IVS helps global contact lens manufacturers achieve error-free lenses (at speed!)

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This month, we are talking about automated visual inspection of cosmetic defects in contact lens inspection, a major area of expertise in the IVS portfolio. Contact lens production falls between medical device manufacturing and pharmaceuticals manufacturing, but it does mean that it falls within the GAMP/ISPE/FDA validation requirements when it comes to manufacturing quality and line validation.

The use of vision technology for the inspection of contact lenses is intricate. Due to the wide variety of lens profiles, powers, and types (such as spherical, toric, aspherical etc.), manufacturers historically relied on human operators to visually verify each lens before it was shipped to a customer. Cosmetic defects in lenses range from scratches and inclusions, through to edge defects, mould polymerisation problems and overall deformity in the contact lens.

The Challenge: Manual Checks are Prone to Human-Error and are Slow!

The global contact lens industry must maintain the highest standards of quality due to the specialist nature of its products. Errors or faults in contact lenses can lead to negative recalls, financial losses, or a drop in brand loyalty and integrity – all issues that could otherwise be very difficult to rectify. However, manually checking lenses has become extremely time-consuming, inefficient, and error-prone. Due to the brain’s tendency to rectify errors naturally, human error is a common cause of oversights. This makes it next to impossible to spot faults by hand, therefore many errors are missed, and contact lens defects can be passed to the consumer.

Coupled with this, many global contact lens manufacturing brands run their production at high speed, which means using human inspectors simply becomes untenable based on the volume and speed of production; so in the last twenty-five years, contact lens manufacturers have moved to high-speed, high-accuracy automated visual inspection, negating the need for manual checks.

To ensure their products’ quality, accuracy, and consistency, many of the world’s top contact lens manufacturing brands have turned to IVS to solve their automated inspection and verification problems. This can be either wet or dry contact lens inspection, depending on the specific nature of the production process.

The Solution: Minimize Errors and Ensure Quality Through IVS Automated Visual Inspection

IVS contact lens inspection solutions have been developed over many years based on a deep and long heritage in contact lens inspection and the required FDA/GAMP validation. The vision system drills down on the specific contact lens defects, allowing finite, highly accurate quality checks and inspection coupled with the latest generation AI vision inspection techniques. This allows the maximum yield achievable, coupled with full reporting and statistical analysis, to give the data feedback to the factory information system, providing real-time, reliable information to the contact lens manufacturer. Failed lenses are rejected, allowing 100% quality inspected contact lenses to be packed and shipped.

IVS have inspection systems for manual load, automated and high-speed contact lens inspection, allowing our solutions to cover the full range of contact lens manufacturers – from small start-ups to the major high-level production volume producers. Whatever the size of your contact lens production, we have validated solutions to FDA/ISPE/GAMP for automated contact lens inspection.

Contact lens manufacturers now have no requirement for manual visual inspection by operators; this can all be achieved through validated automated visual inspection of cosmetic defects, providing high-yield, high-throughput quality inspection results.

More details: https://www.industrialvision.co.uk/wp-content/uploads/2023/05/IVS-Lens-Inspection-Solutions.pdf

Connecting your vision system to the factory, everything you need to know

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There has been a rapid move over the last few years from using vision systems not just for stand-alone, in-line quality control processes, but to drill down and use the information which is created, assessed and archived through the automated vision system.

When vision systems first started being used in numbers in the early 1990’s, the only real option of connection to the production line PLC (programmable logic controller) was through hardwired digital I/O (PNP/NPN), simply acting like a relay to make a decision on product quality and then to trigger a reject arm or air blast to remove the rogue product. The next evolution from there was to start to save some of the basic data at the same time as the inspection process took place. Simple statistics on how many products had passed the vision system inspection and how many had been rejected. This data could then either be displayed on the PLC HMI in simple form or via rudimentary displays on the vision system. From there, communication started with the RS-232 serial line, and this morphed into USB and onto TCP/IP.

The Fieldbus protocol was the next evolution of communication with vision systems. From the standpoint of the user, the fieldbus looked to be state-based, similar to digital I/O. In reality, the data was exchanged serially through a network. Because these messages were sent on a clock cycle, the information was delayed. The benefit of Fieldbus over digital I/O was that each data exchange package of several hundred to 1,000 bytes was exchanged. PROFIBUS, CC-Link, CANopen, and DeviceNet were among the first protocols to be created. While fieldbuses of the first generation utilised serial connections to exchange data, fieldbuses of the second generation used Ethernet. As a result, the technology was sometimes referred to as “Industrial Ethernet” as it evolved, but the term’s meaning is slightly ambiguous.

When compared to serial data transfer, Ethernet enables substantially more data to be sent. However, using Ethernet as a fieldbus medium has the problem of having non-deterministic transmission timings. To obtain enough real-time performance, several Industrial Ethernet protocols such as PROFINET, EtherNet/IP, Ethernet Powerlink, or EtherCAT typically expanded the Ethernet standard (to the Common Industrial Protocol, CIP). For PC-based vision systems, some of these expansions are implemented in hardware, which necessitates the insertion of an expansion card in the vision controller unit.

So, the big evolution came with the support of this “industrial ethernet” communication, so vision systems now use a unified standard supporting real-time Ethernet and Fieldbus systems for PC-based automation. This allows for data to be transferred and exchanged with the PLC through ethernet protocols on a single connection. The benefit is the reduction in complex and costly cabling, easy integration and fast deployment of vision systems. Ethernet is characterised by the large amount of data that can be transferred.

This allows for the seamless and simple integration with all standard PLCs, such as Allen-Bradley, Siemens, Schneider, Omron and others – directly to the vision systems controller unit. Vision system data can be displayed on the PLC HMI, along with the vision HMI, where required.

The most common protocols are:
Profinet — This industrial communications protocol is defined by Profibus International and allows vision systems to communicate with Siemens PLCs and other factory automation devices which support the protocol.

EtherNet/IP — This Rockwell-defined protocol enables a vision system to connect to Allen-Bradley PLCs and other devices.

ModBus/TCP — This industrial network protocol is defined by Schneider Electric and permits direct connectivity to PLCs and other devices over Ethernet.

This progress was and is being accompanied by an increase in the resolutions of industrial cameras. Industrial cameras for machine vision inspection are not always up to date with the commercial world of smartphones. This is largely because the quality necessary for automatic vision assessment is considerably greater than the quality required for merely seeing a photo (i.e. you don’t worry about a dead pixel when looking at your holiday pics!). As a preserved picture archive for end-of-line photo archiving in an industrial environment, high-resolution image quality is now important. In addition to connecting to the line PLC, vision systems must now link to the whole factory network environment and industrial information systems. Vision systems may now interact directly with production databases, with every image of every product leaving the factory gates being taken, recorded and time-stamped, and even linked to a batch or component number via serial number tracking. Now, vision systems are not only used as a goalkeeper to stop bad products from going out the door, but also as a warranty protection providing tangible image data for historic record keeping.

In conclusion, the industrial ethernet connection is now the easiest and fastest way to integrate vision system devices into an automated environment. The ease of connection, the lower cost of cabling, and the use of standard protocols make it a simple and effective method for high-speed communication, coupled with database connections for further image and data collection from the vision system. Vision systems are now linked to complete factory line control and command centres for fully immersive data collection.

How vision systems are deployed for 100% cosmetic defect detection (with examples)

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Cosmetic defects are the bane of manufacturers across all industries. Producing a product with the correct quality in terms of measurement, assembly, form, fit, and function is one thing – but cosmetic defects are more easily noticed and flagged by the customer and end-user of the product. Most of the time, they have no detrimental effect on the use or function of the product, component or sub-assembly, they just don’t look good! They are notoriously difficult for an inspection operator to spot and are therefore harder to check using traditional quality control methods. The parts can be checked on a batch or 100% basis for measurement and metrology checks, but you can’t batch-check for a single cosmetic defect in a high-speed production process. So for mass production medical device manufacturing at 300-500 parts per minute, using vision systems for automated cosmetic defect inspection is the only approach.

From medical device manufacturers, cosmetic defects might manifest themselves as inclusions, black spots, scratches, marks, chips, dents or a foreign body on the part. For automotive manufacturers, it could be the wrong position of a stitch on a seat body, defects on a component, a mark on a sub-assembly, or the gap and flush of the panels on a vehicle.

Using vision systems for automated cosmetic inspection is especially important for products going to the Japanese and the Asian markets. These markets are particularly sensitive to cosmetic issues on products, and medical device manufacturers need to use vision systems to 100% inspect every product produced to confirm that no cosmetic defects are present on their products. This could be syringe bodies, vials, needle tips, ampules, injector pen parts, contact lenses, medical components and wound care products. All need checking at speed during the manufacturing and high-speed assembly processes.

How are vision systems applied for automated cosmetic checks?

The key to applying vision systems for cosmetic visual inspection is the combination of camera resolutions, optics (normally telecentric in nature) and filters to bring out the specific defect to provide the ability to segment it from the natural variation and background of the product. Depending on the type of cosmetic defect that is being checked, it may also require a combination of either linescan, areascan or 3D camera acquisition. For example, syringe bodies or vials can be spun in front of a linescan camera to provide an “unrolled” view of the entire surface of the product.

Let’s drill down on some of the specific elements which are used, using the syringe body as a reference (though these methods can be applied to other product groups):

Absorbing defects

These defects are typically impurities, particulates, fibers and bubble. This needs a lighting technique with linescan to provide a contrasting element for these defects:

Hidden defects

These defects are typically “hidden” from view via the linescan method and so using a direct on-axis approach with multiple captures from an areascan sensor will provide the ability to notice surface defects such as white marks (on clear and glass products) and bubbles.

Cracks and scratch defects

A number of approaches can be taken for this, but typically applying an axial illuminiation with a combination of linescan rotation will provide the ability to identify cracks, scratches and clear fragments.

Why do we use telecentric optics in cosmetic defect detection?

Telecentric lenses are optics that only gather collimated light ray bundles (those that are parallel to the optical axis), hence avoiding perspective distortions. Because only rays parallel to the optical axis are admitted, telecentric lens magnification is independent of object position. Due to this distinguishing property, telecentric lenses are ideal for measuring and cosmetic inspection applications where perspective problems and variations in magnification might result in inconsistent measurements. The front element of a telecentric lens must be at least as large as the intended field of view due to its construction, rendering telecentric lenses insufficient for imaging very large components.

Fixed focal length lenses are entocentric, catching rays that diverge from the optical axis. This allows them to span wide fields of view, but because magnification varies with working distance, these lenses are not suitable for determining the real size of an item. Therefore, telecentric optics are well suited for cosmetic and surface defect detection, often combined with collimated lighting to provide the perfect silhouette of a product.

In conclusion, machine vision systems can be deployed effectively for 100% automated cosmetic defect detection. By combining the correct optics, lighting, filters and camera technology, manufacturers can use a robust method for 100% automated visual inspection of cosmetic and surface defects.

How to calibrate optical metrology systems to ensure precise measurements

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Optical metrology systems play a crucial role in various industries, enabling accurate and reliable measurements for quality control, inspection, and manufacturing processes. To ensure precise and consistent results, it is essential to calibrate these systems meticulously. By understanding the significance of calibration, considering key factors, and implementing regular calibration practices, organizations can optimise the performance of these systems. Accurate measurements obtained through properly calibrated optical metrology systems empower industries to maintain quality control, enhance efficiency, and drive continuous improvement.

How to calibrate your industrial vision system.

We’re often asked about the process for carrying out calibration on our automated vision inspection machines. After all, vision systems are based on pixels whose size is arbitrarily dependent on the sensor resolutions, fields-of-view and optical quality. It’s important that any measurements are validated and confirmed from shift-to-shift, day-to-day. Many vision inspection machines are replacing stand-alone slow probing metrology-based systems, or if it’s an in-line system, it will be performing multiple measurements on a device at high speed. Automating metrology measurement helps reduce cycle time and boost productivity in medical device manufacturing; therefore, accuracy and repeatability are critical.

In the realm of optical metrology, the utilisation of machine vision technology has brought about a transformative shift. However, to ensure that the results of the vision equipment have a meaning that everyone understands, the automated checks must be calibrated against recognised standards, facilitating compliance with industry regulations, certifications, and customer requirements.

The foundational science that instils confidence in the interpretation and accuracy of measurements is known as metrology. It encompasses all aspects of measurement, from meticulous planning and execution to meticulous record-keeping and evaluation, along with the computation of measurement uncertainties. The objectives of metrology extend beyond the mere execution of measurements; they encompass the establishment and maintenance of measurement standards, the development of innovative and efficient techniques, and the promotion of widespread recognition and acceptance of measurements.

For metrology measurements, we need a correlation between the pixels and the real-world environment. For those of you who want the technical detail, we’re going to dive down into the basis for the creation of individual pixels and how the base pixel data is created. In reality, the users of our vision systems need not know this level of technical detail, as the main aspect is the calibration process itself – so you can skip the next few paragraphs if you want to!

The camera sensor is split into individual pixels in machine vision. Each pixel represents a tiny light-sensitive region that, when exposed to light, creates an electric charge. When an image is acquired, the vision system captures the amount of charge produced at each pixel position and stores it in a memory array. A protocol is used to produce a uniform reference system for pixel positions. The top left corner of the picture is regarded the origin, and a coordinate system with the X-axis running horizontally across the rows of the sensor and the Y-axis running vertically down the columns is utilised.

The pixel positions inside the image may be described using the X and Y coordinates in this coordinate system. For example, the top left pixel is called (0,0), the pixel to its right is called (1,0), the pixel below it is called (0,1), and so on. This convention enables the image’s pixels to be referred to in a consistent and standardised manner. So this method provides a way (in combination with sub-pixel processing) to provide a standard reference coordinate position for a set of pixels combined to create an edge, feature or “blob”.

In machine vision, metrology calibration involves the mapping of the pixel coordinates of the vision system sensor back to real-world coordinates. This “mapping” ties the distances measured back to the real-world measurements, i.e., back to millimetres, microns or other defined measurement system. In the absence of a calibration, any edges, lines or points measured would all be relative to pixel measurements only. But quality engineers need real, tangible measurements to validate the production process – so all systems must be pre-calibrated to known measurements.

The process of calibration ensures traceability of measurements to recognised standards, facilitating compliance with industry regulations, certifications, and customer requirements. It enhances the credibility and trustworthiness of measurement data.

How do you calibrate the vision system in real-world applications?

Utilising certified calibration artifacts or reference objects with known dimensions and properties is essential. These standards serve as benchmarks for system calibration, enabling the establishment of accurate measurement references. Proper calibration guarantees that measurements are free from systematic errors, ensuring the reliability and consistency of the data collected. The method of calibration will depend on if the system is a 2D or 3D vision system.

For a 2D vision system, common practice is to use slides with scales carved or printed on them, referred to as micrometre slides, stage graticules, and calibration slides. There is a diverse range of sizes, subdivision accuracy, and patterns of the scales. These slides are used to measure the calibration of vision systems. These calibration pieces are traceable back to national standards, which is key to calibrating the vision inspection machine effectively. A machined piece can also be used with traceability certification, this is convenient when you need a specific measurement to calibrate from for your vision metrology inspection.

One form of optical distortion is called linear perspective distortion. This type of distortion occurs when the optical axis is not perpendicular to the object being imaged. A chart can be used with a pre-defined pattern to compensate for this distortion through software. The calibration system does not compensate for spherical distortions and aberrations introduced by the lens, so this is something to be aware of.

For 3D vision, you no longer need pricey bespoke artefacts or specifically prepared calibration sheets. Either the sensor is factory calibrated or a single round item in the shape of a sphere suffices. Calibrate and synchronise the vision sensor by sending calibrated component measurements to the IVS machine. You will instantly receive visible feedback that you may check and assess during the calibration process.

How often do I need to re-calibrate a vision system?

We often get asked this question, since optimal performance and accuracy of optical metrology systems can diminish over time due to factors like wear and tear or component degradation. Establishing a regular calibration schedule ensures consistent and reliable measurements. However, it’s often down to the application requirements, and the customer needs based on their validation procedures. This can take place once a shift, every two weeks, one a month or even once a year.

One final aspect is storage, all our vision inspection machines come with calibration storage built into the machine itself. It’s important to store the calibration piece carefully, use it as determined from the validation process, and keep it clean and free from dirt.

Overall, calibration is often automatic, and the user need never know this level of detail on how the calibration procedure operates. But it’s useful to have an understanding of the pixel to real-world data and to know that all systems are calibrated back to traceable national standards.

Clinical insight: 3 ways to minimise automated inspection errors in your medical device production line

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Your vision system monitoring your production quality is finally validated and running 24/7, with the PQ behind you it’s time to relax. Or so you thought! It’s important that vision systems are not simply consigned to maintenance without an understanding of the potential automated inspection errors and what to look out for once your machine vision system is installed and running. These are three immediate ways to minimise inspection errors in your medical device, pharma or life science vision inspection solution.

1. Vision system light monitor.
As part of the installation of the vision system, most modern machine vision solutions in medical device manufacturing will have light controllers and the ability to compensate for any changes in the light degradation over time. Fading is a slow process but needs to be monitored. LEDs are made up of various materials such as semiconductors, substrates, encapsulants, and connectors. Over time, these materials can degrade, leading to reduced light output and colour shift. It’s important in a validated solution to understand these issues and have either an automated approach to the changing light condition through close loop control of grey level monitoring, or a manual assessment on a regular interval. It’s something to definitely keep an eye on.

2. Calibration pieces.
For a vision machines preforming automated metrology inspection the calibration is an important aspect. The calibration process will have been defined as part of the validation and production plan. Typically the calibration of a vision system will normally in the form of a calibrated slide with graticules, a datum sphere or a machined piece with traceability certification. Following from this would have been the MSA Type 1 Gauge Studies, this the starting point prior to a G R&R to determine the difference between an average set of measurements and a reference value (bias) of the vision system. Finally the system would be validated with the a Gauge R&R, which is an industry-standard methodology used to investigate the repeatability and reproducibility of the measurement system. So following this the calibration piece will be a critical part of the automated inspection calibration process. It’s important to store the calibration piece carefully, use it as determined from the validation process and keep it clean and free from debris. Make sure your calibration pieces are protected.

3. Preventative maintenance.
Vision system preventative maintenance is essential in the manufacturing of medical devices because it helps to ensure that the vision systems performs effectively over the intended lifespan. Medical devices are used to diagnose, treat, and monitor patients, and they play an important part in the delivery of healthcare. If these devices fail, malfunction, or are not properly calibrated, substantial consequences can occur, including patient damage, higher healthcare costs, and legal culpability for the manufacturer. Therefore, any automated machine vision system which is making a call on the quality of the product (at speed), must be maintained and checked regularly. Preventative maintenance of the vision systems involves inspecting, testing, cleaning, and calibrating the system on a regular basis, as well as replacing old or damaged parts.

Medical device makers benefit from implementing a preventative maintenance for the vision system in the following ways –
Continued reliability: Regular maintenance of the machine vision system can help identify and address potential issues before they become serious problems, reducing the risk of device failure and increasing device reliability.
Extend operational lifespan: Regular maintenance can help extend the lifespan of the vision system, reducing the need for costly repairs and replacements.
Ensure regulatory compliance: Medical device manufacturers are required to comply with strict regulatory standards (FDS, GAMP, ISPE) and regular maintenance is an important part of meeting these standards.

These three steps will ultimately help to lessen the exposure of the manufacture to production faults, and stop errors being introduced into the medical devices vision system production process. By reducing errors in the machine vision system the manufacturer can keep production running smoothly, increase yield and reduce downtime.

Why pharmaceutical label inspection using vision systems is adapting to the use of AI deep learning techniques

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Automated inspection of pharmaceutical labels is a critical part of an automated production process in the pharmaceutical and medical device industries. The inspection process ensures that the correct labels are applied to the right products and that the labels contain relevant and validated information. These are generally a combination of Optical Character Recognition (OCR), Optical Character Verification (OCV), Print Quality Inspection and measurement of label positioning. Some manufacturers also require a cosmetic inspection of the label for debris, inclusions, smudges and marks. The use of vision inspection systems can significantly improve the efficiency and accuracy of the automation process, while also reducing the potential for human error.

Automated vision inspection systems can also help to ensure compliance with regulatory requirements for labelling, and provide manufacturers with a cost-effective and efficient way to improve the quality of their products. With increasing pressure to improve production efficiency and reduce costs, more and more pharmaceutical and medical device manufacturers are turning to automated vision inspection systems to improve their production processes and ensure quality products for their customers.

Over the last few years, more vision inspection systems for pharmaceutical label checks have been adapting to the use of deep learning and artificial intelligence neural networks, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs).

Using AI and deep learning for optical character recognition (OCR) can significantly improve the performance of automated inspection systems for pharmaceutical labels. Traditional OCR systems rely on pre-defined templates and rules to recognize and interpret text on labels, which can be limited in their ability to recognize text accurately in different fonts, sizes, and layouts.

AI-based OCR systems, such as those using deep learning, can be trained on a large dataset of labelled images, allowing them to learn and recognize different fonts, sizes, and layouts of text. This makes them more robust and accurate in recognizing text in real-world scenarios, where labels may have variations in their appearance. The deep learning machine vision systems can also be trained to recognize text that is partially obscured or distorted, which is a common problem in real-world scenarios. This allows the system to make educated guesses about the text, which can improve its accuracy and reliability.

On the flip side, while the level of recognition may improve for hard-to-recognise situations for traditional machine vision inspection, the system also must be validated to set criteria for FDA/GAMP validation. How can this be achieved for a neural deep learning system?

It is true that gathering data for training an AI-based vision inspection system can be a hassle, but it is a crucial step to ensure the system’s performance. One way to overcome this is by using synthetic, validated data, which can be generated using computer-generated images. This can reduce the need for natural images and allows a broader range of variations to be included in the dataset. These synthetic images could be tested and validated on a traditional machine vision set-up, before then transferred to the training set for the AI-based vision inspection. Another way is to use transfer learning, in which a pre-trained validated model is fine-tuned on a smaller dataset of images specific to the task. This can significantly reduce the amount of data and resources needed to train a new model.

In conclusion, validated industries such as medical devices and pharmaceuticals continue to adapt to new, robust methods for traceability and quality print checking. Deep learning is evolving to meet the unique validation requirements of these industries.

Your ultimate guide to GAMP validation for vision systems and industrial automation

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You’re starting a new automation project, and vision inspection is a critical part of the production process. How do you allow for validation of the vision inspection systems and the machine vision hardware and software? This is a question we hear a lot, and in this post we’re going to drill down on the elements of validation in the context of vision systems for the medical devices and pharmaceutical industries. We’ll attempt to answer the questions by providing an overview of GAMP, a guide to key terminology and offering advice on implementing validation for your vision system and machine vision applications.

You’ll hear a lot of terminology and acronyms in industrial automation validation. So we’ll start by explaining some of the terms before drilling down on how the validation process works.

So what is GAMP? Good Automated Manufacturing Practice is abbreviated as “GAMP.” It’s a methodology for generating high-quality machinery based on a life cycle model and the concept of prospective validation. It was developed to serve the needs of the pharmaceutical industry’s suppliers and consumers and is part of any automation project. The current version of GAMP is GAMP 5. The full title is “A Risk-Based Approach to Compliant GxP Computerized Systems”.

The GAMP good practices were developed to meet the Food and Drug Administration’s (FDA) and other regulatory authorities’ (RAs’) rising requirements for computerised system compliance and validation in the industrial automation industry, and they are now employed globally by regulated enterprises and their suppliers.

GAMP was developed by experts in the pharmaceutical manufacturing sector, to aid in the reduction of grey areas in the interpretation of regulatory standards, leading to greater conformity, higher quality, more efficiency, and lower production costs. They tend to be methodological in nature, which in the context of machine vision matches the processes of industrial image processing.

So in layman’s terms, the GAMP and FDA validation is designed to prove the performance of a system in the fundamentals of what it is designed for and lays down the parameters and testing procedures to validate the process across the design, build, commissioning, test and production processes. In machine vision for industrial automation, this will be heavily biased towards the hardware and software specifications, along with the performance criteria for the vision system.

What is GxP that GAMP refers to? Simply, this refers to good practice, with the x in the middle referring to “various fields”. It refers to a set of guidelines and regulations that are followed in industries that produce pharmaceuticals, medical devices, and other products that have a direct impact on the health and well-being of people.

GxP guidelines and regulations are designed to ensure the quality, safety, and efficacy of medical device and pharma products. They cover various aspects of product development, manufacturing, testing, and distribution, as well as the training, qualifications, and conduct of the personnel involved in these processes.

GxP guidelines and regulations are enforced by regulatory agencies, such as the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA), and non-compliance with these guidelines can result in fines, product recalls, and other legal consequences.
There are several different types of GxP, including:

  • GMP (Good Manufacturing Practice): This set of guidelines and regulations covers the manufacturing and testing of pharmaceuticals, medical devices, and other products. This is the most relevant to the validation of vision systems, inspection and machine vision in industrial automation.
  • GCP (Good Clinical Practice): This set of guidelines and regulations covers the conduct of clinical trials involving human subjects.
  • GLP (Good Laboratory Practice): This set of guidelines and regulations covers the conduct of laboratory studies involving the testing of chemicals, drugs, and other substances.
  • GPP (Good Pharmacy Practice): This set of guidelines and regulations covers the practice of pharmacy, including the dispensing and distribution of drugs and other products.

By following GxP guidelines and regulations and validation processes, companies can ensure that their products meet the highest standards of quality, safety, and efficacy, and that they are manufactured, tested, and distributed in a way that protects the health and well-being of their customers.

What is the ISPE? This is the “International Society for Pharmaceutical Engineering”. ISPE is an organisation that bridges pharmaceutical knowledge to improve the development, production, and distribution of high-quality medicines for patients around the world through such means as innovation in manufacturing and supply chain management, operational excellence, and insights into regulatory matters. The ISPE develops the GAMP framework, and GAMP 5 was developed by the ISPE GAMP Community of Practice (CoP).

So the above sets out the framework for validation and why it exists. We’ll now look at what processes are needed to fully validate a vision system into medical device and pharmaceutical manufacturing. On a high level the following critical steps are followed when validating a vision system in production:

1. Define the validation plan: This includes identifying the scope of the validation, the criteria that will be used to evaluate the system, and the testing that will be done to ensure that the system meets these criteria.

2. Install and configure the system: This includes setting up the hardware and software necessary for the system to function correctly, as well as configuring the system to meet the specific needs of the production process.

3. Test the system: This includes conducting functional tests to ensure that the system performs as intended, as well as performance and reliability tests to ensure that the system is accurate and reliable.

4. Validate the system: This includes verifying that the system meets all relevant regulatory standards and requirements, as well as any internal quality standards or guidelines.

5. Document the validation process: This includes documenting the testing that was done, the results of the tests, and any issues that were identified and corrected during the validation process.

6. Implement the system: This includes training production staff on how to use the system and integrating the system into the production process.

7. Monitor and maintain the system: This includes ongoing monitoring and maintenance of the system to ensure that it continues to function correctly and meets all relevant standards and requirements.

But each step needs to be broken down and understood in the context of the validation process relating to GAMP. The following lifecycle shows the typical steps required for the validation of the vision system in production.

What is a URS? A URS, or User Requirement Specification, is a document that outlines the requirements for a product, system, or service. It is typically created at the beginning of a project, and it serves as a roadmap for the development team, providing guidance on what the final product should look like and how it should function.

A URS typically includes a detailed description of the product’s intended use and user base, as well as the specific requirements that the product must meet in order to be successful. These requirements may include functional requirements (e.g., what the product should be able to do), performance requirements (e.g., how fast or accurately the product should operate), and quality requirements (e.g., how reliable or durable the product should be).

The URS is an important tool for defining and communicating the project’s scope and objectives, and it is often used as a reference document throughout the development process. It is typically reviewed and approved by stakeholders, including the development team, customers, and regulatory agencies, before the project begins.

In addition to serving as a guide for the development team, the URS can also be used to evaluate the final product to ensure that it meets all of the specified requirements. This process is known as “verification,” and it is an important step in the quality control process to ensure that the product is fit for its intended use.

The URS is the first document that is required for validation of the vision system (or other industrial automation system), it’s the base document which then leads on the FDS (Functional Design Specification) and the HDS (Hardware Design Specification) and the SDS (Software Design Specification) incorporated as part of that specification. These documents are typically provided by the vision system company as part of the scope of supply of a fully validated vision inspection solution.

What is an FDS? A Functional Design Specification (FDS) is a document that outlines the functional requirements of a product, system, or service. It describes the specific behaviors and capabilities that the machine vision inspection system should have in order to fulfill its intended purpose. The FDS typically includes a detailed description of the vision systems functional requirements, as well as any technical constraints or requirements that must be considered during the design and development of the inspection process.

The purpose of an FDS is to provide a clear and comprehensive understanding of the functional requirements of the overall inspection system, and to serve as a reference for the design and development team as you bring the product to life. It is an important tool for ensuring that the final vision system meets the needs and expectations of the end users.

An FDS may also include information on the vision user interface design, user experience design, and any other aspects of the product that are relevant to its functional requirements. It is typically developed early in the design process and is used to guide the development of the product throughout the project.

Prior to installation, the manufacturer will undertake a Factory Acceptance Test (FAT) which should be documented and agreed between the vision system supplier and the customer.

As the project progresses the applications team need to think about the three major milestones for test, commissioning and integration of the validated vision system – these include:

Installation Qualification (IQ) – this is the process used to verify that the vision system, equipment has been installed correctly and meets all relevant specifications and requirements. It is an important step in the validation process for ensuring that an inspection system is ready for use in a production environment.

During an IQ, a series of tests and checks are performed to ensure that the machine vision system has been installed properly and is functioning correctly. This may include verifying that all required components are present and properly connected, checking that all necessary documentation is complete and accurate, and verifying that the system or equipment meets all relevant safety and performance standards.

The purpose of IQ is to ensure that the vision system is ready for use and meets all necessary requirements before it is put into operation. It is typically performed by a team of qualified professionals from the customers side, and the results of the IQ are documented in a report that can be used to demonstrate that the system or equipment has been installed correctly and is ready for use.

Operational Qualification (OQ) – This is the process used to verify that the vision system complies with the URS and FDS. During an OQ, a series of tests and checks are performed to ensure that the system or equipment operates correctly and consistently under normal operating conditions. This may include verifying that the system or equipment performs all of its required functions correctly, that it operates within specified parameters, and that it is capable of handling normal production volumes.

Performance Qualification (PQ) – This is the final step and is performed by the final user of the vision system. It verifies the operation of the system under full operational conditions. It generally produces production batches using either a placebo or live product, dependent on the final device requirements.

All aspects of the validation approach are captured in the Validation Plan (VP), which, combined with the Requirement Traceability Matrix (RTM) remains live during the project’s life to track all documents and ensure compliance with the relevant standards and traceability of documentation.

Only after the completion of the Performance Qualification is the system validated, and ready for production use.

In summary. The process of validating a vision inspection system in an industrial automation system involves several steps to ensure that the system is accurate, reliable, and meets the specified requirements. These steps can be broadly divided into three categories: planning, execution, and evaluation.

Planning: Before beginning the validation process, it is important to carefully plan and prepare for the testing. This includes defining the scope of the validation, identifying the personnel who will be involved, and establishing the criteria that will be used to evaluate the system’s performance. It may also involve obtaining necessary approvals and documents, such as a validation plan or protocol.

Execution: During the execution phase, the vision inspection system is tested under a variety of conditions to ensure that it is functioning properly. This may include testing with different types of objects, lighting conditions, and image resolutions to simulate the range of conditions that the system will encounter in real-world use. The system’s performance is then evaluated using the criteria established in the planning phase.

Evaluation: After the testing is complete, the results of the validation process are analyzed and evaluated. This may involve comparing the vision system’s performance to the specified requirements and determining whether the system can meet those requirements. If the system fails to meet the requirements, it may be necessary to make modifications or adjustments to the system to improve its performance.

Throughout the validation process, it is important to maintain thorough documentation of all testing and evaluation activities according to GAMP 5 regulations. This documentation can be used to demonstrate the system’s performance to regulatory agencies or other stakeholders, and it can also be used as a reference for future maintenance or upgrades.

The process of validating a vision inspection system in an industrial automation system is a crucial step in ensuring that the system is accurate, reliable, and meets the specified requirements. By following a structured and thorough validation process, companies can ensure that their vision inspection systems are fit for their intended use and can help to improve the efficiency and quality of their operations.

Stop chasing shorts and flash in medical device mould production by using 100% vision inspection

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This month we look at the problem of flash and shorts in injection moulded medical device products and what the benefits are of automating an inspection process for flash and short detection. Medical devices are generally made up of an assembly of smaller plastic components, which together form an assembly. This could be part of a syringe system, testing kit, injection pen, sterile pack, caps, inserts, tubes, inhaler and medical tool holders – or a large assembly, such as breathing apparatus, medical machinery or diagnostic kits. Whatever the assembly or medical kit, the tolerances and fit of the medical device are normally of utmost importance, especially as most devices will be filled with a pharmaceutical drug of some kind or be used in a clinical setting. Flash and shorts could cause leakages, changes to the operation of the device, assembly faults or simply look bad to the user. Flashes and shorts in medical device components need to be rejected before they have a chance of reaching the end-user or the pharmaceutical supplier.

There are many things that can cause flash in plastic injection mouldings, such as mismatched parting lines, bad venting, low clamping pressure, low viscosity, and uneven flow. Some of these problems are caused by the tools, while others are caused by how the tools are used in the moulding machine.

Parting Lines that are mismatched: Dust, dirt, contaminants, and leftovers can make it hard for the two halves of an injection mould to fit together properly. If the holes in the mould are worn, they won’t fit together as well either. The metal surfaces of mould plates can also be deformed by pressure, and complicated part shapes can make it hard to close the mould.

Wrong Venting: Vents that are too big or too small can let out too much or too little air, depending on how old and worn they are. If the vents aren’t deep enough, stiff plastics may be able to stick out, and if the vents aren’t thin enough, fluids may be able to pass through.

Low Clamping Pressure: During injection moulding, the pack/hold phase makes sure that the cavity is completely filled, but it can also force the mould open. Even if the mould halves fit together tightly, plastic could leak out of the mould if the clamping pressure isn’t strong enough to stop this force.

Low viscosity and uneven flow: Depending on how the plastic is processed, it may flow too easily or fill the mould too quickly. For example, melt temperatures that are too high, residence times that are too long, moisture left over from drying that wasn’t done well enough, and using too many coolants.

Shorts, also called “short shots,” are parts that aren’t filled with enough plastic and often don’t have any details because of this. Sinks and parts that aren’t packed tightly enough can sometimes be caused by shorts or be a sign that shorts are starting. There are many things that can cause shorts, and it is important to figure out what they are before trying to fix them. A common cause is that the venting isn’t set up right, so gases can’t escape and plastic can’t flow into the cavity. Another common cause is that the non-return valve on the screw isn’t closing properly, which lets plastic flow backwards as the machine injects.

Many of the actions taken to eliminate flash can result in shorts and vice versa. These actions can be made via changes to the process, mould, or combination of the two. Whatever the solution to eliminate flash and shorts, there’s always the possibility they will be introduced into the product at a later date, and so 100% automated inspection of flash and shorts for medical devices is a perfect solution to stop bad products from getting into the market.

So you have your machine set and production running, but many process engineers end up chasing the process and firefighting the moulding processing, and so moving the goalposts between shorts and flash if the process starts to go out of sync. Using automated inspection can help control this problem and reduce the chase! The vision inspection systems can be interfaced directly with the Injection Moulding Machine (IMM), with products falling out of the tooling on the outfeed conveyor, which in turn can be the in-feed conveyor for a vision inspection machine.

Vision inspection of mould defects is achieved by moving the product either directly into a bowl feed for movement passed the cameras or via using a cleated belt conveyor, allowing the IMM robot to deliver the product to pockets which the vision system robot can pick and introduce into the inspection machine.

Vision systems for flash and short detection can find and measure the fault size, and even the XY location of the flash on the part, allowing statistical process control data of the medical device flash and short position to be logged, allowing process engineering to investigate the root cause of the problem. And remember, it is also possible to check for other defects during the inspection process, so if weld lines, chips, cracks, particulates or distortions are also an issue with the process, these can be incorporated into the automated inspection process. Automated inspection should form part of the validated process for medical device production.

The vision system allows 100% inspection of every moulded product, giving reassurance that the final product delivered to the customer is of the correct quality level, will fit, and won’t cause a leak! This allows you to stop the chase, knowing your flash and shorts will be automatically sorted and rejected at source.

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

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Semi-automatic (or Semi-automated) inspection machines for the pharmaceutical industry are intended for easy and effective examination of vials, ampoules, cartridges, and syringes carrying injectable liquids, powders, or freeze-dried products. This is the step up from a manual inspection approach which would typically be done by a trained inspector by rotating the vial in front of a black and white background for the analyses of certain defects, particles and particulates. The semi-automatic approach allows for higher volumes of pharmaceutical products to be inspected at greater speeds.

In simple terms, “Semi-Automatic” means the ability to rotate the product and present it with the correct lighting LUX level on a black and white background for the purpose of an operator to visually identify defects in the container or contents. This is typically in the form of a stand-alone semi-automated machine or part of an in-feed or in-line production system. Therefore, this shouldn’t be confused with fully automated visual inspection systems.

The key documents relating to the inspection of pharmaceutical vials and ampoules for contamination are driven by the United States Pharmacopeia (USP) Chapter 1 Injections and Implanted Drug Products (Parenterals). Product Quality Tests states that injectable drug preparations should be designed to exclude particulate matter as defined in USP Chapters “787” Subvisible Particulate Matter in Therapeutic Protein Injections, “788” Particulate Matter in Injections, and “789” Particulate Matter in Ophthalmic Solutions. Each final container should be inspected for particulate matter, as defined in Chapter “790” Visible Particulates in Injections. Containers that show the presence of visible particulates must be rejected.

Particulates can take all forms, from contamination and debris particulates, coating fragments, air bubbles or inconsistencies, visual defects in primary containers, polymer particles through to glass & metallic particles and microscopic cracks or inclusions in the glass or polymer. All these levels of defects need to be identified and rejected.

Every product needs to undergo a visual inspection to determine whether or not it contains any particle matter. It is possible to automate, manually perform, or partially automate the initial 100% check. The subsequent inspection to ensure that the acceptable quality level (AQL) has been met must be carried out manually. The presumed value for the lower limit of the visible range is 100µm; however, this value can shift based on the product container, the nature of the drug product, and the qualities of the particulate matter (shape, colour, refractive index etc.).

The sample in its primary container (vial, prefilled syringe, and glass cartridge) is rotated under standardised conditions for semi-automated visual inspection (roughly above 50-100 µm). The liquid in the spinning and/or subsequently stopped container can then be viewed to confirm compliance to quality levels. This is the way to reach regulatory compliance by applying semi-automatic inspection.

Although the regulation of particulate matter in pharmaceutical products is as detailed above, there is no regulatory guidance on either the limits of particulate matter in principal packaging components. Instead, the specifications are determined through collaboration between the customers and the providers. To lessen the amount of particulate matter in finished drug products, pharmaceutical manufacturers and packaging suppliers must work together to achieve this goal. In particular, this can be accomplished by making use of components that have minimal levels of loose, embedded, and adhered particulates. But no matter what contingencies are put in place, the production process should include a level of semi-automatic inspection to allow rouge particulates to be rejected from the final product.

Human inspectors are adaptable and can provide a response to something they have never seen before or something that “doesn’t look right”. They are also able to more easily withstand typical variations in containers, particularly those generated by moulding, which results in a reduction in the frequency of wrongly rejected good products. This compares to fully automated visual inspection, which has rigid boundaries for a pass and fails. However, people are restricted in the number of inspections that can be performed (i.e., the number of containers per minute or hour that they can inspect). Therefore, semi-automatic inspection is the solution to allow a high volume of product to be inspected at speed by an operator while still complying with the relevant regulatory levels.

Operators are also prone to weariness and require numerous pauses in order to work at a high level for extended periods of time. All of these constraints contribute to a greater degree of variation in the outcomes of manual inspections; however, this variation can be reduced by semi-automated inspection, combined with receiving enough training and adhering to standard operating procedures.

Manufacturers should conduct a 100% inspection during the stage at which there is the greatest likelihood that visible particulates will be detected in the final container (for example, before labelling to maximise container clarity). This is the stage at which the likelihood that visible particulates will be detected in the final container is highest. The equipment and the physical environment in which the visual inspection will be performed should both be designed by the manufacturers to reduce the amount of unpredictability in the inspection process while simultaneously increasing the amount of information that can be gleaned from it.

After 100% semi-automated inspection is completed, a manual inspection based on ISO 2859-1/ANSI/ASQ Z1.4 should be performed. The size of the AQL sampling depends on the batch size. Inspection Level II should be used.

Overall, Semi-automatic inspection of pharmaceutical products provides a route to regulatory compliance prior to the adoption of fully automated visual quality control.

3 simple steps to improve your machine vision system

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What’s the secret to a perfect vision system installation? That’s what all industrial automation and process control engineers want to know. The engineering and quality managers have identified a critical process requiring automated inspection, so how do you guarantee that the machine vision system has been set up to the best possible engineering standard. And how can you apply this knowledge to refine the current vision systems on the line? Let’s look at some of the critical parts of the vision inspection chain and see the vital elements and what needs to be addressed. IVS has been commissioning machine vision systems for over two decades, so we understand the core elements inside and out of vision system installs. Use some of our tricks of the trade to improve your vision system. These 3 simple steps to improve your machine vision system will help you understand the immediate changes you can make to improve a vision system.

Step 1 -Lighting and filters.
What wavelength of light are you using, and how does this compare to the sensitivity of the camera, coupled with the colours on the part requiring inspection? An intelligent approach to how segmentation of the scene will take place should be based on the correct lighting set-up, the angle of the machine vision lighting and how the contrast is created based on the colour of the part against the light, either reflecting to or away from the camera. Coloured illumination may intensify or suppress defined colours in monochrome imaging. This way, the contrast helps recognise relevant features, which is decisive for an application-specific and optimally matched vision solution. For example, blue light cast onto a multi-colour surface will only be reflected by the blue content. The more blue content in the object, the more light is reflected, so the brighter the object will appear. In contrast, red content illuminated in blue appears exceptionally dark.

Combining, a filter mounted on the end of the lens allows blocking all unwanted ambient light and passing only the necessary wavelength of light required, increasing contrast and resolution. The choice of which filter to use depends on the type of result you are trying to achieve. But selecting an appropriate filter may improve your current system’s quality if ambient light is a problem for you.

Bandpass filters are frequently one of the simplest methods to significantly improve image quality. They have a Gaussian transmission curve and are ideal for investigating the effects of monochromatic imaging. But what are the different filters available?

Multi Bandpass Filters
Multi-Bandpass Filters use a single filter to transmit two or more distinct wavelength bands. They provide natural colour reproduction during the day and near-infrared illumination at night, resulting in accurate, high-contrast pictures. As a result, these are more suited for applications including intelligent traffic solutions, security monitoring, and agricultural inspection.

Longpass Filters
Longpass Filters enable a smooth transition from reflection to transmission and are available in various wavelengths based on the system’s requirements. Longpass filters are an efficient method for blocking excitation light in fluorescent applications and are best employed in a controlled environment where several wavelengths must be passed.

Shortpass/NIR Cut Filters
Shortpass filters offer an unrivalled shift from transmission to reflection and superb contrast. They work best in colour imaging to ensure natural colour reproduction and prevent infrared saturation.

Polarising Filters
Polarising filters minimise reflection, increase contrast, and discover flaws in transparent materials. They’re ideally suited for tasks requiring a lot of reflection, such as analysing items on glossy or very shiny surfaces.

Neutral Density Filters
Neutral Density Filters are known in the machine vision industry as “sunglasses for your system” because they diminish light saturation. Absorptive and reflective styles are available and are ideally suited for situations with excessive light. They offer excellent methods for controlling lens aperture and improving field depth.

So how do you match the filter to the light? Well, the LED’s nanometers (nm) wavelength typically relates to the band pass rating for the filter. So if you work in UV at 365 or 395nm, you would use an equivalent rated filter – BP365. If you’re using a green LED at 520nm, this would be a 520 band pass filter, and so forth. According to the filter rating, most filters have a curve chart to see where the light is cut-off.

Step 2 Camera resolution and fields of view (fov)
A vision system’s spatial resolution refers to the sensor’s active available pixels. When reviewing an improvement in your set-up and understanding where the fault may lie, it’s important to consider how many pixels you have available for image processing relative to the size of the defect or computation you are trying to achieve. For example, if you are trying to defect a 100 micron (0.1mm) x 100 microns (0.1mm) inclusion on the surface of a product, but the surface is 4m x 4m in size, by using a standard 5MP camera (say 2456 x 2054 pixels) you will only be able to resolve a single pixel to at best 4000/2054 ~ so 1.9mm. This is nowhere near the level of detail you need for your imaging system. So to improve your machine vision system, see what size of a defect you need to see and then work out your field of view (fov) relative to the pixels available. But bear in mind having a single-pixel resolution for your measurement is not good enough, as the system will fail the Measurement Systems Analysis (MSA) and Gauge R&R studies (These are tests used to determine the accuracy of measurements).

In this case, you need to be at a minimum of 20 pixels across the measurement tolerance – so 0.2/20 = 0.01mm. Therefore, you will need 10 microns per pixel for accurate, repeatable measurement – over 4m; this is 400,000 pixels. So the 5MP pixel camera original specified will never inspect one shot – the camera or the object will have to be moved in small fields of view or larger resolution cameras specified for the system to hit the inspection criteria.

Step 3 Vision algorithm optimisation
One of the major steps for improving a machine vision system is a thorough review of the vision system software and the underlying sequence of inspection for the image processing system. If the system is a simple, intelligent vision sensor, it might be a matter of reviewing the way the logical flow of the system has been set. Still, for more complex PC-based vision applications, there will be some fundamental areas which can be reviewed and checked.

1. Thresholding. Thresholding is a simple look-up table that places pixel levels into a binary 1 and 0 depending on where they sit above, below or in-between two levels. The resulting image is a pure black and white image, then used to segment an area from the background from another object. Dynamic thresholding is also available, which intuitively sets the threshold level based on the pixel value distribution. But because it uses the fundamental pixel grey level to determine the segmentation, it can be easily affected by changes in light, as this will ultimately change the pixel value and lead to changes required to the threshold level. This is one of the most common faults in a system which has not been set up with a controlled lighting level. So if you are reviewing a threshold level change, you should also check the lighting and filters used on the system, as seen in Step 1.

2. Calibration. Perhaps not calibration in the true sense of the word, but if the camera on your vision system has been knocked out of place (even slightly), it may have detrimental effects on the inspection outcome and the yields of the vision system. Re-calibration may need to be done. This could initially be checked, as most modern systems have a cross-hair type target built into the software to allow a comparison image from the original system install to be compared with a live image. Check the camera is in-line, and the field of view is still the same. Focus and light should also be consistent. Some measurement-based machine vision systems will come shipped with a calibration piece or a Dot and Square Calibration Target. These generally come with a NIST Traceability Certificate, so you know that the artefact is traceable to national standards. This calibration target should be placed, and the system checked against it.

So these 3 simple steps to improve your machine vision system may just help you increase your vision system’s yield and stop the engineering manager from breathing down your neck. This will ultimately enhance your production productivity and advance the systems in use. And remember, you should always look at ways to reduce false failures in a vision system; this is another way to boost your vision system performance and improve yield.

How to automate Critical to Quality (CTQ) metrology checks

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

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

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

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

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

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

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

The economic case for investing in machine vision systems is usually strong due to the two key following areas:

  1. Cost savings through reducing labour, re-work/testing, removing more expensive capital expenditure, material and packaging costs and removing waste
  2. Increased productivity through process improvements, greater flexibility, increased volume of parts produced, less downtime, errors and rejections

However, just viewing the benefits from an economic perspective does not do justice to the true value of your investment. Machine vision systems can add value in all of the additional following ways. Unfortunately, due to the intangible nature of some of these contributors it can be difficult to put an actual figure on the value but that shouldn’t stop attempts to include them. These five pillars of payback are critical in understanding the economic benefits of installing vision systems and are the best way to assess how machine vision can impact your organisations bottom line.

Intellectually

  • By freeing staff from repetitive, boring tasks they are able to focus thinking in ways that add more value and contribute to increasing innovation. This is good for mental health and good for the business.
  • By reducing customer complaints, product recalls and potential fines automated inspection can help to build and protect your brand image in the minds of customers
  • Building a strong image in the minds of potential business customers through demonstrating adoption of the latest technology, particularly when they come and visit your factory!
  • Through the collection of better data and improved tracking machine vision can help you develop a deeper understanding of your processes

Physically

  • The adoption of machine vision can help to complement and even improve health and safety practice
  • Removing operators from hazardous environments or strenuous activity reduces exposure to sickness, absence, healthcare costs or insurance claims

Culturally

  • Machine vision can contribute and even accelerate a culture of continuous improvement and lean manufacturing
  • Through increased competitiveness and improving service levels machine vision helps build a business your people can be proud of

Environmentally

  • Contributing to a positive, safe working environment for staff
  • Through better use of energy and resources, smoother material flow and reduced waste machine vision systems can help reduce your impact on the environment

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

However, even in the case of high levels of capital investment it should be obvious, from the potential benefits outlined above, that a machine vision system can quickly pay for itself.