Mixed Reality with Machine Vision, and what it means for automated visual inspection in production

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We are about to see a huge crossover in the application of Mixed Reality combined with automated machine vision applications. While they are two separate and distinct application areas in their own rights, we are now at a point where the two disciplines can be combined to aid automation and increase productivity in manufacturing.

What is Mixed Reality?

Mixed Reality combines the physical and digital realms. These two realities represent the opposites of a spectrum known as the virtuality continuum. This spectrum of realities is known as the mixed reality spectrum. On one end of the spectrum is the physical reality of our existence as humans. On the opposite end of the spectrum, we have digital reality. In layman’s terms, it’s the ability to mix what you perceive and see in front of you with a digital map or projection into your line of sight. So it’s the ability of a system to display digital objects as if they existed in the real world. You can imagine the heads-up displays (HUD) from fighter jets as the original pre-cursor to the current mixed reality that allowed pilots to see a projection in front of them. The difference is that mixed reality is now carried with the user, providing a real-time and immersive experience. Mixed reality experiences result from serious technical advances in the last few years.

What is Mixed Reality in production?

Let’s drill down on the hardware – one of the key players in this market is the Microsoft HoloLens 2 headset. This mixed reality unit is designed to operate untethered (no cables) from the control. It’s a self-contained holographic device which allows industrial applications to be loaded directly into the unit, thus allowing for complex mixed reality industrial operations. It has a see-through display; this allows the user to see the physical environment while wearing the headset (as compared to virtual reality).

An operator, technician or engineer will typically put on the unit at the start of a shift. It has a screen in front of the line of sight. This allows for instructions, graphics, prompts and signs to be projected directly onto the engineer’s vision allowing a mixed reality industrial environment. The unit also has multiple camera units for eye tracking and can broadcast the video the user sees to a remote location. All these aspects are combined to give a mixed and augmented reality experience.

How can Mixed Reality be applied in industry?

Mixed reality can be applied in several different ways. Let’s drill down on some of the application areas it could be used for.

– Task Guidance. Imagine you have a machine on the other side of the world and this machine has a fault. Your engineers can assist remote workers by walking them through issues as if standing in front of the device themselves.

– Remote Inspection. The ability to inspect a remote area for maintenance, damage or repair requirements. It reduces the cost of quality assurance and allows experts to assess issues from a distance immediately.

– Assembly Assistance. The ability to assist remote workers by walking through complex tasks using animated prompts and step-by-step guides. The ability to project drawings, schematics and 3D renders of how the assembly will look will help to facilitate mixed reality use in manufacturing.

– Training. Industrial training courses can be integrated directly onto the mixed reality headset. This is especially important when manufacturers rely on transitory contract workers who need training repeatedly.

– Safety. Improvement in health and safety training and situational awareness. Project prompts and holograms relating to safety in the workplace to guide workers in factory environments.

Mixed Reality assembly and machine vision.

Machine vision in industrial manufacturing is primarily concerned with quality control, i.e. verifying a product through automated visual identification that a component, product or sub-assembly is correct. This can be related to measurement, presence of an object, reading a code or verifying print. Combining augmented mixed reality with the automated machine vision operation provides a platform to boost productivity.

Consider an operator/assembly worker sitting in front of a workbench with wearable tech such as the HoloLens. An operator could be assembling a complex unit with many parts. They can see the physical items around them, such as components and assemblies. Still, they can also interact with digital content, such as a shared document that updates in real-time to the cloud or instruction animations for assembly. That is, in essence, the promise of mixed reality.

The mixed reality unit provides animated prompts, 3D projections and instructions to walk the operator step-by-step through the build. With this, a machine vision camera is mounted above the operator, looking down on the scene. As each sequence is run and a part assembled, the vision system will automatically inspect the product. Pass and fail criteria can be automatically projected into the line of sight in the mixed reality environment, allowing the operator to continue the build knowing the part has been inspected. In the case of a rejected part, the operator receives a new sequence “beamed” into their projection, with instructions on how to proceed with the failed assembly and where to place it. Now the mixed reality is part of the normal production process when combined with machine vision inspection. Data, statistics and critical quality information from the machine vision system are all provided in real-time in front of the operator’s field of view.

What’s the future of Mixed Reality and Machine Vision?

Imagine the engineering director walking the production line. Though now he wears a mixed reality unit, each machine vision system has a QR code “anchor” on the outside of the machine. By looking in that direction, the engineering manager has all the statistics beamed in front of him of the vision system operation. Yield, parts inspected, reasons for failure, real-time data and even an image of the last part that failed through the system. Data combined with graphical elements all allow better control of yield and ultimately the factory’s productivity. Machine vision systems will have integrated solutions for communication directly with wearable technology. Coupled with this, some integrated vision system solutions will have specific needs for operators to direct image capture for artificial intelligence image gathering.

And perhaps ultimately, such a mixed reality unit becomes part of the standard supply of a machine vision inspection machine to help with not just the running of the machine but to facilitate faster back-up, maintenance and training of such machine vision systems.

How robotic vision systems and industrial automation can solve your manufacturing problems

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In this post we’re going to drill down on robotic vision systems and how in the context of industrial automation they can help you boost productivity, increase yield and decrease quality concerns in your manufacturing process.

The question of how you can increase productivity in manufacturing is an ongoing debate, especially in the UK where productivity has been dampened over the last years compared to other industrialised nations. The key to unlocking and kickstarting productivity in manufacturing (output per hour worked) is the increased use of automation, to produce more, with less labour overhead. This is in the form of general industrial automation and robotic vision systems, by combining these two disciplines manufacturers can boost productivity and become less reliant on transient labour and a temporary workforce who requires continuous training and replacement.

So where do you start? Well, a thorough review of the current manufacturing process is a good place to begin. Where are the bottlenecks in flow? What tasks are labour-intensive? Where do processes slow down and start up again? Are there processes where temporary labour might not build to your required quality level? What processes require a lot of training? Which stations could easily lend themselves to automating?

When manufacturers deploy robot production cells, there can be a worry that you are losing the human element in the manufacturing process. How will the robot spot a defect when no human is involved in the production process now? We always have the quality supervisor and his team checking our quality while they built it, who’s going to do it now? The robot will never see what they did. And they mark a tally chart of the failures, we’re going to lose all our data!

All these questions go on in the mind of the quality director and engineering manager. So the answer is to integrate vision systems at the same time as the deployment of robotic automation. This can be in the form of robotic vision or as a separate stand-alone vision inspection process. This guarantees the process has been completed and becomes the eyes of the robot. Quality concerns relating to the build level, presence of components, correct fitment of components and measuring tolerances can all be solved.

For example, a medical device company may automate the packing of syringe products. Perhaps the medical syringes are pre-filled, labelled and placed into trays, all with six-axis robots and automation. A machine vision system could be used following all these tasks to check the pre-fill syringe level is correct, that all components in the plastic device are fitted in the correct position and finally an automated metrology check to confirm the medical device is measured to tolerance automatically. And remember the quality supervisor and his team also created a tally chart of failures, well with modern vision systems you’ll get all the data automatically stored, batch information, failure rates, yield, as well as the images of every product before they went out the door for information and warranty protection.

So how can robots replace operators? It normally comes down to some of the simple tasks, moved over to robots, including:

  • Robot Inspection Cells – robotics and vision combine to provide fully automated inspection of raw, assembled or sub-assemblies, replacing the tedious human inspection for the task.
  • Robot Pick and Place – the ability for the robot to pick from a conveyor, at speed, replacing the human element in the process.
  • Automated Bin Picking – By combining 3D vision with robot control feedback, a robot can autonomously pick from a random bin to allow parts to be presented to the next process, a machine tool loaded or even for handing to a human operator to increase the takt time for the line.
  • Robot construction and assembly. A task which has been done for many years by robots, but the advent of vision systems allow the quality inspection to be combined with the assembly process.
  • Industrial Automation – using vision systems to poke yoke processes to deliver zero defects forward in the manufacturing process, critical in a kanban, Just-in-Time (JIT) environment.
  • Robot and Vision Packing – picking and packing into boxes, containers and pallets using a combination of robots and vision for precise feedback. Reducing the human element in packing.

So, maybe you can deploy a robotic vision system and industrial automation into your process to help solve your manufacturing problems. It’s a matter of capital investment in the first place in order to streamline production, increase productivity and reduce reliance on untrained or casual workers in the manufacturing process. Robotic vision systems and industrial automation can solve your manufacturing problems.

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 elevate Statistical Process Control (SPC) in vision system inspection (and how to connect to Industrial IoT!)

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It’s the Production Managers dream in the era of Industry 4.0. A system that allows them to drill down on the assets in the production chain quickly and have immediate data on the throughput, results, and SPC data. This is all part of a goal to intelligently connect a company’s people, processes, places and assets to drive value across the whole organisation. That’s the goal of Industrial IoT (IIoT). And that’s what every manufacturer is striving for, having immediate, intelligent and critical data available on every process. This helps increase the efficiency of all manufacturing operations and allows plant-wide measuring and monitoring of machines’ quality, performance, and availability. Producers benefit from higher OEE and throughput while maintaining guaranteed levels of quality and cost.

The value of IIoT is that it immediately provides visibility of assets in a plant or in the field, and it transforms how a firm connects to the internet of things. The process of converting a factory to IIoT is to collect usage and performance data from existing systems and smart sensors, delivering advanced insights to unlock data-driven intelligence. Vision systems are an essential part of the manufacturing process. They have a significant impact on output and deliverables, so these are usually one of the first components connected to the factory information system. Companies are now establishing collaborative cultures in which engineers can make data-driven decisions that provide true economic benefit.

A fundamental part of machine vision inspection data is Statistical Process Control (SPC). Statistical Process Control (SPC) is a method for monitoring and controlling quality during the manufacturing process that is widely used in industry. During production, quality data in the form of Product or Process measurements are obtained in real-time. This information is then (at the very basic level) plotted on a graph with pre-set control limits. Control limits are set by the process’s capabilities, whereas the client’s requirements determine specification limits. Having this data connected to the IIoT system allows for more detailed analysis and alarms connected based on the moving data.

But how can I connect the vision system to the IIoT system?
There are many options. There are a large number of ways a vision system can send data to another system. This depends on the data (images, measurement data, settings, region of interest data etc.) required to be collected. Typically, the machine vision system will support a plug-in facility or a format convertor which allows the customisation of outputs to the IIoT system. But there are several immediate solutions that most vision systems will provide “out-of-the-box”, including
Digital I/O (small amount of data)
Fieldbus
PLC standard comms (industrial ethernet)
SQL Databases (SQL Server, SQLite, MySQL)
XML output
TCP/IP (traditional TCP/IP versus Industrial Ethernet)
Reports (Excel, PDF, Word)
Web API’s
In addition, many systems now communicate on an industrial basis using:
MQTT
SignalR
Web Sockets
UDP
The vision system may have a role to play in closed-loop control with the production process, allowing for moving parameters of a process to be controlled by the information returned from the vision system. This could already directly connect to the PLC or line controller for the vision system, e.g. by industrial ethernet. In this case, pulling the data into the factory IIoT may be as simple as communicating the machine PLC data to the factory server. It can be collated with the overall factory statistics and information into the global IIoT information exchange and display.

So next time you get a call from the production manager asking for a status report on the shop floor vision system and production statistics, just refer them to the company wide IIoT system, they’ll have all the data they need!

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

Robot vision trends in medical device production

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Medical device manufacturing continues to drive the adoption of robot vision to increase throughput, increase yield and reduce the reliance on manual processes. Many production facilities have now turned to collaborative robots which allow operations in medical device production to be taken to the next level of optimisation.

Cobots with vision, tend to work hand in hand with humans balancing the imperative for safety with the need for flexibility and productivity. Robots no longer need to work alone. Collaborative robots are generally designed with inherent safety so they can work alongside humans where possible. Collaborative automation means greater speed and efficiency. Of course, a thorough risk-assessment is still required but the functionality within these robots leads them to be assessed safe once the surrounding conditions and operationality capability are tuned to the safety needs (including any gripper or end-effector design).

Cobots are designed for low payload applications such as handling small parts and inspection tasks so are ideal for medical production processes. Companies continue to look for ways to improve their efficiency by using robot with vision systems, allowing automation of current manual processes to produce productivity gains. Cobots provide a lower price point and lower integration cost to the customer, providing a faster return on investment that unlocks many more industrial tasks for medical device production automation.

Some examples of the current trends in medical device manufacturing for adopting robot vision solutions include:

Machine Tending

Cobot vision removes the need for many operators to be occupied tending to production machinery, feeding and general assembly operations. The cobot can open and close a machine door, pick and place parts for assembly, and even start a machining process by pushing the start button.

Palletising

The ability to stack and un-stack pallets is another requirement, with the vision system providing real-time off-sets and alignment for the pallet stack. A compact robotic cell that accurately loads products onto a pallet, reduces the need for rework while also allowing staff to work safely around the robot. Using cobots with vision within a robot cell allows safe interaction with staff members. Personnel can enter the robot’s operational area to speed up the pallet changes, providing a robotic solution that can work around staff without risking their safety.

Box Erection

Taking flat boxes off a stack and erecting them into a useable format as outer shipper cases is a repetitive and time-consuming action in industry to which cobots with vision are very well suited. Medical device manufacturers need not only to erect the boxes and casings, but print and confirm the serial batch numbers and use by date on the product, all of which can be completed by the same machine vision inspection system.

Functional Repetitive Testing

Manufacturers need to put their products through thousands of hours of test cycling to provide reliability, for example the continued movement of a syringe body or an asthma inhaler valve. Collaborative robots with vision can performs hundreds of hours of testing to support faster testing, improving reliability and quality.

Structured Bin Picking

Parts in a structured position allowing single picks from known datums can be easily completed with cobots and smart vision sensors. Pick and place into a known fixture or position allows integration of such solutions in complex automation production processes.

Unstructured Random Bin Picking

Bin picking from a random box of parts can now be accomplished with cobots and 3D vision. A point cloud of known data, identifying individually pickable parts is fed to the robot allowing parts which would have previously required a human picker to now to picked and placed into the next process.

Robots with machine vision will continue to dominate the foreseeable needs for automation of production in medical device manufacturing. With the drive for ever greater flexibility and the need to reduce manpower and increase reliability, this area of automation will continue to be a focus for the medical devices and pharmaceutical industries.

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.