Tag: Innovation

This month, we’re following on from last month’s post.
We’ve all seen films showing the dystopian factory landscape a few hundred years in the future. Flying cars against a grey backdrop of industrial scenery. Robots serving customers at a fictitious future bar, and probably – if they showed a production line, it would be full of automatons and robots. We will drill down on the role that industrial vision systems can have on the real factory production line. Is it the case that in the future, all production processes will be replaced with automated assembly, coupled with real-time quality inspection? Intelligent robots which can handle every production process?

Let’s start with what’s happening now, today. It’s certainly the case that machine vision has developed rapidly over the last thirty years. We’ve gone from slow scientific image processing on a 386 computer (look it up!) to AI deep learning running at hundreds of frames per second to allow the real-time computation and assessment of an image in computer vision. But this doesn’t mean that every process has (or can be) replaced with industrial vision systems, but what’s stopping it from being so?

Well, the main barrier to this is the replication of the human dexterity and ability to manoeuvre the product in delicate (compared to an industrial automated process) ways. In tandem with this is the ability for a human to immediately switch to a different product mix, size and type of product. So you could have a human operator construct a simple bearing in a matter of seconds, inspect it – and then ask them to build next a completely different product, such as a small medical device. With some training and show how, this information exchange can be done and the human operator adapts immediately. But a robot would struggle. Not to say it couldn’t be done, but with today’s technology, it takes effort. This is the goal of Industry 5.0, the next step generation from Industry 4.0 flexible manufacturing. Industry 5.0 is a human-centric approach, so industry understanding that humans working in tandem with robotics, vision, and automation is the best approach.

So we need to look at industrial vision in the context of not just whether the image processing can be completed (be it with traditional algorithms or AI deep learning), but also how we can present the product meaningfully at the correct angles to assess it. Usually, these vision inspection checks are part and parcel with an assembly operation, which requires specific handling and movement (which a human is good at). This is the reason most adoption of machine vision happens on high throughput, low variation manufacturing lines – such as large-scale medical device production where the same product is validated and will be manufactured in the same way over many years. This makes sense – the payback is there and automation can be applied for assembly and inspection in one.

But what are the drivers for replacing people on production lines with automated vision inspection?
If the task can be done with automation and vision inspection, it makes sense to do it. Vision inspection is highly accurate, works at speed and is easy to maintain. Then there are the comparisons comparing the processes to a human operator. Vision systems don’t take breaks, don’t get tired, and robots don’t go to parties (I’ve never seen a robot at a party) – so they don’t start work in the morning with their minds not on the task at hand! So, it makes sense to move towards automated machine vision inspection wherever possible in a production process, and this represents huge growth in the adoption of industrial vision systems in the coming years.

If the job is highly complex in terms of intricate build, with many parts and variants, then robots, automation and vision systems are not so easy to deploy. However, with the promise of Industry 5.0, we have the template of the future – moving towards an appreciation of including the human operator in factory automation – combining humans with robots, automation, augmented reality, AI and automated inspection. So, the dystopian future might not be as bad as the filmmakers make out, with human operators still being an integral part of the production process.

This month, we are discussing the technological and cultural shift in the automotive manufacturing environment caused by the advent of robotics and machine vision, coupled with the development of AI vision systems. We also drill down on the drivers of change and how augmented reality will shape the future of manufacturing technology for automotive production.

Robots in Manufacturing
Robots in manufacturing have been used for over sixty years, with the first commercial robots used in mass production in the 1960s – these were leviathans with large, unwieldy pneumatic arms, but they paved the way for what was to come in the 1970s. At this time, it was estimated that the USA had 200 such robots [1] used in manufacturing; by 1980, this was 4,000 – and now there are estimated to be more than 3 million robots in operation [2]. During this time the machine vision industry has grown to provide the “eyes” for the robot. Machine vision uses camera sensor technology to capture images from the environment for analysis to confirm either location (when it comes to robot feedback), quality assessment (from presence verification through to gauging checks) or simply for photo capture for warranty protection.

The automotive industry is a large user of mass-production robots and vision systems. This is primarily due to the overall size of the end unit (i.e. a built car), along with vision systems for confirmation of quality due to the acceptable parts per million failure rate (which is extremely low!). Robots allow repetitive and precise assembly tasks to be completed accurately every time, reducing the need for manual labour and providing a faster speed for manufacturing.

Automating with industrial robots is one of the most effective ways to reduce automotive manufacturing expenses. Factory robots help reduce labour, material, and utility expenses. Robotic automation reduces human involvement in manufacturing, lowering wages, benefits, and worker injury claims.

AI Machine Vision Systems
Deep learning in the context of industrial machine vision teaches robots and machines to do what comes naturally to humans, i.e. to learn by example. New multi-layered “bio-inspired” deep neural networks allow the latest machine vision solutions to mimic the human brain activity in learning a task, thus allowing vision systems to recognise images, perceive trends and understand subtle changes in images that represent defects. [3]

Machine vision performs well at quantitatively measuring a highly structured scene with a consistent camera resolution, optics and lighting. Deep learning can handle defect variations that require an understanding of the tolerable deviations from the control medium, for example, where there are changes in texture, lighting, shading or distortion in the image. Deep-learning vision systems can be used in surface inspection, object recognition, component detection and part identification. AI deep learning helps in situations where traditional machine vision may struggle, such as parts with varying size, shape, contrast and brightness due to production and process constraints.

Augmented Reality in Production Environments
In industrial manufacturing, machine vision is primarily concerned with quality control, which is the automatic visual identification of a component, product, or subassembly to ensure that it is proper. This can refer to measurement, the presence of an object, reading a code, or verifying a print. Combining augmented, mixed reality with automated machine vision operations creates a platform for increased efficiency. There is a shift towards utilising AI machine vision systems (where applicable!) to improve the reliability of some quality control checks in vision systems. Then, combine that assessment with the operator.

Consider an operator or assembly worker sitting in front of a workstation wearing wearable technology like the HoloLens or Apple Vision Pro (an augmented reality headset). An operator could be putting together a sophisticated unit with several pieces. They can see the tangible objects around them, including components and assemblies. They can still interact with digital content, such as a shared document that updates in real time to the cloud or assembly instructions. That is essentially the promise of mixed reality.

The mixed-reality device uses animated prompts, 3D projections, and instructions to guide the operator through the process. A machine vision camera is situated above the operator, providing a view of the scene below. As each step is completed and a part is assembled, the vision system automatically inspects the product. Pass and fail criteria can be automatically projected into the operator’s field of sight in the mixed reality environment, allowing them to continue building while knowing the part has been inspected. In the event of a rejected part, the operator receives a new sequence “beamed” into their projection, containing instructions on how to proceed with the failed assembly and where to place it. When integrated with machine vision inspection, mixed reality has become a standard aspect of the production process. Data, statistics, and important quality information from the machine vision system are shown in real time in the operator’s field of view.

Robots, AI Vision Systems and Augmented Reality
Let’s fast forward a few years and see what the future looks like. Well, it’s a combination of all these technologies as they continue to develop and mature. So AI vision systems mounted on cobots help with the manual assembly, while the operator wears an augmented reality headset to direct and guide the process for an unskilled worker. Workers are tracked while all tasks are being quality assessed and confirmed, while data is stored for traceability and warranty protection.

In direct automotive manufacturing, vision systems will become easier to use as large AI deep-learning datasets become more available for specific quality control tasks. These datasets will continue to evolve as more automotive and tier one and two suppliers use AI vision to monitor their production, allowing quicker deployment and high-accuracy quality control assessment across the factory floor.

References

The shadowgraph used to be the preferred method, or perhaps the only method for the quality control department to check the adherence to measurement data. These typically vertical projectors have a plate bed, with parts laid on it, and the light path is vertical. This allows a silhouette and profile of the part to be shown to the operators as an exact representation of the part in black, but projected at a much higher magnification. These projectors are best for flat, flexible parts, washers, O rings, gaskets and parts that are too small to hold, such as very small shafts, screws etc. They are also used in orthopedic manufacturing to view profiles and edges of joint parts.

So, this old-fashioned method involves projecting a silhouette of the edge of the product or component. This projection is then overlaid with a clear (black-lined) drawing of the product showing some additional areas where the component shouldn’t stray into. The operator chooses the overlay from a bunch available, based on the part number of the part. This is laid over the projection and manually lined up with the tolerances shown as a thick band in key areas for quality checking. For example, if you’re an orthopedic joint manufacturer and are coating your product with an additive material, you might use a shadowgraph to check the edges of the new layer. The shadowgraph will project a magnified representation of the component to compare against.

Some optical comparators and shadowgraphs have developed to a level where they are combined with a more modern graphical user interface (GUI) and are digital to make overlays easier to check, coupled with the ability to take manual measurements.

Ultimately though, all these types of manual shadowgraphs take time to use, are manually intensive and are totally reliant on an operator to make the ultimate decision on what is a pass and a fail. You also have no record of what the projection looked like and the whole process takes time. This all leads to a loss of productivity and makes your Six Sigma quality levels dependent on an operator’s quality check, and as we all know, manual inspection (even 200% manual inspection) is not a reliable method for manufacturers to keep their quality levels in check. Operators get tired and there is a lack of consistency between different people in the quality assessment team.

But for those products with critical-to-quality measurements, the old shadowgraph methodology can now be replaced with a fully automated metrology vision system to reduce the reliance on the operator in making decisions, coupled with the ability to save SPC data and information.

In this case, the integration of ultra-high-resolution vision systems, in tandem with cutting-edge optics and collimated lighting, presents a formidable alternative to manual shadowgraphs, ushering in an era of automation. With this advancement, operators can load the product, press a button, and swiftly obtain automatic results confirming adherence to specified tolerances. Furthermore, all pertinent data can be meticulously recorded, with the option to enable serial number tracking, ensuring the preservation of individual product photos for future warranty claims. This transition not only illuminates manufacturing processes but elevates them to a brighter, six-sigma enhanced quality realm. Moreover, these systems can be rigorously validated to GAMP/ISPE standards, furnishing customers with an unwavering assurance of consistently superior quality outcomes.

For your upcoming quality initiatives or product launches, consider pivoting towards automated vision system machines, transcending the limitations of antiquated shadowgraph technology and operator-dependent assessments. Embrace the superior approach that is now within reach, and steer your manufacturing quality towards a future defined by precision, efficiency, and reliability.

By embracing automated vision systems, manufacturers streamline processes and ensure unparalleled precision and consistency in product quality. This shift represents a pivotal leap forward in manufacturing excellence, empowering businesses to meet and exceed customer quality expectations while navigating the ever-evolving landscape of modern production.

We’re often asked – what’s the difference between machine vision and computer vision, and how do they both compare with image processing? It’s a valid question as the two seemingly similar fields of machine vision and computer vision both relate to the interpretation of digital images to produce a result from the image processing of photos or images.

What is machine vision?
Machine vision has become a key technology in the area of quality control and inspection in manufacturing. This is due to increasing quality demands and the improvements it offers over and above human vision and manual operation.

Definition
It is the ability of a machine to consequentially sense an object, capture an image of that object, and then process and analyse the information for decision-making.

In essence, machine vision enables manufacturing equipment to ‘see’ and ‘think’. It is an exciting field that combines technologies and processes to automate complex or mundane visual inspection tasks and precisely guide manufacturing activity. In an industrial context, it is often referred to as ‘Industrial Vision’ or ‘Vision Systems’. As the raw image in machine vision are generally captured by a camera connected to the system in “real-time” (compared to computer vision), the disciplines of physics relating to optical technology, lighting and filtering are also part of the understanding in the machine vision world.

So, how does this compare to computer vision and image processing?
There is a great deal of overlap between the various disciplines, but there are clear distinctions between the groups.

Image processing has its roots in neurobiology and scientific analysis. This was primarily down to the limitations of processor speeds when image processing came into the fore in the 1970’s and 1980’s—for example, processing single images following the capture from a microscope-mounted camera.

When processors became quicker and algorithms for image processing became more adept at high throughput analysis, image processing moved into the industrial environment, and industrial line control was added to the mix. For the first time, this allowed an analysis of components, parts and assemblies as part of an automated assembly line to be quantified, checked and routed dependent on the quality assessment, so machine vision (the “eyes of the production line”) was born. Unsurprisingly, the first industry to adopt this technology en mass was the PCB and semiconductor manufacturers, as the enormous boom in electronics took place during the 1980s.

Computer vision crosses the two disciplines, creating a Venn diagram of overlap between the three areas, as shown below.

As you can see, the work on artificial intelligence relating to deep learning has its roots in computer vision. Still, over the last few years, this has moved over into machine vision, as the image processing learnt from biological vision, coupled with cognitive vision moves slowly into the area of general purpose machine vision & vision systems. It’s rarely a two-way street, but some machine vision research moves back into computer vision and general image processing worlds. Given the more extensive reach of the computer vision industry, compared to the more niche machine vision industry, new algorithms and developments are driven by the broader computer vision industry.

Machine vision, computer vision and image processing are now broadly intertwined, and the developments across each sector have a knock-on effect on the growth of new algorithms in each discipline.

Where are we at, and what is the future?

We had a break from the blog last month while we worked on this latest piece, as it’s a bit of a beast. This month we discuss artificial intelligence (AI) for machine vision systems. An area with much hype due to the introduction of AI language models such as ChatGPT and AI image models like DALLE. AI is starting to take hold for vision systems, but what’s it all about? And is it viable?

Where are we at?

Let’s start at the beginning. The term “AI” is an addendum for a process that has been around for the last twenty years in machine vision but has only now become more prolific (or should we say, the technology has matured to the point of mass deployment). We provided vision systems in the early 00’s using low-level neural networks for feature differentiation and classification. These were primarily used for simple segmentation and character verification. These networks were basic and had limited capability. Nonetheless, the process we used then to train the network is the same as it is now, but on a much larger scale. Back then, we called it a “neural network”; now it’s termed artificial intelligence.

The term artificial intelligence is coined as the network has some level of “intelligence” to learn by example and expand its knowledge on iterative training levels. The initial research into computer vision AI discovered that human vision has a hierarchical structure on how neurons respond to various stimuli. Simple features, such as edges, are detected by neurons, which then feed into more complex features, such as shapes, which finally feed into more complex visual representations. This builds individual blocks into “images” the brain can perceive. You can think of pixel data in industrial vision systems as the building blocks of synthetic image data. Pixel data is collected on the image sensor and transmitted to the processor as millions of individual pixels of differing greyscale or colour. For example, a line is simply a collection of like-level pixels in a row with edges that transition pixel by pixel in grey level difference to an adjacent location.

Neural networks AI vision attempts to recreate how the human brain learns edges, shapes and structures. Neural networks consist of layers of processing units. The sole function of the first layer is to receive the input signals and transfer them to the next layer and so on. The following layers are called “hidden layers”, because they are not directly visible to the user. These do the actual processing of the signals. The final layer translates the internal representation of the pattern created by this processing into an output signal, directly encoding the class membership of the input pattern. Neural networks of this type can realise arbitrarily complex relationships between feature values and class designations. The relationship incorporated in the network depends on the weights of the connections between the layers and the number of layers – and can be derived by special training algorithms from a set of training patterns. The end result is a “web” of connections between the networks in a non-linear fashion. All of this is to try and mimic how the brain operates in learning.

With this information in hand, vision engineering developers have been concentrating their efforts on digitally reproducing human neural architecture, which is how the development of AI came into being. When it comes to perceiving and analysing visual stimuli, computer vision systems employ a hierarchical approach, just like their biological counterparts do. Traditional machine vision inspection continued in this vein, but AI requires a holistic view of all the data to compare and develop against.

So, the last five years have seen an explosion in the development of artificial intelligence, deep learning vision systems. These systems are based on neural network development in computer vision in tandem with the development of AI in other software engineering fields. All are generally built on an automatic differentiation library to implement neural networks in a simple, easy-to-configure solution. Deep learning AI vision solutions incorporate artificial intelligence-driven defect finding, combined with visual and statistical correlations, to help pinpoint the core cause of a quality control problem.

To keep up with this development, vision systems have evolved into manufacturing AI and data gathering platforms, as the need for vision data for training becomes an essential element for all AI vision systems compared to deploying traditional machine vision algorithms. Traditional vision algorithms still need image data for development, but not to the same extent as needed for deep learning. This means vision platforms have developed to integrate visual and parametric data into a single digital thread from the development stage of the vision project all the way through production quality control deployment on the shop floor.

In tandem with the proliferation of the potential use of AI in vision systems is the explosion in suppliers of data-gathering “AI platforms”. These systems are more of a housekeeping exercise for image gathering, segmentation and human classification before the submission to the neural network, rather than being a quantum leap in image processing or a clear differential compared to traditional machine vision. Note: most of these companies have “AI” in their titles. These platforms allow for clear presentation of the images and the easy submission to the network for computation of the neural algorithm. Still, all are based on the same overall architecture.

The deep learning frameworks – Tensorflow or PyTorch – what are they?

Tensorflow and PyTorch are the two main deep learning frameworks developers of machine vision AI systems use. Each was developed by Google and Facebook, respectively. They are used as a base for developing the AI models at a low level, generally with a graphical user interface (GUI) above it for image sorting.

TensorFlow is a symbolic math toolkit that is best suited for dataflow programming across a variety of workloads. It provides many abstraction levels for modelling and training. It’s a promising and rapidly growing deep learning solution for machine vision developers. It provides a flexible and comprehensive ecosystem of community resources, libraries, and tools for building and deploying machine-learning apps. Recently, Tensorflow has integrated Keras into the framework, a precursor to Tensorflow.

PyTorch is a highly optimised deep learning tensor library built on Python and Torch. Its primary use is for applications that use graphics processing units (GPUs) and central processing units (CPUs). Vision system vendors favour PyTorch over other Deep Learning frameworks such as TensorFlow and Keras because it employs dynamic computation networks and is entirely written in Python. It gives researchers, software engineers, and neural network debuggers the ability to test and run sections of the code in real-time. Because of this, users do not have to wait for the entirety of the code to be developed before determining whether or not a portion of the code works.

Whichever solution is used for deployment, the same pros and cons apply in general to machine vision solutions utilising deep learning.

What are the pros and cons of using artificial intelligence deep learning in machine vision systems?

Well, there are a few key takeaways when considering using artificial intelligence deep learning in vision systems. These offer pros and cons when considering whether AI is the appropriate tool for industrial vision system deployment.

Cons

– Industrial machine vision systems must have high yield, so we are talking 100% correct identification of faults, in substitute for accepting some level of false failure rate. There is always a trade-off in this process in setting up vision systems to ensure you err on the side of caution so that real failures are guaranteed to be picked up and automatically rejected by the vision system. But with AI inspection, the yields are far from perfect, primarily because the user has no control of the processing functionality that has made the decision on what is deemed a failure. A result of pass or fail is simply given for the outcome. The neural network having been trained from a vast array of both good and bad failures. This “low” yield (though still above 96%) is absolutely fine for most requirements of deep learning (e.g. e-commerce, language models, general computer vision), but for industrial machine vision, this is not acceptable in most application requirements. This needs to be considered when thinking about deploying deep learning.

– It takes a lot of image data. Sometimes thousands, or tens of thousands of training images are required for the AI machine vision system to start the process. This shouldn’t be underestimated. And think about the implications for deployment in a manufacturing facility –you have to install and run the system to gather data before the learning part can begin. With traditional machine vision solutions, this is not the case, development can be completed before deployment, so the time-to-market is quicker.

– Most processing is completed on reduced-resolution images. It should be understood that most (if not all) deep learning vision systems will reduce the image size down to a manageable size to process in a timely manner. Therefore, resolution is immediately lost from a mega-pixel resolution image down to a few hundred pixels. Data is compromised and lost.

– There are no existing data sets for the specific vision system task. Unlike AI deep learning vision systems used in other industries, such as driverless cars or crowd scene detection, there are usually no pre-defined data sets to work from. In those industries, if you want to detect a “cat”, “dog”, or “human”, there is available data sources for fast-tracking the development of your AI vision system. Invariably in industrial machine vision, we look at a specific widget or part fault with no pre-determined visual data source to refer to. Therefore, the image data has to be collected, sorted and trained.

– You need good, bad and test images. You need to be very careful in selecting the images into the correct category for processing. A “bad” image in the “good” pile of 10,000 images is hard to spot and will train the network to recognise bad as good. So, the deep learning system is only as good as the data provided to it for training and how it is categorised. You must also have a set of reference images to test the network with.

– You sometimes don’t get definitive data on the reason for failure. You can think of the AI deep learning algorithm as a black box. So, you feed the data in and it will provide a result. Most of the time you won’t know why it passed or failed a part, or for what reason, only that it did. This makes deploying such AI vision systems into validated industries (such as medical devices, life sciences and pharmaceuticals) problematic.

– You need a very decent GPU & PC system for training. A standard PC won’t be sufficient for the processing required in training the neural network. While the PC used for the runtime need be nothing special, your developers will need a high graphics memory PC with a top-of-the-range processer. Don’t underestimate this.

Pros

– They do make good decisions in challenging circumstances. Imagine your part requiring automated inspection has a supplier constantly supplying a changing surface texture for your widget. This would be a headache for traditional vision systems, which is almost unsolvable. For AI machine vision, this sort of application is naturally suited.

– They are great for anomaly detection which is out of the ordinary. One of the main benefits of AI-based vision systems is the ability to spot a defect that has never been seen before and is “left field” from what was expected. With traditional machine vision systems, the algorithm is developed to predict a specific condition, be it the grey scale of a feature, the size in pixels which deems a part to fail, or the colour match to confirm a good part. But if your part in a million that is a failure has a slight flaw that hasn’t been accounted for, the traditional machine vision system might miss it, whereas the AI deep learning machine vision system might well spot it.

– They are useful as a secondary inspection tactic. You’ve exhausted all traditional methods of image processing, but you still have a small percentage of faults which are hard to classify against your known classification database. You have the image data, and the part fails when it should, but you want to drill down to complete a final analysis to improve your yield even more. This is the perfect scenario for AI deep learning deployment. You have the data, understand the fault and can train on lower-resolution segments for more precise classification. This is probably where AI deep learning vision adoption growth will increase over the coming years.

– They are helpful when traditional algorithms can’t be used. You’ve tried all conventional segmentation methods, pixel measurement, colour matching, character verification, surface inspection or general analysis – nothing works! This is where AI can step in. The probable cause of the traditional algorithms not operating is the consistency in the part itself which is when deep learning should be tried.

Finally, it might just be the case that deep learning AI is not required for the vision system application. Traditional vision system algorithms are still being developed. They are entirely appropriate to use in many applications where deep learning has recently become the first point of call for solving the machine vision requirement. Think of artificial intelligence in vision systems as a great supplement and a potential tool in the armoury, but to be used wisely and appropriately for the correct machine vision application.

Learn more about Artificial Intelligence (AI) Deep Learning here:

View Page

In-line visual quality control is more crucial than ever in today’s competitive medical device production environment. To keep ahead of the competition, manufacturers must ensure that their products fulfil the highest standards. One of the most effective ways to attain this goal is through automated vision metrology using vision technology.

So what do we mean by this? There are several traditional methods that a quality manager uses to keep tabs on the production quality. In the past, this would have been the use of a collection of slow CMM metrology machines to inspection the product one at a time. CMMs measure the coordinates of points on a part using a touch probe. This information can then be used to calculate the dimensions, surface quality and tolerances of the part. So we are not talking about high-speed vision inspection solutions in this context, but the precise, micron level, precision metrology inspection for measurements and surface defects.

The use of computer-controlled vision inspection equipment to measure and examine products is known as automated vision metrology. These vision systems offer several advantages over manual inspection CMM-type probing methods. For starters, automated metrology is faster. This is due to the fact that machines can perform measurements with great precision and repeatability at speed, as there is no need to touch the product, it’s non-contact vision assessment. Second, real-time inspection of parts is possible with automated metrology. This enables firms to detect and rectify flaws early in the manufacturing process, before they become costly issues.

Advantages of Automated Vision Metrology
The use of automated metrology in manufacturing has many intrinsic benefits. These include:

• Decreased inspection time: Compared to manual inspection procedures, automated vision metrology systems can inspect parts significantly faster. This can save a significant amount of time during the manufacturing process.
• Specific CTQ: Vision inspection systems can be set to measure specific critical to quality elements with great precision and reproducibility.
• Non-contact vision inspection: As the vision camera system has no need to touch the product, it has no chance of scratching or adding to the surface quality problems which a touch probe inevitably has. This is especially important in industries such as orthopaedic joint and medical device tooling manufacturing.
• Enhanced productivity: Automated vision metrology systems can be equipped with autoloaders, allowing for the fast throughput of products without the need for an operator to manually load a product into a costly fixture. Manufacturers can focus on other tasks that demand human judgement and inventiveness by freeing up human workers from manual inspection tasks.

Modern production relies heavily on automated vision metrology. It has several advantages, including enhanced accuracy, less inspection time, improved quality control, increased production, and higher product quality. With automated loading options now readily available, speed of inspection for precision metrology measurements can be completed at real production rates.

Overall, automated vision metrology is a potent tool for improving the quality and efficiency of production processes. It is an excellent addition to any manufacturing organisation wanting to increase quality control at speed, and make inspection more efficient compared to traditional CMM probing methods.

Machine vision lighting is one of the most critical elements in automated visual inspection. Consistent lighting at the correct angle, of the correct wavelength with the right lux level allows automated inspection to be robust and reliable. But how do you select the correct light for your machine vision application?

Where the inspection takes place on the production process has to be set according to the requirements of the quality check. 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 in machine vision 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
• Fixed machine vision lighting for each inspection process

Lighting is critical because it enables the camera to see necessary details. In fact, poor lighting is one of the major causes of failure of a machine vision systems. 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. The positioning matrix of machine vision lighting is shown below.

So it’s important to select the machine vision light relative to the position of the light with respect to the part being inspected.

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

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

Due to the obvious complexities, there is often no substitute other than to test various techniques and solutions. It is imperative to get the best machine vision lighting solution in place to improve the yield, robustness and long term effectiveness of the automated inspection solution.