Tag: Vision Tips

Applying automated vision metrology technologies for process control before, during and after assembly and machining/moulding is now a prerequisite in any production environment, especially in the orthopaedic and additive manufacturing industries. This month, we’re drilling down on the data and knowledge you can create for your manufacturing process when deploying vision metrology systems for production quality control. Let’s face it – we all want to know where we are versus the production schedule!

What are “vision metrology” systems and machines?

It’s the use of non-contact vision systems and machines using metrology grade optics, lighting and software algorithms to allow increased throughput of quality control inspection and data collection in real-time for the production process.

What are the benefits of vision metrology systems?

Before we look at the data available on such systems and how they can be utilised to create data-driven production decisions, we should look at the benefits of automated vision metrology systems. This, combined with the data output, clearly focuses on why such systems should be deployed in today’s production environments.

Increased throughput. One of the main drivers and benefits of using automated vision metrology systems is the higher throughput compared to older contact and probing CMM approaches. Coupled with integration into autoload and autotending options, the higher production rates of deploying automated vision metrology allow for faster production and increased output from the factory door.

Better yields. As the systems are non-contact, there is no chance of damage or marking of the product, which is the risk with probing and contact inspection solutions. Checking quality through automated vision allows yields to improve across the production process.

Faster reaction to manufacturing issues. Data is king in the fast-moving production environment. Real-time defect detection, seeing spikes in quality and identifying quality issues quicker, allows production managers to react faster to changes or faults in manufacturing faults. Vision metrology allows immediate review and analysis of statistical process control on a batch, shift and ongoing basis. Monitor live progress of work orders as they happen.

Less downtime. With no contact points and fewer moving parts, maintenance downtime is minimal when applying automated vision metrology machines. Maintenance can be used for other tasks and increases productivity across the manufacturing floor.

Guarantees your quality level. Ultimately, the automated vision metrology machine is a goal-keeper. This allows the quality and production team to sleep easy, knowing that parts are not only being inspected, but also providing warranty protection via a photo save of every product through production.

How do we get data from an automated vision metrology quality control system?

This is where the use of such vision technology gets interesting. The central aspect is the quality control and automated checking of crucial measurements (usually critical to quality CTQ characteristics). Still, the data driving the quality decision is paramount for the manager. The overall data covers a range of data objects that could potentially be needed when running a modern manufacturing plant. These include OEE data, SPC information, shift records, KPI metrics, continuous improvement information, root cause analysis, and data visualisation.

OEE Data (Overall Equipment Effectiveness). The simplest way to calculate OEE is the ratio of fully productive time to planned production time. Fully productive time is just another way of saying manufacturing only good parts as quickly as possible (at the ideal cycle time) with no stop time. A complete OEE analysis includes availability (run time/planned production time), performance ((ideal cycle time x total count)/run time) and quality (good count/total count). The vision metrology data set allows this data to be easily calculated. The data is stored within the automated vision metrology device or sent directly to the factory information system.

Statistical Process Control (SPC) is used in industrial manufacturing quality control to manage, monitor, and maintain production processes. The formal term is “the use of statistical techniques to control a process or production method”. The idea is to make a process as efficient as possible while producing products within conformance specifications with as little scrap as possible. Vision metrology allows vital characteristics to be analysed at speed on 100% of product, compared to the slow, manual load CMM route. SPC data can be displayed on the vision system HMI, and immediate decisions can be made on the process performance and data presentation.

Shift records are part of the validation and access control system for the vision system used in vision metrology. This stops operators from accessing certain features and logging all information against a specific shift operator or team. Data analytics allow trends in operator handling and contribute to the data provided by the system. You may see a spike in quality concerns from a specific shift or operator; these trends can easily be tracked and traced.

KPI metrics are Key Performance Indicators normally specific to the manufacturing site. However, these invariably include monitoring the quality statistics, OEE, process parameters, environmental factors (e.g. temperature and humidity), shift data, downtime and metrology measurements. This is all part of understanding how certain situations impact the output from processes and how levels can be maintained.

Continuous Improvement is the process to improve a manufacturing facility’s product and service quality. Therefore, the data and images from any automated vision metrology machine play an important role in providing live data related to Six Sigma and real-time monitoring of the ppm (parts per million) failure rate within the facility.

Root cause analysis of manufacturing issues is challenging without tangible data from a quality inspection source. The ability to review product images at the point of inspection (with the date and time stamped information), coupled with the specific measurement data, allows easier and quicker root cause analysis when problems crop up.

Data visualisation is now built into modern automated vision metrology systems. The production manager and operators can see quality concerns, spikes in rejects, counts of good and bad, shift data, and live SPC all in a single screen. This can be displayed locally or sent immediately to the factory information system.

By utilising the latest-generation automated vision metrology systems manufacturers can now build a fully-integrated data driven production environment. They are allowing easier control based on accurate, immediate information and photos of products running through production.

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.

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.

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.

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.