitude of optical/lens configurations available with some of the key varieties shown below:

Lens Type Characteristics Applications
Standard resolution lenses
  • sensor resolution of less than a megapixel
  • fixed focal lengths from 4.5 – 100 mm
  • MTF of 70 – 90 lp/mm
  • low distortion
  • most widely used
High resolution lenses
  • focal lengths up to 75 mm
  • MTF in excess of 120 lp/mm
  • very low distortion (<0.1%)
  • cameras with a small pixel size
  • precise measurement
Macro lenses
  • small fields of view approximately equal to camera’s sensor size
  • very good MTF characteristics
  • negligible distortion
  • lack flexibility – not possible to change iris or working distance
  • optimised for ‘close-up’ focusing
Large format lenses
  • required when camera sensor exceeds that which can be accommodated with C-mount lenses
  • often modular in construction including adapters, helical mounts and spacers
  • most commonly used in line scan applications
Telecentric lenses
  • collimated light eliminates dimensional and geometric image variations
  • no distortion
  • images with constant magnification and without perspective distortion whatever the object distance
  • to enable collimation front aperture of lens needs to be at least as large as the field of view meaning lenses for large fields of view are comparatively expensive
  • specialist metrology
Liquid lenses
  • Change shape within milliseconds
  • enables design of faster and more compact systems without complex mechanics
  • MTBF in excess of 1 billion movements
  • Longer working life due to minimal moving parts
  • where there is a need to rapidly change lens focus due to object size or distance changes
360˚
  • view every surface of object with as few cameras as possible
  • complex image shapes
360˚ – pericentric lenses
  • specific path of light rays through the lens means that a single image can show detail from the top and sides of an object simultaneously
  • cylindrical objects
360˚ – Catadioptric lenses
  • sides of inspected object observed over wide viewing angle, up to 45° at max., making it possible to inspect complex geometries under convenient perspective
  • small objects down to 7.5 mm diameter
360˚ – Hole inspection optics
  • a large viewing angle >82°
  • compatible with a wide range of object diameters and thicknesses
  • viewing objects containing holes, cavities and containers
  • imaging both the bottom of a hole and its vertical walls
360˚ – Polyview lenses
  • provide eight different views of side and top surfaces of object
  • enables inspection of object features that would otherwise be impossible to acquire with a single camera

You need enough resolution to see the defect or measure to an appropriate tolerance. Modern vision systems use sub-pixel interpolation, but nothing is better than having whole pixels available for the measurement required. Match the right lens to the right camera. If in doubt, over specify the camera resolution, most machine vision systems can cut down the image size from the sensor anyway.

The 7 elements of a machine vision system.

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For today’s post we thought we’d take you back to the beginning. Not all customers have used machine vision or vision systems in their production process before, many will be new to machine vision. So it’s important to understand the basics of a vision inspection system and what the fundamentals of the overall system look like. This helps to understand how a vision inspection machine operates at a rudimentary level.

The components of a vision system include the following basic seven elements. Although each of these components serves its own individual function and can be found in many other systems, when working together they each have a distinct role to play. To work reliably and generate repeatable results it is important that these critical components interact effectively.

  • 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 digitizing 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.

It all starts with the part or product being inspected. This is because it is the part size, specified tolerances and other parameters which will help to inform the required machine vision solution. To achieve desired results the system will need to be designed so that part placement and orientation is consistent and repeatable.

A sensor, which is often optical or magnetic, is used to detect the part and trigger:

  • the light source to highlight key features and
  • the camera to capture the image

This part of the process may also include what is often referred to as ‘staging’. Imagine a theatre and this is the equivalent of putting the actor centre stage in the best possible place for the audience to see. Staging is often mechanical and is required to:

  • Ensure the correct part surface is facing the camera. This may require rotation if several surfaces need inspecting
  • Hold the part still for the moment that the camera or lens captures the image
  • Consistently put the part in the same place within the overall image ‘scene’ to make it easy for the processor to analyse.

Lighting is critical because it enables the camera to see necessary details. In fact poor lighting is one of the major causes of failure. For every application there are common lighting goals:

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

In this respect the positioning and type of lighting is key to maximise contrast of features being inspected and minimise everything else.

Of course, an integrated inspection machine will have all of these aspects already designed and taken care of within the scope of the quality inspection unit, but these are some just some of the basic elements which make up the guts of a machine vision system.

Human Vision v Machine Vision (and what the operational benefits really are)

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We get asked this all the time! What are the differences between using a human inspector and what can we expect from an intelligent vision system. Advances in artificial intelligence, in particular deep learning, are enabling computers to learn for themselves and so gaps in this area continue to decrease. But it’s still safe to say that vision systems work from logic, don’t get tired and don’t have an “off” day. And of course, some production processes are too high speed for an operator to inspect (such as in medical device and pharmaceutical manufacturing).

Some of the key characteristics in comparison between human and machine vision can be seen in the table below:

Human Vision Machine Vision
Speed The human visual system can process 10 to 12 images per second. High speed – hundreds to thousands of parts per minute (PPM)
Resolution High image resolution High resolution & magnification.
Interpretation Complex information. Best for qualitative interpretation of unstructured scene Follows program precisely. Best for quantitative and numerical analysis of a structured scene
Light spectrum Visible light – Human eyes are sensitive to electromagnetic wavelengths ranging from 390 to 770 nanometres (nm) requires additional lighting to highlight parts being inspected, but can record light beyond human visible spectrum. Some machine-vision systems function at infrared (IR), ultraviolet (UV), or X-ray wavelengths.
Consistency, reliability & safety Impaired by boredom, distraction & fatigue Continuous repeatable performance – 24/7
100% accuracy

As we can see in the table, machine vision offers some real benefits over human vision, and this is what an increasing number of industries are recognising and exploiting.

The operational benefits of machine vision

So, for the forward-thinking companies exploiting this technology, there are a number of operational benefits of machine vision including:

  • Increased competitiveness – higher productivity and output
  • Lower costs – Less downtime and waste, increased speed and error correction. Automatically identify and correct manufacturing problems on-line with machine vision forming part of the factory control network
  • Improved product quality – 100% quality check for maximum product quality
  • Improved brand reputation management – More stringent compliance with industry regulations, leading to fewer product recalls and a reduced number of complaints
  • Customer complaint handling process improvements – Image archiving and recording throughout the whole process
  • Increased safety – machine vision solutions contribute to a positive, safe working environment
  • Improvements to sustainable working practices – machine vision can improve the use of energy and resources, make material flow smoother, prevent system jams, reduce defects/waste and even save on space. It can also help to enable Just In Time processes through tracking and tracing products and components throughout the production process, avoiding component shortages, reducing inventory and shortening delivery time.
  • Higher innovation – Releasing staff from manual, repetitive tasks for higher value work can lead to greater creativity and problem-solving.

Limitations

Whilst it’s hopefully clear that there are many benefits to machine vision, there are also some limitations. Machine vision systems are able to deliver results equal to and often above human performance with greater speed, continuity and reliability over a longer time period.

However, we should remember that the way the machine ‘sees’ can only be as good as the conditions and rules that are created to enable it to do so. In this respect, it is important that the following basic rules are observed:

  • The inspection task has been described precisely and in detail, in a way appropriate for the special characteristics of machine ‘‘vision’’.
  • All permissible variants of test pieces (with regard to shape, colour, surface etc.) and all types of errors have been taken into account.
  • The environmental conditions (illumination, image capturing, mechanics etc.) have been designed in such a way that the objects or defects to be recognised stand out in an automatically identifiable way.
  • These environmental conditions are kept stable.

In a related point, machine vision can be limited by the relationship between the amount of data required to help train it and the processing power needed to efficiently process the data. However, deep learning, which enables machines to learn rather than train, requires much less data and so provides an opportunity for progress in this respect.

Top 6 medical device manufacturing defects that a vision system must reject

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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.

What are false failures in machine vision (and the art of reducing them.)

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Over the last twenty plus years in the machine vision industry we’ve learnt a lot about customer expectation and the need to provide solutions which are robust, traceable and provide good value for money. One of the key elements of any installation or inspection machine is the mystical area of false failures of the vision system, and making sure this is reduced to the point of virtual elimination. A small change in the false failure rate in a positive direction can have a massive impact on yield, especially when the inspection takes place at 500 parts per minute!

For customer’s new to machine vision, it’s a concept they aren’t used to. The idea that you have to have a few false rejects events to happen in order to ensure that no false accepts take place. During the design phase of the vision set-up and inspection routine structure, we review a collection of “Good” parts as well as a pool of “Bad” parts. Statistically, this would imply an infinite number of each in order to calculate the set points for each measured feature. It is impossible to create an effective vision solution without this collection of components. We use various vision software methods to analyse all of the parts and features to create a collection of data sets using these good and bad parts.

This is where the system’s true feasibility will be determined. Once a vision tool has been created for any given feature, a population of data points will usually have some degree of uniqueness across the good and bad parts. If the data does not have two mutually exclusive distributions of results, some good parts must be rejected to ensure that no faulty parts are passed and allowed to continue to the customer. In other words, there is a cost to rejecting some good parts in order to ensure that a failed part is never sent to the customer.

You’ll notice that this chart explains the reasons for false rejections. More importantly, it emphasises that if a system is not rejecting good parts, it may be passing bad ones.

Of course, with all our years of experience in machine vision, we understand this issue – so all our systems and machines are designed so that there is enough difference between good and bad that the distributions are as distinct as possible, that’s the art. That is not so difficult to achieve in presence/absence type applications. It’s more difficult in gauging and metrology vision, where there’s some measurement uncertainty, and extremely difficult in surface flaw detection. Experience in the field of machine vision is everything when it comes to false failure reduction, and that’s what we provide you with our long-standing knowledge in vision systems.