Industrial Vision Systems (IVS), a supplier of inspection machines to industry, has launched a range of new optical sorting machines specifically for the high-speed sorting of small components such as fasteners, rings, plastic parts, washers, nuts, munitions and micro components. The devices provide automatic inspection, sorting, grading and classification of products at up to 600 parts per minute. The systems intercept and reject failed parts at high speed, discovering shifts in quality, and providing quality assurance through the production cycle.

The new Optical Sorting Machines from IVS utilise the latest vision inspection algorithms allowing manufacturers to focus on other activities while the fully automated sorting machines root out rogue products and make decisions on quality automatically. For classification checks, the systems use Artificial Intelligence (AI) and Deep Learning, providing the machines with an ability to “learn by example” and improve as more data is captured.

The glass disc of the machine provides 360-degree inspection enabling the system to act as the ‘eyes’ on the factory floor and record production trends and data. By intercepting and rejecting failed parts at high speed, it gives manufacturers the ability to provide 100% automatically inspected product to their customers, without human intervention.

With real-time data and comprehensive reporting to see defect rates, this enables engineers to immediately respond to problems and take corrective action before products are delivered to a customer.

Andrew Waller, director at Industrial Vision Systems, said: “Our machines allow manufacturers to stay ahead of their competitors. These new systems are designed for manufacturers of mass-produced, small products which previously would have struggled to sort quality concerns. We can perceive and detect defects others miss at high-speed. Our optical sorting technology takes vision inspection to the next level. Clear, ultra-high-definition images allow our new generation of systems to recognise even the hardest to spot flaws and to sort wrong batch parts. This allows our customers to achieve continuous yield reductions, categorise failures based on their attributes, and build better products.”

“Innovations in Pharmaceutical Technology” – IVS featured in leading international Pharmaceutical magazine.

The articles covers how traditional image processing techniques are being superseded by vision systems utilising deep learning and artificial intelligence in pharmaceutical manufacturing.

Pharmaceutical and medical device manufacturers must be lean, with high-speeds, and an ability to switch product variants quickly and easily, all validated to ‘Good Automated Manufacturing Practice’ (GAMP). Most medical device production processes involve some degree of vision inspection, generally due to either validation requirements or speed constraints (a human operator will not keep up with the speed of production). Therefore, it is critical that these systems are robust, easy-to-understand and seamlessly integrate within the production control and factory information system.

Historically, such vision systems have used traditional machine vision algorithms to complete some everyday tasks: such as device measurement, surface inspection, label reading and component verification. Now, new “deep-learning” algorithms are available to provide an ability for the vision system to “learn”, based on samples shown to the system – thus allowing the quality control process to mirror how an operator learns the process. So, these two systems differ: the traditional system being a descriptive analysis, and the new deep learning systems based on predictive analytics.

Download the magazine from this link (Article Page 30):

Find more details on IVS solutions for the medical device and pharmaceutical industries here:

IVS featured in “InVision” magazine in Germany

An article by Christian Demant, Director of IVS, has appeared in the leading German machine vision magazine, “InVision” ( The article describes a 3D final inspection industrial automation line for quality control of insulation parts – designed, built and integrated by Industrial Vision Systems. The report covers the major vision inspection elements of the machine process, including:

3D Vision – 3D vision sensors from above and below scan the product, to build up a complete 3D profile of the surface allowing small surface inclusions, dents and raised defects to be automatically assessed and rejected.

All cosmetic and topography is automatically checked.

2D Vision – Precision 2D machine vision cameras from above and below create an accurate measurement profile of the part allowing finite metrology checks on the product. Vision sensors for automated sorting and inspection.

Metal Detection – Automated metal detection allows any rogue metallic parts which could have embedded into the product to be identified.

Check Weigher – The inspection line has to cope with varying sizes of product; the check weigher accurately checks weight allowing over or undersized product to be automatically rejected.
Printing Inspection – Upon passing all inspection processes, the product is automatically marked, and this mark is then automatically inspected by the vision system.

Pick and Place – Good product are picked and stacked, ready for immediate packing by the operator. Reject parts are allowed to run off the outfeed reject zone for reject or rework.

Download the magazine from the following link (Page 46):

Find out more about the machine vision application areas IVS support:


Industrial Vision Systems (IVS®), a supplier of machine vision systems to industry, has launched the IVS-COMMAND-Ai™ in-line inspection solution designed for high-speed automated visual inspection, helping reduce manufacturer fines and protecting brand reputations. The IVS-COMMAND-Ai Vision Sensors integrate directly with all factory information and control systems, allowing complete part inspection, guidance, tracking and traceability with additional built-in image and data saving.

For those applications requiring complex classification, the IVS-COMMAND-Ai system utilises the latest deep learning artificial intelligence (ai) vision inspection algorithms. New multi-layered “bio-inspired” deep neural networks allow the latest IVS® 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 which represent defects.

Designed for complex manufacturing industries such as medical devices, pharmaceuticals, food & drink and automotive, the IVS-COMMAND-Ai Vision Systems are fitted with adaptable HD smart cameras to provide inspection from all angles and at high precision. This allows production lines to review and alert any flaws and defects in real-time, providing instant factory information on compatible devices. It also possesses speeds of up to 60 frames per second and can quickly be integrated on-line to inspect high speed and static products.

By achieving a robust inspection performance, the new IVS-COMMAND-Ai Vision Systems oversees complex vision inspections such as presence verification, OCR and gauging through to surface, defect and quality inspection in one solution. Comprehensive Statistical Process Control (SPC) data also provides closed-loop control to further safeguard production.

All IVS vision sensors can be integrated onto production lines, assembly cells, workbenches, robots and linear slides. Their robust design allows vision sensor integration into any industrial production process for seamless inspection, identification or guidance.

Earl Yardley, director at Industrial Vision Systems, comments: “Our vision systems are very easy to program, are highly accurate, offer easy maintenance and provide peace of mind in final quality acceptance. However, the IVS-COMMAND-Ai vision systems take it a step further. It is the complete, robust quality control inspection vision sensor solution, and it is ready to be deployed in all manufacturing environments. It will improve yield and deliver immediate improvements to product quality; and at these critical times, reliability and consistency are vital.”

Vision Sensors

Deep Learning (AI) – Enhancing automated inspection of medical devices?

Integrated quality inspection processes continue to make a significant contribution to medical device manufacturing production, including the provision of automated inspection capabilities as part of real-time quality control procedures. Long before COVID-19, medical device manufacturers were rapidly transforming their factory floors by leveraging technologies such as Artificial Intelligence (AI), machine vision, robotics, and deep learning.

These investments have enabled them to continue to produce critical and high-demand products during these current times, even ramping up production to help address the pandemic. Medical device manufacturers must be lean, with high-speeds, and an ability to switch product variants quickly and easily, all validated to ‘Good Automated Manufacturing Practice’ (GAMP). Most medical device production processes involve some degree of vision inspection, generally due to either validation requirements or speed constraints (a human operator will not keep up with the speed of production). Therefore, it is critical that these systems are robust, easy-to-understand and seamlessly integrate within the production control and factory information system.

Deep learning

Historically, such vision systems have used traditional machine vision algorithms to complete some everyday tasks: such as device measurement, surface inspection, label reading and component verification. Now, new “deep-learning” algorithms are available to provide an ability for the vision system to “learn”, based on samples shown to the system – thus allowing the quality control process to mirror how an operator learns the process. So, these two systems differ: the traditional system being a descriptive analysis, and the new deep learning systems based on predictive analytics.
Innovative machine and deep learning processes ensure more robust recognition rates. Medical device manufacturers can benefit from enhanced levels of automation. Deep learning algorithms use classifiers, allowing image classification, object detection and segmentation at a higher speed. It also results in greater productivity, reliable identification, allocation, and handling of a broader range of objects such as blister packs, moulds and seals. By enhancing the quality and precision of deployed machine vision systems, this adds a welcome layer of reassurance for manufacturers operating within this in-demand space.
Deep learning has other uses in medical device manufacturing too. As AI relies on a variety of methods, including machine learning and deep learning, to observe patterns found in data, deep learning is a subfield of machine learning that mimics the neural networks in the human brain by creating an artificial neural network (ANN). Like the human brain solving a problem, the software takes inputs, processes them, and generates an output. Not only can it help identify defects, but it can, as an example, help identify missing components from a medical set. Additionally, deep learning can often classify the type of defect, enabling closed-loop process control.
Deep learning can undoubtedly improve quality control in the medical device industry by providing consistent results across lines, shifts, and factories. It can reduce labour costs through high-speed automated inspection. It can help manufacturers avoid costly recalls and resolve product issues, ultimately protecting the health and safety of those towards the end of the chain.

AI Limitations

However, deep learning is not a silver bullet for all medical device and pharmaceutical vision inspection applications. It may be challenging to adopt in some applications due to the Federal Drugs Administration (FDA)/GAMP rules relating to validation.

The main issue is the limited ability to validate such systems. As the vision inspection solution utilising AI algorithms needs sample data, both good and bad samples – it makes validating the process extremely difficult, where quantitative data is required. Traditional machine vision will provide specific outputs relating to measurements, grey levels, feature extraction, counts etc. which are generally used for validating a process. With deep learning, the only output is “pass” or “fail”.

This is a limiting capability of deep learning enabled machine vision solutions – the user has to accept the decision provided by the AI tool blindly, providing no detailed explanation for the choice. In this context, the vision inspection application should be reviewed in advance, to see if AI is applicable and appropriate for such a solution.


In conclusion, deep-learning for machine vision in industrial quality control is now widely available. Nevertheless, each application must be reviewed in detail – to understand if the most appropriate solution is to utilise traditional machine vision with quantifiable metrics or the use of deep-learning with its decision based on the data pool provided. As AI and deep learning systems continue to develop for vision system applications, we will see more novel ways of adapting the solutions to replace traditional image processing techniques.

You can find out more details on IVS deep learning vision systems here:


Industrial Vision Systems (IVS), a supplier of machine vision systems to industry, has today launched a free downloadable guide explaining and reviewing vision systems and machine vision. The new ‘Vision to Automate’ guide is designed to support and direct UK manufacturers who are looking to adopt smart factory systems such as robotics, vision systems, automation, and machine learning.

With manufacturers desperately keen to resume some normality when it comes to production post-COVID-19, this 32-page premium guide reviews the basics of machine vision and vision systems, including components, applications and return on investment. ‘Vision to Automate’ also drills down into how an automated factory floor can increase productivity by improving processes, provide greater flexibility, and increase the volume of parts. This, in turn, reduces costs through a reduction in re-testing and labour.

IVS is already witnessing an increasing number of factory floor managers looking to increase the number of collaborative robots operating side by side with human workers post-lockdown, to ease fears of picking up infections. ‘Vision to Automate’ explains how this crucial human-robot collaboration will support the flexible production of highly complex items in lower quantities.

The guide also dissects automated bin-picking robots, which allows vision and robotics to operate autonomously picking product from bins and totes to load machines, bag products or to produce sub-assemblies. This is an area which IVS believes will become common across factory floors as workplaces evolve post-lockdown.

Earl Yardley, director at Industrial Vision Systems, comments: “The key for us is to outline the basics. What is machine vision? How does it work? What can it be used for? ‘Vision to Automate’ answers those questions. Forward-thinking businesses are leveraging automation to support their organisation, and I believe we will continue to see critical changes to working practices and automation deployment. This will create new opportunities across manufacturing within many industry sectors. This includes cutting edge production ideologies with vision robotics and an increasing ability to reduce human to human contact with the deployment of autonomous robotics. We see a growing demand for vision-guided robot systems to maintain production capacity and reduce dependence on the human workforce which will further drive the adoption of flexible manufacturing for generations to come. Removing operators from some production operations will allow factories to reopen with reduced human to human contact, increasing yield and protecting the rest of what is likely to be an anxious workforce.”

Download for FREE via the IVS homepage.

Industrial Vision Systems enters 20th year in business

IVS continues to spearhead the automation revolution supplying some of the world’s leading brands with machine vision technology.

Oxford, United Kingdom, March 1, 2020 – Industrial Vision Systems (IVS®) is celebrating its 20th anniversary as a leading global machine vision provider. Founded in 2000, IVS has since grown to now serve customers around the world, with the supply of thousands of vision systems over their impressive 20-year growth.

“We’re very proud to see what IVS has become,” said Earl Yardley, Director, one of the co-founders of IVS. “We started IVS by mastering our clients’ production and quality control challenges. Reflecting on the continuing success we’ve had, it’s a reminder that we are still on the right path, particularly with the growth of machine vision, and the advent of deep learning and artificial intelligence in vision system deployment.”

With proficiency in machine vision, robotics and industrial automation, IVS has developed a comprehensive suite of standard vision inspection machines, combined with hundreds of unique solutions to service major industries such as medical device, pharma, automotive, electronics and packaging.

IVS’s impressive growth can also be credited to the rise of machine vision and automation within production processes. Through standalone projects and complete automation lines, IVS’s global team has demonstrated its indisputable capability to support their customers at every step of the project process.

Andrew Waller, Director and co-founder, added: “IVS has an outstanding engineering team, who together address some of the most demanding and complex machine vision applications. Our team’s enthusiasm to understand and be tested by our customer problems has kept us focussed on being innovative, to ultimately further develop the company so that we can rise to any challenge over the next twenty years. We want to thank all our customers and employees for their trust and commitment which has made IVS one of the most respected machine vision suppliers to industry today.”

Launched in 2000, IVS vision systems are used all over the world in automated production processes for inspection, guidance, identification, measurement, tracking and counting. Its systems are reputed to be some of the most innovative and advanced machine vision solutions on the market today, successfully deployed in thousands of systems around the world.

Machine vision trends – what we can expect in 2019

Over the past year, unparalleled levels of developments have occurred in artificial intelligence (AI), big data, 3D imaging, and robotic process automation – none more so than on the factory floor. Industrial Vision Systems Ltd (IVS), a supplier of vision inspection solutions to industries such as medical devices, pharmaceuticals, food & drink, automotive, and printing & packaging, provides vision systems for quality control and robotic vision. This is a particular trend, amongst four others, which IVS believes will be prevalent in 2019.

3D Imaging and Bin Picking

Automation is driving factories to be smart and to reduce the workforce in operations where industrial automation can replace a person. Machine vision has been used for some time for the final quality control inspection, but new markets are opening up with the advent of 3D sensors and integrated solutions for bin picking. Random objects are picked by a robot gripper irrespective of the position and orientation of the part. 3D vision systems can recognise randomly placed parts in large scanning volumes, such as a tote and part boxes. The picking of complex objects in different orientations and stacks is possible thanks to dynamic robot handling. Combining Artificial Intelligence (AI) with bin picking operations allows autonomous part selection, increasing productivity and cycle time, reducing the need for human interaction in the process.

Deep Learning in the Cloud

The coming of 5G data networks for autonomous vehicles provides the ability to perform cloud-based machine vision computation. Massive Machine Type Communications (mMTC) allows large amounts of data to be processed in the cloud for machine vision applications. Deep learning algorithms using Convolutional Neural Network classifiers allows image classification, object detection and segmentation at speed. Development of these new AI and deep learning system will increase over the coming year.


2018 was a record year for robot sales according to the International Federation of Robotics with industrial robot sales increasing by 31 per cent. Trends such as human collaborative robots, simplification of use and process learning have helped propel the use of robots in industrial automation. In the future industrial robots will be easier and quicker to program using intuitive interfaces. The human-robot collaboration will support the flexible production of small quantity production with high complexity. The reduction in complexity of use allows the widespread use of robots and vision systems in the mid to long term.

Hyperspectral Imaging

Next generation modular hyperspectral imaging systems provide chemical material properties analysis in industrial environments. Chemical Colour Imaging visualises the molecular structure of materials by different colouring in the resulting images. This allows the chemical composition to be analysed in standard machine vision software. Typical applications include plastic detection in meat production, detection of different recyclable materials and blister pill inspection quality control. The main barrier for such systems is the amount of data and speed required for processing, but the development of faster processes, better algorithms and on camera calibration still make this a hot topic for 2019.
Thermal Imaging Industrial Inspection

Thermal imaging cameras have traditionally been used for defence, security and public safety with far-ranging uses of thermal images for detection. For many industrial applications, such as the production of parts and components for the automotive or electronics industry, thermal data is critical. While machine vision can see a production problem, it cannot detect thermal irregularities. Thermal imagery combined with machine vision is a growing area, allowing manufacturers to spot problems which can’t be seen by eye or standard camera systems. Thermal imaging provides non-contact precision temperature measurement and non-destructive testing – an area of machine vision and automation control set to grow.

Earl Yardley, Industrial Vision Systems Director, comments: “Industry 4.0-related technologies are driving much of the changes that are currently taking place in manufacturing. This applies in all sectors, but it is particularly important in high-specification and highly regulated industries like food & drink, pharmaceutical and medical device manufacturing. There are many reasons for companies moving towards Factory Automation technologies including making production lines more efficient, making more effective use of resources, and improving productivity. I fully expect to see growing demand in this area across many sectors in 2019.”

IVS featured in “Robots and Production” Magazine in Germany

An article by Christian Demant, Director of IVS, has appeared in the leading German manufacturing technology magazine, “Robots and Production” ( The article reports on the standards and risk assessments carried out by IVS with regard to installation of vision systems onto robots. The latest IVS-RICi Robot Inspection Cells are included in the article, included highlights relating to a recent machine sold to a major automotive manufacturer. IVS continue to develop state-of-the-art machine vision cameras, systems and machines for use in multi-robot inspection cells.

A copy of the article (in german language), can be found here:

Industrial Vision Systems launches 2019 company brochure with a host of new technologies

Industrial Vision Systems (IVS®), a supplier of vision inspection machines and solutions to industry, is pleased to announce the release of its new 2019 corporate brochure. This 16-page, full-colour brochure presents a sample of the corporation’s strong machine vision products, coupled with the range of inspection machines available from the IVS® portfolio.

The company, based at the Harwell Science and Innovation Campus in Oxfordshire, is using the brochure to provide an insight for readers into its latest developments, training services, and new products which includes Sorting Machines, Thermal Imaging Inspection and Robot Inspection Cells for next generation automated inspection.

The brochure is designed to ensure that readers are provided with information on how IVS® inspection machines recognise, measure, and monitor at dynamic speeds at ultra-high definition levels. Customers around the globe rely on IVS® systems and machinery for robust, automated visual inspection of their manufacturing processes. IVS® machines provide warranty and brand protection, stop fines from customers and consumers, while increasing manufacturing quality.

IVS Director, Earl Yardley comments: “Our new brochure is designed to provide both current and prospective customers with a preview into our very latest vision system capabilities. We have at our disposal vast experience in industrial image processing which comes in the form of a large team of vision engineering specialists who together offer an extraordinary range of capability in machine vision technology and industrial automation. We continue to strengthen our machine portfolio to cover the very latest machine vision algorithms, allowing us to leverage the most up-to-date vision technology for 2019. This visual brochure provides a glimpse into our latest developments, product launches, and application areas.”

To download a copy of the brochure, please visit:
IVS Product Overview PDF

Many UK workers unconcerned about robots taking their jobs

However, findings reveal some misconceptions about the productive role robots can play in the workplace

A survey of over 2,000 UK workers has shown that many are unconcerned about the impact new technology may have on their current job roles. The research, conducted by Industrial Vision Systems (IVS), a supplier of machine vision solutions to industry, found that 39 percent would be happy if a factory used artificial intelligence robots to make decisions on quality control and a further 10 percent would be very happy.

However, in contrast, the research also found some stark misconceptions about the impact robots and artificial intelligence can have in aiding productivity in the workplace. A quarter of employees (25 percent) stated that if they had a robot colleague assisting them at work, they would feel threatened that they might take their job. 22 percent also said that they would be sad that it’s potentially one less person to talk to in the workplace and another 18 percent said that they would be afraid the robot would make a mistake.

In comparison, just 11 percent said that they were confident that the job would be done well if they had a robot colleague assisting them at work, and 13 percent were generally happy with the thought.

IVS provides vision systems for robots which enable companies to enhance their productivity by utilising robots to assist human workers with inspection processes. This relieves the human worker from what you may call more commonplace work, which means they are then deployed to higher value tasks within the workplace. In the future, production inspection will include space for an operator and a robot to work in partnership as part of the quality control process of manufacturing.

Considering the survey findings, IVS believes that working with collaborative robots has the added advantage of working safely and efficiently in workspaces currently occupied by humans and that the current misconception of working with vision enabled robots could hinder productivity levels in various sectors and industries.