Robot vision trends in medical device production.

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Medical device manufacturing continues to drive the adoption of robot vision to increase throughput, increase yield and reduce the reliance on manual processes. Many production facilities have now turned to collaborative robots which allow operations in medical device production to be taken to the next level of optimisation.

Cobots with vision, tend to work hand in hand with humans balancing the imperative for safety with the need for flexibility and productivity. Robots no longer need to work alone. Collaborative robots are generally designed with inherent safety so they can work alongside humans where possible. Collaborative automation means greater speed and efficiency. Of course, a thorough risk-assessment is still required but the functionality within these robots leads them to be assessed safe once the surrounding conditions and operationality capability are tuned to the safety needs (including any gripper or end-effector design).

Cobots are designed for low payload applications such as handling small parts and inspection tasks so are ideal for medical production processes. Companies continue to look for ways to improve their efficiency by using robot with vision systems, allowing automation of current manual processes to produce productivity gains. Cobots provide a lower price point and lower integration cost to the customer, providing a faster return on investment that unlocks many more industrial tasks for medical device production automation.

Some examples of the current trends in medical device manufacturing for adopting robot vision solutions include:

Machine Tending

Cobot vision removes the need for many operators to be occupied tending to production machinery, feeding and general assembly operations. The cobot can open and close a machine door, pick and place parts for assembly, and even start a machining process by pushing the start button.

Palletising

The ability to stack and un-stack pallets is another requirement, with the vision system providing real-time off-sets and alignment for the pallet stack. A compact robotic cell that accurately loads products onto a pallet, reduces the need for rework while also allowing staff to work safely around the robot. Using cobots with vision within a robot cell allows safe interaction with staff members. Personnel can enter the robot’s operational area to speed up the pallet changes, providing a robotic solution that can work around staff without risking their safety.

Box Erection

Taking flat boxes off a stack and erecting them into a useable format as outer shipper cases is a repetitive and time-consuming action in industry to which cobots with vision are very well suited. Medical device manufacturers need not only to erect the boxes and casings, but print and confirm the serial batch numbers and use by date on the product, all of which can be completed by the same machine vision inspection system.

Functional Repetitive Testing

Manufacturers need to put their products through thousands of hours of test cycling to provide reliability, for example the continued movement of a syringe body or an asthma inhaler valve. Collaborative robots with vision can performs hundreds of hours of testing to support faster testing, improving reliability and quality.

Structured Bin Picking

Parts in a structured position allowing single picks from known datums can be easily completed with cobots and smart vision sensors. Pick and place into a known fixture or position allows integration of such solutions in complex automation production processes.

Unstructured Random Bin Picking

Bin picking from a random box of parts can now be accomplished with cobots and 3D vision. A point cloud of known data, identifying individually pickable parts is fed to the robot allowing parts which would have previously required a human picker to now to picked and placed into the next process.

Robots with machine vision will continue to dominate the foreseeable needs for automation of production in medical device manufacturing. With the drive for ever greater flexibility and the need to reduce manpower and increase reliability, this area of automation will continue to be a focus for the medical devices and pharmaceutical industries.

Why the Human Machine Interface (HMI) really does matter for vision systems.

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Control systems are essential to the operation of any factory. They control how inputs from various sources interact to produce outputs, and they maintain the balance of those interactions so that everything runs smoothly. The best control systems can almost appear magical; with a few adjustments here and there, you can change the output of an entire production line without ever stopping. This is just as true for the vision inspection process and machinery.

The human machine interface (HMI) in a quality control vision system is important to provide feedback to production, monitor quality at speed and provide easy to see statistics and information. The software interface provides the operator with all information they need during production, guiding them through the process and providing instructions when needed. It also gives them access to a wide array of tools for adjusting cameras or other settings on the fly, as well as useful data such as machine performance reports.

If a product has been rejected by the inspection machine it may be some time before an operator or supervisor can review the data. If the machine is fully autonomous and communicates directly with the factory information system the data might be automatically sent to the factory servers or cloud for later recall – this is standard practice in most modern production facilities. But the inspection machine HMI can also provide an immediate ability to recall the vision image data, detailed information on the quality reject criteria and can be used to monitor shift statistics. It’s important that the HMI is clearly designed, with ergonomics and ease-of-use for the shop floor operator as the main driver. For vision systems and machine vision technology to be adopted you need the buy-in from operators, the maintenance team and line supervisors.

The layout of the operator interface is important to give immediate data and statistical information to the production team. It is important to have an interface that can be easily read from a distance, and displays the necessary information in a single screen. During high-speed inspection operations (such as medical device and pharmaceutical inspection operations) it is not possible to see every product inspected, that’s why a neatly designed vision system display, showing the last failed image, key data and statistical process control (SPC) information provides a ready interface for the operator to drill down on process specifics which may be affecting the quality of the product.

It’s clear why the HMI in quality control vision systems are so crucial to production operations. They help operators see important manufacturing inspection data when necessary, recommend adjustments on the fly, monitor production in real-time and provide useful data about performance all at once.

Top 6 medical device manufacturing defects that a vision system must reject

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What does every medical device manufacturer crave? Well, the delivery of the perfect part to their pharmaceutical customer, 100% of the time. One of the major stumbling blocks to that perfect score and getting a Parts Per Million (PPM) rate down to zero are the scratches, cracks and inclusions which can appear (almost randomly) in injection moulded parts. Most of the time, the body in question is a syringe, vial, pen delivery, pipette or injectable product which is transparent or opaque in nature, which means the crack, inclusion, or scratch is even more apparent to the end customer. Here we drill down on the top six defects you can expect to see in your moulded medical device and how vision systems are used to automatically find and reject the failure before it goes out of the factory door.

1. Shorts, flash and incomplete junctions.

Some of the failures are within the tube surface and are due to a moulding fault in production. For example, incomplete injections in mouldings create a noticeable short shot in the plastic ends of the product. The forming of junctions at the bottom end of the tube creates a deformation that must be analysed and rejected. Flash is characterised as additional material which creates an out of tolerance part.

2. Scratches and weld lines

These can be present on the main body, thread area or top shaft. They often have a slight depth to them and can be evident from some angles and less so from others. These could also be confused with weld lines which can run down the shaft or body of a syringe product, but this is also a defect that is not acceptable in production.

3. Cracks

Cracks in a medical device can have severe consequences to the patient and consumer; this could allow the pharmaceutical product to leak from the device, change the dosage or provide an unclean environment for the drug delivery device. Cracks could be in the main body or sometimes in the thread area, or around the sprue.

4. Bubbles, Black Spots and Foreign Materials

Bubbles and black spots are inclusions and foreign material, which are not acceptable on any product, but on a drug delivery device that is opaque or clear in nature, they stand out a lot! These sort of particulates are typically introduced through the moulding process and need to be automatically rejected. Often, the cosmetic nature for these sorts of inclusions is more the issue than the device’s functional ability based on the reject.

5. Sink marks, depressions and distortions

Sink marks, pits, and depressions can cause distortion in the components, thus putting the device out of tolerance or off-specification from the drawing. These again are caused by the moulding process and should be automatically inspected out from the batch.

6. Chips

Chips on the device have a similar appearance to short shots but could be caused on exit from the moulding or via physical damage through movement and storage. Chips on the body can cause out of tolerance parts and potential failure of the device in use.

But how does this automatic inspection work for all the above faults?

Well, a combination of optics, lighting and filters is the trick to identifying all the defects in medical plastic products. This combined with the ability to rotate the product (if it’s cylindrical in nature) or move it in front of many camera stations. Some of the critical lighting techniques used for the automated surface inspection of medical devices are:

Absorbing – the backlight is used to create contrasts of particulates, bubbles and cosmetic defects.
Reflecting – Illumination on-axis to the products creates reflections of fragments, oils and crystallisation.
Scattering – Low angle lighting highlights defects such as cracks and glass fragments.
Polarised – Polarised light highlights fibres and impurities.

These, combined with the use of telecentric optics (an optic with no magnification or perspective distortion), allows the product to be inspected to the extremities, i.e. all the way to the top and to the bottom. Thus, the whole medical device body can be examined in one shot.

100% automated inspection has come a long way in medical device and pharmaceutical manufacturing. Utilising a precision vision system for production is now a prerequisite for all medical device manufacturers.

How to run a Gauge R & R study for machine vision.

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When you’re installing a vision system for a measuring task, don’t be caught out with the provider simply stating that the pixel calibration task is completed by dividing the number of pixels by the measurement value. This does provide a calibrated constant, but it’s not the whole story. There is so much more to precision gauging using machine vision. Measurement Systems Analysis (MSA) and in particular Gauge R&R studies are tests used to determine the accuracy of measurements. They are the de-facto standard in manufacturing quality control and metrology, and especially relevant for machine vision-based checking. Repeated measurements are used to determine variation and bias. Analysis of the measurement results may allow individual components of variation to be quantified. In MSA accuracy is considered to be the combination of trueness (bias) and precision (variation).

The three most crucial requirements of any vision gauging solution are repeatability, accuracy and precision. The variance of repeated measurements, or repeatability, refers to how near the measurements are to each other. The accuracy of the measurements refers to how close they are to the genuine value. The number of digits that can be read from the measurement gauge is known as precision.

A Gauge R & R (also known as Gage R & R) repeatability and reproducibility is defined as the process used to evaluate a gauging instrument’s accuracy by ensuring its measurements are repeatable and reproducible. The process includes taking a series of measurements to certify that the output is the same value as the input, and that the same measurements are obtained under the same operating conditions over a set duration.

The “repeatability” aspect of the GR&R technique is defined as the variation in measurement obtained:

– With one vision measurement system
– When used several times by the same operator
– When measuring an identical characteristic on the same part

The “reproducibility” aspect of the GR&R technique is the variation in the average of measurements made by different operators:

– Who are using the same vision inspection system
– When measuring the identical characteristic on the same part

Operator variation, or reproducibility, is estimated by determining the overall average for each appraiser and then finding the range by subtracting the smallest operator average from the largest.

So, it’s important that vision system measurements are checked against all of these aspects, so a bias test, process capability and gauge validation. The overall study should be performed as per MSA Reference Manual 2010 fourth edition.

More information on IVS gauging and measuring can be found here.

Why using vision systems to capture the cavity ID in injection moulded parts helps you stay ahead of the competition.

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Machine vision systems are excellent at analysing products, capturing data and providing a large database of useful statistics for your operations and production manager to pore over. For products which are injection moulded we often get tasked with measuring key dimensions, gauging a spout or find the rogue short shot on a medical device. Normally this is at speed, with products running directly out of the Injection Moulding Machine (IMM), up a conveyor and into the vision input (normally a bowl feed, robot or conveyor), with the added bonus of images saved for reference as the automatic inspection takes place. If you’ve had a failure, it’s always a good idea to have the photo of the product to show to the quality team and for process control feedback. Let’s face it, the vision system is a goal keeper, and you need to feedback to the production team to help improve the process.

But what if the failure is intermittent and not always easy to capture? Your quality engineers may be scratching their heads, wondering why there is a product failure every now and then. Is there any way this can be tracked back to source? The neat answer is to complete cavity identification (ID) during the inspection process. The tooling for an IMM can include a cavity number so that each individual product has a unique reference. This is used for other forms of quality feedback, such as reviewing tool wear, tooling failures, short-shot imbalance and general troubleshooting for injection moulded products. So, if the cavity Identification number can be read at speed, saved and the data tracked against it, you start to see a picture of how your process is running, spikes in quality concerns related to a particular cavity, and ultimately full statistical process control of each tool cavity. Utilising precision optical character recognition allows vision systems to read each cavity number (or letter/or combination), to drill down the data to an individual cavity within the tool.

So next time the quality director comes down onto the shop floor asking what cavities are giving you problems, you’ll have all the data to hand (plus the photos to really wow them!).

Why bin picking is one of the most difficult vision system tasks (and how to overcome it!).

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Autonomous bin picking, or the robotic selection of objects with random poses from a bin, is one of the most common robotic tasks, but it also poses some of the most difficult technological challenges. To be able to localise each part in a bin, navigate to it without colliding with its environment or other parts, pick it, and safely place it in another location in an aligned position – a robot must be equipped with exceptional vision and robotic intelligence.

Normally the 3D vision system scanner is mounted in a fixed, stationary position in the robotic cell, usually above and in-line with the bin. The scanner must not be moved in relation to the robot after the system has been calibrated. As a general rule of thumb, the more room is required for the bin picking application – including the space for robot movement, the size of the bin and parts, etc. – the larger model of the machine vision scanner required. So more resolution and pixels equates in simple terms to more precision and accuracy.

Calibration is completed with any suitable ball attached to the endpoint of the robotic arm or to the gripper. The ball needs to be made of a material that is appropriate for scanning, which means it needs to be smooth and not too reflective.

One of the problems with this approach is that the 3D vision system itself could cast a shadow on the bin and inhibit a high-quality acquisition of the scene. This problem is usually solved by making a compromise and finding the most optimal position for the scanner in relation to the bin or by manually rearranging the parts within the bin so that the vision system captures them all in the end. But is there another way?

A way to overcome this is to mount the 3D vision system on the robot itself. Of course, there are certain prerequisites to this approach (i.e. the robot can cope with the additional weight, there is room for mounting and there is cycle time available for movement), but there is some functional advantages to this approach.

For a successful calibration, the scanner must be mounted behind the very last joint (e.g. on the gripper). Any changes made to the scanner’s position after the calibration renders the calibration matrix invalid and the whole calibration procedure must be carried out again. This sort of calibration is done with a marker pattern – a flat sheet of paper (or another material) with a special pattern recognized the 3D vision system.

So what are the advantages? Well, you can’t scan your large bin with a smaller (and so lower cost) vision system scanner, because your scanner is mounted above it and its view is fixed. A small scanner mounted directly on the robotic arm allows you to get closer to the bin and choose which part of it to scan, thus potentially saving costs and helping with resolution.

Robotic mounted bin picking may also eradicate the need to darken the room where the robotic cell is located. The ambient light coming from a skylight might pose serious challenges to the deployed 3D vision system. A scanner attached to the robot can make scans of a bin from one side first and then from another, minimising the need to make any unusual adjustments to the environment.

It can also happen that the 3D vision system itself casts shadows on the bin and inhibits a high-quality acquisition of the scene. This problem is usually solved by making a compromise and finding the most optimal position for the scanner in relation to the bin or by manually rearranging the parts within the bin so that the vision system captures them all in the end. Robot mounted picking eliminates this problem as it enables the scanner to “look” at the scene from any angle and from any position.

In conclusion, there are many approaches to automated bin picking using 3D vision systems, each with their own unique approaches dependent on the environment, industrial automation needs, cost and the cycle time available for picking.