Logan Welch, Author at AUTOMATION INSIGHTS

Flush, Non-Flush, or Quasi-Flush: Choosing the Right Proximity Sensor for Optimal Object Detection

Proximity sensors are aptly named for their ability to detect objects in close proximity. They are not suitable for detecting objects across a room or on a conveyor belt. Their focus is on detecting objects up close and personal. Inductive proximity technology allows detection from physical contact with the sensor head to a few millimeters away. When choosing the right type of inductive proximity technology, several factors must be considered. Let’s start at the beginning.

Inductive proximity sensors may seem magical, but they operate based on specific magical characteristics. To prove my point, show them (and try to explain them) to a kid. Imagine an invisible electromagnetic field surrounding the sensors. This field can only be disrupted by a metal target. Different metals can affect this field at varying distances, depending on the type of metal and the sensor used. In simple terms, the sensor can detect if an object is a metal and, to some extent, the type of metal– all without touching the object physically.

Now that we’ve covered the basics, let’s focus on understanding the characteristics of the magical electromagnet field, its impact on sensing range, mounting, and the risks of sensor and/or part damage.

You may have heard the terms flush or non-flush used for inductive proximity sensors. I’ll throw one more into the mix: quasi-flush.

Non-flush mounting

Non-flush mount proximity sensors offer the longest range – the air gap between the target and the sensing head. This can be a good thing or a bad thing, depending on the situation. For precise positioning requirements, the extra range might cause issues. However, if precision is unnecessary, the extended ranges could be beneficial as objects might come into range slightly differently. One major downside of non-flush sensors is their susceptibility to damage. Typically, several millimeters to half an inch of the sensing head is exposed, increasing the risk of shearing off the sensor head or damaging the object you are detecting.

Flush mount proximity

With flush-mount proximity, you gain some protection for both the sensor head and the object being detected, but it comes with a trade-off of reduced sensing range. This is because the shape of the electromagnetic field coming out of the sensing head is focused to avoid triggering the mounting block or other hardware.

Quasi-flush mounting

If you are looking for a Goldilocks solution, consider quasi-flush mounting. With this style of sensing head, you recess the sensor into a mounting block, which helps focus the electromagnet field a bit more, thereby adding more field length compared to a flush mount. It is important to ensure your mounting block has a bevel around the sensing head to avoid false triggers of the output.

So, when deciding which type to use, I recommend using flush or quasi-flush sensors for any target that may come into contact with the sensing head. This choice will prolong the sensor’s life and better ensure proper target triggering. Non-flush sensors are great when you need a larger gap between the target and the sensing head, and precision is not a big issue.

In closing, proximity sensing is designed to be a non-contact form of object detection, specifically metal objects. The goal is to avoid any contact with the sensing head, although we’re aware that object/sensor collisions can happen.

Which One Is the Salad Fork Again? Fork Sensors in Modern Factory Automation

You’ve probably heard of forklifts, salad forks, forks in the road, even forked tongues, but what do forks have to do with factory automation and object detection? I’ll get to the answer, but for now, let’s talk about arguably the most reliable form of object detection: through-beam photoelectric sensors.

An unsung hero of reliable object detection is the through-beam photoelectric sensor. Its operation is simple: an emitter sends light to a receiver. No reflectors, no fancy, high-tech lasers, and very few material limitations are involved. If the emitter and receiver are properly aligned, and within their designated range, the sensor is happy and will function well. It detects when the transmitted light is blocked.

The Achilles heel of through-beam sensors: why alignment matters

You probably have a through-beam photoelectric sensor at the bottom of your garage door. Garage door companies use this technology because it’s both reliable and inexpensive, with the power to span large distances. However, the Achilles heel of through-beam sensors is their vulnerability to misalignment. Whether it’s a complex light curtain or a simple garage door safety switch, ensuring the alignment of the emitter and receiver is key for the sensor’s reliability. Proper alignment also takes up more time during installation and may cause issues during production. Misalignment can occur whether a kid is hitting the emitter with her bike in the garage or a production worker is hanging his coat on the sensor on the factory floor.

The evolution of object detection: introducing the “fork” sensor

Imagine having the benefits of a through-beam sensor but without the hassles of installation and the risk of production disruptions. This is the key principle behind one of my favorite types of sensors designed for factories: the fork sensor. It consists of an emitter and a receiver set at a fixed distance and pre-aligned at the factory, all enclosed within a fork-shaped housing. These sensors are available in various spacings, each optimized for reliable object detection and greatly reducing the chances of errors. The beauty of this housing is that it allows for single-point mounting and provides protection for the vital parts of the sensor, preventing them from harm and being knocked out of alignment.

The use of forks in eating separates us from our ancient ancestors. We have evolved even more over the years to use different-sized forks for different courses and types of foods. Like the title of this article suggests, I can never remember which one is the salad fork. Think about the benefits of my favorite sensor type, the “fork” sensor, and see if it could make your automation process more civilized.

Waterways: the Many Routes of Water Detection

Water is everywhere, in most things living and not, and the amount of this precious resource is always important. The simplest form of monitoring water is if it is there or not. In your body, you feel the effects of dehydration, in your car the motor overheats, and on your lawn, you see the dryness of the grass. What about your specialty machine or your assembly process? Water and other liquids are inherently clear so how do you see them, especially small amounts of it possibly stored in a tank or moving fast? Well, there are several correct answers to that question. Let’s dive into this slippery topic together, pun intended.

While mechanical float and flow switches have been around the longest, capacitive, photoelectric, and ultrasonic sensors are the most modern forms of electronic water detection. These three sensing technologies all have their strong points. Let’s cover a few comparisons that might help you find your path to the best solution for your application.

Capacitive sensors

Capacitive sensors are designed to detect nonferrous materials, but really anything that can break the capacitive field the sensor creates, including water, can do this. This technology allows for adjustment to the threshold of what it takes to break this field. These sensors are a great solution for through tank level detection and direct-contact sensing.

Ultrasonic sensors

Want to view your level from above? Ultrasonic sensors give you that view. They use sound to bounce off the media and return to the sensor, calculating the time it takes to measure distance. Their strong point is that they can overcome foam and can bounce off the water where light struggles when there is a large distance from the target to the receiver. Using the liquid from above, ultrasonics can monitor large tanks without contact.

Photoelectric sensors

Use photoelectric sensors when you’re looking at a solution for small scale. Now, this might require a site tube if you are monitoring the level on a large tank, however, if you want to detect small amounts of water or even bubbles within that water, photoelectric sensors are ideal. Using optical head remote photoelectric sensors tied to an amplifier, the detail and speed are unmatched. Photoelectric sensors are also great at detecting liquid levels on transparent bottles. In these applications with short distances, you need speed. Photoelectric sensors are as fast as light.

So, have you made up your mind yet? No matter which technology you choose, you will have a sensor that gives you accurate detail and digital outputs and is easy on the budget. Capacitive, ultrasonic, and photoelectric sensors provide all this and they grow with your application with adjustability.

Liquids are everywhere and not going away in manufacturing. They will continue to be an important resource for manufacturing. Cherish them and ensure you account for every drop.

Simplified Sensing Over a Complex Headache

The constant need for more data and higher accuracy has pushed sensing technologies to the extreme. Advancements in factory automation have created a perfect storm of innovation and new capabilities. This is probably an unpopular opinion but, do we always need all of this?

I started my career in factory automation in the late 90s. This was a time of technology transitions. PLCs had been around for ages but had never been so affordable. Technologies, such as time-of-flight laser measurement, industrial cameras, and inductive coupling, were new and exciting, and they were becoming more affordable, too.

As a controls engineer, I remember using these advanced technologies and systems as a way of keeping my projects future-proof – or so I thought. In reality, sometimes they just made things more complicated.

Let me explain this using an example where tried, true and affordable sensors could have made the project more reliable and future-proof from the start.

Photoelectric sensors have earned their place in the automation hall of fame. I don’t see a time when their use will not be necessary as a reliable way to conduct presence detection.

I was working on a project that required tracking several washing machine cabinet bases to be counted and orientated correctly on a conveyor. I wanted to use an industrial camera because the technology was getting better and better. I paid $7,000 for the camera and accessories. After several days and iterations, the camera system was working perfectly. It continued working for about a week before it was knocked out of alignment by a production worker who was using it as a leaning post. It took another day or so to dial back in.

Tried, true and affordable win out

The solution I ultimately chose was the easiest. I strategically placed seven basic photoeyes underneath the conveyor to identify what base it was looking at and if certain characteristics were present for quality tracking. My investment was around $400, and it was extremely protected from failure. And, if a sensor went out, rather than calling an engineer in the middle of the night, a maintenance electrician could simply replace it with a new one.

Another huge benefit of using photoeyes was the avoidance of buyer’s remorse. Camera technology is always evolving. From one day to the next they get better and more capable, but also might have proprietary comms or software. Basic photoelectric sensors with a PNP or NPN output can easily be swapped out by almost any brand for decade to come.

Keep it simple

At the end of the day, sometimes it is best to keep the solution simple, clean, and backed by the tried-and-true technologies in factory automation. Next time you dig into a project, take a moment to think about my example. Melt the solution down to the lowest common denominator and build up the complexity from there. You might just save more than just money; you might save a headache or two.

Looking Into & Through Transparent Material With Photoelectric Sensors

Advance automated manufacturing relies on sensor equipment to ensure each step of the process is done correctly, reliably, and effectively. For many standard applications, inductive, capacitive, or basic photoelectric sensors can do a fine job of monitoring and maintaining the automated manufacturing process. However, when transparent materials are the target, you need a different type of sensor, and maybe even need to think differently about how you will use it.

What are transparent materials?

When I think of transparent materials, water, glass, plexiglass, polymers, soaps, cooling agents, and packaging all come to mind. Because transparent material absorbs very little of the emitted red LED light, standard photoelectric sensors struggle on this type of application. If light can make its way back to the receiver, how can you tell if the beam was broken or not? By measuring the amount of light returned, instead of just if it is there or not, we can detect a transparent material and learn how transparent it is.

Imagine being able to determine proper mixes or thicknesses of liquid based on a transparency scale associated to a value of returned light. Another application that I believe a transparent material photoelectric senor would be ideal for is the thickness of a clear bottle. Imagine the wall thickness being crucial to the integrity of the bottle. Again, we would measure the amount of light allowed back to the receiver instead of an expensive measurement laser or even worse, a time-draining manual caliper.

Transparent material sensor vs. standard photoelectric sensor

So how does a transparent material sensor differ from a standard photoelectric sensor? Usually, the type of light is key. UV light is absorbed much greater than other wavelengths, like red or blue LEDs you find in standard photoelectric sensors. To add another level, you polarize that UV light to better control the light back into the receiver. Polarized UV light with a polarized reflector is the best combination. This can be done on a large or micro scale based on the sensor head size and build.

Uses for transparent material sensor include packaging trays, level tubes, medical tests, adhesive extrusion, and bottle fill levels, just to name a few. Transparent materials are everywhere, and the technology has matured. Make sure you are looking into specialized sensor technologies and working through best set-up practices to ensure reliable detection of transparent materials.

Add Depth to Your Processes With 3D Machine Vision

What comes to mind first when you think of 3D? Cheap red and blue glasses? Paying extra at a movie theater? Or maybe the awkward top screen on a Nintendo 3DS? Neither industrial machine vision nor robot guidance likely come to mind, but they should.

Advancements in 3D machine vision have taken the old method of 2D image processing and added literal depth. You become emerged into the application with true definition of the target—far from what you get looking at a flat image.

See For Yourself

Let’s do an exercise: Close one eye and try to pick up an object on your desk by pinching it. Did you miss it on the first try? Did things look foreign or off? This is because your depth perception is skewed with only one vision source. It takes both eyes to paint an accurate picture of your surroundings.

Now, imagine what you can do with two cameras side by side looking at an application. This is 3D machine vision; this is human.

How 3D Saves the Day

Robot guidance. The goal of robotics is to emulate human movements while allowing them to work more safely and reliably. So, why not give them the same vision we possess? When a robot is sent in to do a job it needs to know the x, y and z coordinates of its target to best control its approach and handle the item(s). 3D does this.

Part sorting. If you are anything like me, you have your favorite parts of Chex mix. Whether it’s the pretzels or the Chex pieces themselves, picking one out of the bowl takes coordination. Finding the right shape and the ideal place to grab it takes depth perception. You wouldn’t use a robot to sort your snacks, of course, but if you need to select specific parts in a bin of various shapes and sizes, 3D vision can give you the detail you need to select the right part every time.

Palletization and/or depalletization. Like in a game of Jenga, the careful and accurate stacking and removing of parts is paramount. Whether it’s for speed, quality or damage control, palletization/ depalletization of material needs 3D vision to position material accurately and efficiently.

I hope these 3D examples inspire you to seek more from your machine vision solution and look to the technology of the day to automate your processes. A picture is worth a thousand words, just imagine what a 3D image can tell you.

Document Product Quality and Eliminate Disputes with Machine Vision

“I caught a record-breaking walleye last weekend,” an excited Joe announced to his colleagues after returning from his annual fishing excursion to Canada.

“Record-breaking? Really? Prove it.” demanded his doubtful co-worker.

Well, I left my cell phone in the cabin so it wouldn’t get wet on the boat so I couldn’t take a picture, but I swear that big guy was the main course for dinner.”

“Okay, sure it was Joe.”

We have all been there — spotted a mountain lion, witnessed an amazing random human interaction, or maybe caught a glimpse at a shooting star. These are great stories, but they are so much more believable and memorable with a picture or video to back them up. Now a days, we all carry a camera within arm’s reach. Capturing life events has never been easier and more common, so why not use cameras to document and record important events and stages within your manufacturing process?

As the smart phone becomes more advanced and common, so does the technology and hardware for industrial cameras (i.e. machine vision). Machine vision can do so much more than pass fail and measurement type applications. Taking, storing, and relaying pictures along different stages of a production process could not only set you apart from the competition but also save you costly quality disputes after it leaves your facility. A picture can tell a thousand words, so what do you want to tell the world? Here are just a couple examples how you can back up you brand with machine vision:

Package integrity: We have all seen the reduced rack at a grocery store where a can is dented or missing a label. If this was caused by a large-scale label application defect, someone is losing business. So, before everyone starts pointing fingers, the manufacturer could simply provide a saved image from their end-of line-vision system to prove the cans were labeled when shipped from their facility.

Assembly defects: When you are producing assembled parts for a larger manufacturer, the standards they set are what you live and die by. If there is ever a dispute, having several saved images from either individual parts or an audit of them throughout the day could prove your final product met their specifications and could save your contract.

Barcode legibility and placement: Show your retail partners that your product’s bar code will not frustrate the cashier by having to overcome a poorly printed or placed barcode. Share images with them to show an industrial camera easily reading the code along the packaging line ensuring a hassle-free checkout as well as a barcode grade to ensure their barcode requirements are being met.

In closing, pictures always help tell a story and make it more credible. Ideally your customers will take your word for it, but when you catch the record-breaking walleye, you want to prove it.

Machine Vision: A Twenty-first Century Automation Solution

Lasers, scanners, fingerprint readers, and face recognition is not just science fiction anymore. I love seeing technology only previously imagined become reality through necessity and advances in technology. We, as a world economy, need to be able to verify who we are and ensure transitions are safe, and material and goods are tracked accurately. With this need came the evolution of laser barcode readers, fingerprint identification devices, and face ID on your phone. Similar needs have pushed archaic devices to be replaced within factory automation for data collection.

When I began my career in control engineering the 1990s high tech tools were limited to PLCs, frequency drives, and HMIs. The quality inspection data these devices relied on was collected mostly through limit switches and proximity sensors. Machine vision was still in it’s expensive and “cute” stage. With the need for more information, seriously accurate measurement, machining specs, and speed; machine vision has evolved, just like our personal technology has, to fill the needs of the modern time.

Machine vision has worked its way into the automation world as a need to have rather than a nice to have. With the ability to stack several tools and validations on top of each other, within a fraction of a second scan we now have the data our era needs to stay competitive. Imagine an application requiring you to detect several material traits, measure the part, read a barcode for tracking, and validate a properly printed logo screened onto the finished product. Sure, you could use several individual laser sensors, barcode readers and possibly even a vision sensor all working in concert to achieve your goal. Or you could use a machine vision system to do all the above easily with room to grow.

I say all of this because there is still resistance in the market to move to machine vision due to historical high costs and complexity. Machine Vision is here to stay and ready for your applications today. Think of it this way. How capable would you think a business is they took out a carbon copy credit card machine to run a payment for you? Well, think of this before you start trying to solve applications with several sensors. Take advantage of the technology at your fingertips; don’t hold on to nostalgia.

Beyond the Human Eye

Have you ever had to squint, strain, adjust your glasses, or just ask for someone with better vision to help read something for you? Now imagine having to adjust your eyesight 10 times a second. This is the power of machine vision. It can adjust, illuminate, filter, focus, read, and relay information that our eyes struggle with. Although the technology is 30 years old, machine vision is still in its early stages of adoption within the industrial space. In the past, machine vision was ‘nice to have’ but not really a ‘need to have’ technology because of costs, and the technology still not being refined. As traceability, human error proofing, and advanced applications grow more common, machine vision has found its rhythm within factory automation. It has evolved into a robust technology eager to solve advanced applications.

Take, for example, the accurate reading, validation, and logging of a date located on the concaved bottom of an aluminum can. Sometimes, nearly impossible to see with the human eye without some straining involved, it is completely necessary to ensure it is there to be able to sell the product. What would be your solution to ensuring the date stamp is there? Having the employee with the best eyes validate each can off the line? Using more ink and taking longer to print a larger code? Maybe adding a step by putting a black on white contrasting sticker on the bottom that could fall off? All of these would work but at what cost? A better solution is using a device easily capable of reading several cans a second even on a shiny, poor angled surface and saving a ton of unnecessary time and steps.

Machine vison is not magic; it is science. By combining high end image sensors, advanced algorithms, and trained vision specialists, an application like our aluminum can example can be solved in minutes and run forever, all while saving you time and money. In Figure 1 you can see the can’s code is lightly printed and overcome by any lighting due to hotspots from the angle of the can. In Figure 2 we have filtered out some of the glare, better defined the date through software, and validate the date is printed and correct.

Take a moment to imagine all the possibilities machine vision can open for your production process and the pain points it can alleviate. The technology is ready, are you?