Tag: Innovation

In today’s rapidly evolving manufacturing landscape, the role of operator guidance systems has taken centre stage. As businesses strive to enhance productivity, minimise errors, and maintain workforce engagement, innovative technologies such as augmented reality (AR) and digital work instructions are transforming the way operators interact with their tasks. This guide explores the pivotal elements of operator guidance, addressing common challenges and offering actionable insights to optimise manufacturing processes.

1. Understanding Operator Guidance

Operator guidance refers to the methods and tools designed to assist workers in executing their tasks with precision and efficiency. Whether through augmented reality, vision systems, digital instructions, or automated feedback systems, these tools aim to:

• Simplify complex tasks
• Reduce errors and rework
• Shorten training durations
• Enhance workforce versatility

The integration of such systems into manufacturing environments transforms workstations into interactive, digital ecosystems that support operators in real time. By offering real-time feedback, operators are less likely to make errors, ensuring consistency and quality throughout the manufacturing process. It also allows new operators to understand a task immediately.

Additionally, operator guidance systems improve communication on the shop floor. By aligning visual aids, auditory signals, and data analytics, they ensure that workers have access to clear and concise information. This holistic approach boosts efficiency and fosters a culture of continuous improvement.

2. The Shift Toward Digital Work Instructions

Digital work instructions have emerged as a game-changer in manufacturing, replacing traditional paper manuals with dynamic, interactive platforms. The benefits include:

• Cost Efficiency: Eliminating printing and distributing manuals saves resources while promoting sustainability.
• Real-Time Updates: Digital platforms ensure operators have access to the latest procedures, reducing the risk of outdated information causing errors.
• Interactive Learning: Multimedia elements such as videos and interactive checkpoints enhance comprehension and retention, shorten training times, and improve task accuracy.
• Streamlined Processes: Instructions tailored to specific tasks or product variants eliminate the need for manual searches, delivering the right information at the right time.

Companies adopting digital work instructions also benefit from seamless integration with Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) systems. This integration enables data-driven insights for continuous improvement and fosters transparency and adaptability, ensuring production goals are consistently met.

Digital work instructions are handy in high-mix, low-volume production environments where the need to adapt to new product lines quickly is critical. The ability to easily modify and distribute updated instructions enhances operational flexibility and reduces downtime.

3. Overcoming Challenges in Operator Guidance

3.1 Finding and Retaining Skilled Labour

The global shortage of skilled operators is a significant challenge for manufacturers. High turnover rates, coupled with extended training periods, can disrupt operations. Implementing operator guidance systems minimises the impact of these challenges by:

• Reducing the dependency on pre-existing expertise
• Accelerating onboarding with intuitive training modules
• Enabling lower-skilled workers to perform complex tasks effectively

By incorporating gamification elements into training modules, manufacturers can further engage employees. This approach improves learning outcomes and increases job satisfaction, reducing turnover rates. It allows work instructions to adapt to various languages automatically.

3.2 Adapting to Increased Demand

Fluctuations in customer demand often stress production capacities. Flexible guidance systems help manufacturers scale operations by:

• Training temporary workers swiftly
• Standardising processes to ensure consistent quality
• Allowing real-time adjustments based on workload variations

Advanced analytics integrated into guidance systems allow managers to predict and prepare for demand surges, optimising resource allocation.

3.3 Addressing Operator Resistance

Operator scepticism can hinder the adoption of new technologies. Concerns about micromanagement and loss of autonomy are common. To counter this, systems are designed with operator-centric features such as:

• Experience Level Functionality: Customisable instruction intensity based on operator proficiency
• Freedom in Task Execution: Allowing operators to perform tasks in their preferred sequence where feasible
• Error Prevention as Support: Real-time feedback focuses on preventing mistakes rather than penalising them

Involving operators in designing and implementing guidance systems can also mitigate resistance. By incorporating user feedback, organisations can ensure that the systems are perceived as tools for empowerment rather than oversight.

4. The Role of Augmented Reality in Operator Guidance

Augmented reality (AR) enhances operator guidance by overlaying digital information directly onto physical workspaces. This immersive approach offers:

• Real-Time Visual Feedback: Operators receive step-by-step instructions projected onto their workbench, ensuring clarity and precision.
• Reduced Cognitive Load: By focusing on one task at a time, operators can work more efficiently without memorising sequences.
• Error Prevention: AR systems, integrated with sensors and smart tools, provide immediate warnings if a task is performed incorrectly.

Types of AR Implementation:

• Wearables: AR glasses and headsets provide hands-free access to instructions but may cause discomfort during extended use.
• Handheld Devices: Tablets offer portability but interrupt workflow when operators need both hands.
• Projected AR: Systems that project instructions directly onto workspaces offer ergonomic, non-intrusive solutions ideal for industrial settings. This approach is particularly valued for its standalone nature, eliminating reliance on battery power and enhancing safety.

Projected AR also offers significant scalability, making it suitable for both small-scale operations and large industrial setups. Its ability to integrate seamlessly with existing tools and workflows ensures minimal disruption during implementation.

5. Benefits of Implementing Operator Guidance Systems

5.1 Enhanced Training
Guidance systems significantly reduce training durations by up to 50%, enabling temporary and new workers to become productive faster. Interactive features foster skill acquisition and confidence. Visual and interactive tools accelerate learning and reduce dependency on senior operators.

By incorporating real-time feedback mechanisms, these systems create a continuous learning environment. Operators can identify and correct errors on the spot, enhancing their overall proficiency over time.

5.2 Improved Quality and Efficiency
Standardised processes ensure that every operator performs tasks uniformly, minimising errors and waste. Systems that validate task completion further enhance quality assurance. By minimising rework and reducing scrap, these systems help manufacturers maintain competitive advantages.

Data from guidance systems can also be used to implement continuous improvement initiatives. By analysing patterns in errors and inefficiencies, managers can identify opportunities for process optimisation.

5.3 Adaptability and Flexibility
With customisable instructions, operators can seamlessly switch between tasks or product variants. This flexibility supports high-mix, low-volume production environments. The ability to integrate temporary or seasonal workers smoothly ensures operational continuity.

Moreover, guidance systems support cross-training initiatives, enabling workers to develop skills across multiple roles. This versatility enhances workforce agility and resilience.

5.4 Data-Driven Insights
Embedded analytics identify bottlenecks and error-prone steps, providing actionable data to optimise workflows and improve productivity. Reports generated from these systems allow supervisors to target specific areas for improvement, enhancing overall operational efficiency.

The integration of IoT devices further enhances data collection, offering deeper insights into machine performance and operator interactions. This holistic view enables more informed decision-making.

6. Empowering Operators: A Broader Perspective

Instead of focusing on a single case, the real power of operator guidance systems lies in their adaptability across diverse industries. Whether it is automotive manufacturing, electronics assembly, or medical device assembly, these systems are engineered to overcome common challenges such as complex task sequences and quality control. By integrating features like real-time feedback, customisable training modules, and seamless MES/ERP connectivity, businesses of all sizes can achieve:

• Up to 50% faster onboarding of new staff
• Streamlined multi-variant production processes
• A significant reduction in waste and rework
• Enhanced adaptability for workforce changes

This versatility highlights the universal benefits of operator guidance systems, making them indispensable tools for any forward-thinking manufacturing operation.

The ability to customise systems based on industry-specific requirements ensures that organisations can address their unique challenges effectively. From ensuring compliance in regulated industries to optimising throughput in high-volume sectors, these systems deliver measurable value.

7. The Future of Operator Guidance

As Industry 4.0 continues to evolve, operator guidance systems will play an even more critical role in bridging human expertise with advanced automation. Emerging trends include:

• AI-Driven Insights: Predictive analytics to pre-empt errors and optimise training
• IoT Integration: Seamless communication between tools, sensors, and guidance platforms
• Personalised Experiences: Tailoring guidance to individual operator preferences and skill levels
• Eco-Conscious Manufacturing: Incorporating sustainable practices through optimised resource management

By aligning with these trends, operator guidance systems will continue to serve as a cornerstone of modern manufacturing strategies. The focus on sustainability and worker well-being ensures that these systems contribute to broader organisational goals beyond operational efficiency.

Conclusion

Operator guidance with vision systems represents a pivotal advancement in manufacturing, addressing challenges from labour shortages to quality assurance. By leveraging technologies like augmented reality and digital work instructions, manufacturers can empower their workforce, reduce costs, and achieve unparalleled operational excellence. Embracing these systems is not just a step forward—it is an essential leap into the future of manufacturing. These systems’ adaptability, efficiency, and support for human-centric operations make them indispensable for achieving long-term success in a competitive global manufacturing market.

More details on IVS Operator Guidance Systems can be found here: www.industrialvision.co.uk/products/operator-guidance-platform

This month, we’re discussing the elements of semi-automatic inspection. This is the halfway house that pharmaceutical manufacturers have to move to from manual quality inspection (of individual products) at a bench when volumes start to increase but don’t warrant a fully automated inspection solution. The catalyst for change is when the inspection room is full of quality inspectors, and it’s time to increase the throughput of quality assessment. The semi-automatic inspection solution allows vials (liquid and lyo), syringes, cartridges and ampoules to be presented to the operator at speed to review before a magnifier, enabling manual rejection to be completed. This allows a single operator to do the job of a room of quality inspectors. This process mimics the standard GAMP manual inspection of the black and white background booths found in manual inspection, allowing for the throughput of high volumes. However, how does the semi-automatic inspection fit into the validation process and allow validation at the relevant levels?

Validation of such semi-automatic inspection must adhere to the United States Pharmacopeia (USP). The following chapters from the USP are regarded as the main literature and source for regulatory information surrounding the visual inspection of injectables:

Chapter 〈790〉 VISIBLE PARTICULATES IN INJECTIONS
Chapter 〈1790〉 VISUAL INSPECTION OF INJECTIONS
Chapter 〈788〉 PARTICULATE MATTER IN INJECTIONS

Chapter 〈790〉 establishes the expectation that each unit of injectable product will be inspected as part of the routine manufacturing process. This inspection should take place at a point when defects are most easily detected; for example, prior to labelling or insertion into a device or combination product. Semi¬automated inspection should only be performed by trained, qualified inspectors. The intent of this inspection is to detect and remove any observed defect. When in doubt, units should be removed

Defect Types

IVS semi-automatic inspection machines with operators allow for the inspection of cosmetic defects in the container and particulate matter within the fluid. The machines will enable the inspection of the following defects:

Cosmetic Defects:
• Glass cracks
• Scratches
• Missing stoppers/caps
• Improper cap/stopper closure
• Glass inclusions

Particulate Matter:
• Fibers
• Glass
• Metal
• Product Related
• Rubber

What are the particle definitions which apply to Semi-Automatic Inspection?

Extrinsic – Highest risk
Particles may originate from many sources. Those that are foreign to the manufacturing process are considered exogenous or “extrinsic” in origin; these include hair, non-process-related fibres, starch, minerals, insect parts, and similar inorganic and organic materials. Extrinsic material is generally a one-time occurrence and should result in the rejection of the affected container in which it is seen; however, elevated levels in the lot may implicate a broader contribution from the same source. These particles may carry an increased risk of microbiological or extractable contamination because less is known about their path before deposition in the product container or their interaction with the product.

Intrinsic – Medium Risk
Other particles are considered “intrinsic”, from within the process. Intrinsic particles may come from processing equipment or primary packaging materials that were either added during processing or not removed during container preparation. These primary product-contact materials may include stainless steel, seals, gaskets, packaging glass and elastomers, fluid transport tubing, and silicone lubricant. Such particles still pose the risk of a foreign body, but generally come from sterile or sanitized materials and more is known about their interactions when in contact with the product.

Inherent- Lower Risk
“Inherent” particles are considered the lowest risk as they are known to be or intended to be associated with specific product formulations. The physical form or nature of inherent particles varies from product to product and includes solutions, suspensions, emulsions, and other drug delivery systems that are designed as particle assemblies (agglomerates, aggregates). Product formulation-related particulate formation should be studied in the development phase and in samples placed on stability to determine the normal characteristics and time-based changes that can occur.

Defects are commonly grouped into classifications based on patient and compliance risk. The most common system uses three groups: critical, major, and minor. Critical defects are those that may cause serious adverse reaction or death of the patient if the product is used. This classification includes any nonconformity that compromises the integrity of the container and thereby risks microbiological contamination of the sterile product. Major defects carry the risk of a temporary impairment or medically reversible reaction, or involve a remote probability of a serious adverse reaction. This classification is also assigned to any defect which causes impairment to the use of the product. These may result in a malfunction that makes the product unusable. Minor defects do not impact product performance or compliance; they are often cosmetic in nature, affecting only product appearance or pharmaceutical elegance.

Inspection criteria

On semi-automatic inspection machines, the vials are spun up at speed prior to reaching the inspection area. This sets any visible particles in motion which aids in detection as stationary particles will be difficult to detect. Upon 100% inspection, visible extrinsic and intrinsic particles should be reliably removed. The test method allows inherent particles to be accepted if the product appearance specification allows inherent particle types. The size of particles reliably detected (2′::70% probability of detection) is generally 150 µm or larger. This Probability of Detection (POD) is dependent on the container characteristics (e.g., size, shape, transparency), inspection conditions (lighting and duration), formulation characteristics (colour and clarity), and particle characteristics (size, shape, colour, and density). For syringes the products are agitated and turned over so that no particulate matter is left in the bung and can be seen.

Critical Inspection Conditions

Light intensity
The results of the inspection process are influenced by the intensity of the light in the inspection zone. In general, increasing the intensity of the light that illuminates the container being inspected will improve inspection performance; 〈790〉 recommends light levels of 2,000-3,750 lux at the point of inspection for routine inspection of clear glass containers. Special attention should be given to assure that inspection is not performed below the lower limit of 2,000 lux.

Background and Contrast
Contrast between the defect of interest and the surrounding background is required for detection, and increased contrast improves detection. The use of both black and white backgrounds is described in 〈790〉, as well as other global pharmacopoeias. The use of both backgrounds provides good contrast for a wide range of particulate and container defects, which can be light or dark in appearance.

Inspection Rate
This is controlled by the roller travel speed. Sufficient time must be provided to allow for thorough inspection of each container; chapter 〈790〉 specifies a reference time of 10 s/container (5s each against both black and white backgrounds).

Magnification
Some inspection processes use a large magnifier to increase image size and thus increase the probability of detecting and rejecting containers with defects near the threshold of detection. Most lenses used during this inspection process are around X2 magnification.

Overall, Semi-Automatic Inspection is a necessary step when pharmaceutical manufacturers move into medium-volume production. Validating such systems allows production to ramp up to the required volume without the need for manual benches and provides capacity for future volumes before fully automated vision inspection with vision systems. More details on how to use Semi-Automatic inspection for regulatory compliance can be found here.

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

• A History of Industrial Robots [Internet] Weolver [cited 23rd Sept 2020]
• International Federation of Robotics [Internet]. International Federation of Robotics; 2019 [cited 29 July 2020].
• Artificial Intelligence [Industrial Vision Systems Ltd]

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.

	Input	Output
Image processing	Image is processed using algorithms to correct, edit or process an image to create a new better image.	Enhanced image is returned.
Computer vision	Image/video is analysed using algorithms in often uncontrollable/unpredictable circumstances.	Image understanding, prediction & learning to inform actions such as segmentation, recognition & reconstruction.
Machine vision	Use of camera/video to analyse images in industrial settings under more predictable circumstances.	Image understanding & learning to inform manufacturing processes.

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.