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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.

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Using Vision Sensors to Conquer 1D and 2D Barcode Reading Applications

As many industries trend towards the adoption and use of two-dimensional barcodes and readers, the growth in popularity, acceptance of use, and positive track record of these 2D code readers offer a better way to track data. Vision-sensing code readers have many benefits, such as higher read rate performance, multi-directional code detection, simultaneous multiple codes reads, and more information storage.

While traditional red line laser scanners or cameras with decoding and positioning software are commonly used to read barcodes, there are three main types of barcodes: 1D, 2D, and QR codes. Each type has different attributes and ways of reading.

1D barcodes are the traditional ladder line barcodes typically seen in grocery stores and on merchandise and packaging. On the other hand, 2D Data Matrix codes are smaller than 1D barcodes but can hold quite a bit more information with built-in redundancy in case of scratches or defacement. QR codes, which were initially developed for the automotive industry, can hold even more information than Data Matrix codes, were initially developed for the automotive industry to track parts during vehicle manufacturing and are now widely used in business and advertising.

There are various types of vision sensors for reading different types of barcodes. QR codes are often used in business and advertising, while micro QR codes are typically seen in industrial applications such as camshafts, crankshafts, pistons, and circuit boards. Deciphering micro QR codes typically require an industrial sensor.

The need to easily track products and collect information about their whereabouts has been a long-standing problem in manufacturing and industrial automation. While one-dimensional barcodes have been the traditional solution, advances in one-dimensional code reading continue to improve. New hardware, code readers, and symbology, however, have made an emergence, and new image-based scanners are becoming a popular alternative for data capture solutions.

In summary, vision sensors are becoming increasingly important in 1D and 2D barcode reading applications due to their higher read rate performance, multi-directional code detection, simultaneous multiple codes read, and more information storage. As the need for tracking products and collecting information about their whereabouts continues to grow, industries will benefit from the use of vision sensors to improve efficiency and accuracy.

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Future Proofing Weld Cell Operations

Weld cells are known for their harsh environments, with high temperatures, electromagnetic field disruptions, and weld spatter debris all contributing to the reduced lifespan of standard sensors. However, there are ways to address this issue and minimize downtime, headaches, and costs associated with sensor replacement.

Sensor selection

Choosing the appropriate sensor for the environment may be the answer to ensuring optimal uptime for a weld cell environment. If current practices are consistently failing, here are some things to consider:

    • Is there excessive weld spatter on the sensor?
    • Is the sensor physically damaged?
    • Is there a better mounting solution for the sensor?

For example, sensors or mounts with coatings can help protect against weld spatter accumulation while specialized sensors can withstand environmental conditions, such as high temperatures and electromagnet interferences. To protect from physical damage, a steel-faced sensor may be an ideal solution for increased durability. Identifying the root cause of the current problem is critical in this process, and informed decisions can be made to improve the process for the future.

Sensor protection

In addition to selecting the correct sensor, further steps can be taken to maximize the potential of the weld cell. The sections below cover some common solutions for increasing sensor lifetime, including sensor mounts and bunkers, and entirely removing the sensor from the environment.

Mounting and bunkering

Sensor mounting enables the positioning of the sensor, allowing for alignment correction and the possibility of moving the sensor to a safer position. Some examples of standard mounting options are shown in image 1. Bunkering is generally the better option for a welding environment, with material thickness and robust metal construction protecting the sensor from physical damage as displayed on the right in image 2. The standard mounts on the left are made of either plastic or aluminum. Selecting a mounting or bunkering solution with weld spatter-resistant coating can further protect the sensor and mounting hardware from weld spatter buildup and fully maximize the system’s lifetime.

Image 1
Image 2

Plunger probes

Using a plunger probe, which actuates along a spring, involves entirely removing the sensor from the environment. As a part comes into contact with the probe and pushes it into the spring, an embedded inductive sensor reads when the probe enters its field of vision, allowing for part validation while fully eliminating sensor hazards. This is a great solution in cases where temperatures are too hot for even a coated sensor or the coated sensor is failing due to long-term, high-temperature exposure. This mechanical solution also allows for physical contact but eliminates the physical damage that would occur to a normal sensor over time.

The solutions mentioned above are suggestions to keep in mind when accessing the current weld cell. It is important to identify any noticeable, repeatable failures and take measures to prevent them. Implementing these measures will minimize downtime and extend the lifetime of the sensor.

Leave a comment for a follow-up post if you’d like to learn about networking and connectivity in weld cells.

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Magnetic Field Positioning Systems for Reliable, Accurate and Repeatable Absolute Position Feedback

Magnetic field positioning systems are increasingly popular due to their ability to provide reliable, accurate, and repeatable absolute position feedback.

These systems use magnetic field sensors to get a larger range of feedback across a pneumatic cylinder – a great alternative to traditional cylinder prox switches that may not work well in certain applications. They also allow for continuous monitoring of piston position in tight spaces, providing feedback in the form of analog voltage, current output, and IO-Link interface. And in many cases, these systems can replace the need for a linear transducer, making them a cost-effective solution for many industries.

One of the key benefits of magnetic field positioning systems is their versatility. They can be used in a wide range of industrial applications, such as:

    • Ultrasonic welding to validate set height with position feedback
    • Nut welding to verify set height with position feedback
    • Dispensing
    • Gripping for position feedback for different parts
    • Liner position indicators

While using these sensors greatly improves productivity in areas where prox sensors cannot provide the reliability needed, when selecting the magnetic field position system, it is important to consider the application requirements. The accuracy and feedback speed, for example, may vary depending on the application.

Magnetic field position systems are also available in different lengths. If the standard length does not meet requirements, you can choose a non-contact type that can be mounted on a slide with a magnetic trigger.

Overall, magnetic field positioning systems are an excellent choice for any industry that requires reliable, accurate, and repeatable absolution position feedback. With their versatility and flexibility, they are sure to improve productivity and efficiency in a wide range of applications.

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Sensing Ferrous and Non-Ferrous Metals: Enhancing Material Differentiation

Detecting metallic (ferrous) objects is a common application in many industries, including manufacturing, automotive, and aerospace. Inductive sensors are a popular choice for detecting metallic objects because they are reliable, durable, and cost-effective. Detecting a metallic object, however, is not always as simple as it seems, especially if you need to differentiate between two metallic objects. In such cases, it is crucial to understand the properties of the metals you are trying to detect, including whether they are ferrous or non-ferrous.

Ferrous vs. non-ferrous

Ferrous metals, such as mild steel, carbon steel, stainless steel, cast iron, and wrought iron, contain iron. They are typically magnetic, heavier, and more likely to corrode than non-ferrous metals, which do not contain iron. Aluminum, copper, lead, zinc, nickel, titanium, and cobalt are examples of non-ferrous metals. They are typically nonmagnetic, lightweight, and less likely to corrode.

Sensing ferrous and non-ferrous metal:

When it comes to detecting ferrous and non-ferrous metals using inductive sensors, the reduction factor plays a crucial role. The reduction factor is the ratio of the sensor’s effective sensing distance for a given metal to the sensor’s effective sensing distance for steel. In other words, it is the degree to which a metal affects the sensing range of an inductive sensor. Ferrous metals typically have less of an effect on sensing range than non-ferrous metals because inductive sensors function based on the law of induction, and magnetic metals are more likely to interact with the magnetic field created by the sensor.

The reduction factor for each type of metal varies depending on the metal’s properties. Ferrous metals typically have a higher reduction factor than non-ferrous metals, which means they can be detected from a greater distance. For example, both steel and stainless steel have a reduction factor of 0.6 to 1, which means they can be detected from the full switching distance of the sensor of 4 mm. In contrast, non-ferrous metals, such as aluminum, copper, and brass, have a lower reduction factor of 0.25 to 0.5, which means they can only be detected from a fraction of the operating switching distance, typically 1 to 2 mm.

Understanding the reduction factor for each metal allows you to answer the question of what happens when you need to differentiate between two metallic parts. If one metal is ferrous and the other is non-ferrous, then you can place the sensor at a distance that will detect the ferrous metal but not the non-ferrous metal. However, this may not be an efficient solution if the metals have similar reduction factors, or if you need to detect the non-ferrous metal over the ferrous metal.

Using ferrous-only or non-ferrous-only sensors

The better solution is to use a ferrous-only or non-ferrous-only sensor. These sensors are specifically designed to detect only one type of metal and ignore the other type, resulting in a reduction factor of zero. Ferrous-only sensors detect only ferrous metals, and their reduction factors range from 0.1 to 1 for steel and stainless steel, while the reduction factors for non-ferrous metals such as aluminum, copper, and brass are zero. Non-ferrous-only sensors detect only non-ferrous metals, and their reduction factors range from 0.9 to 1.1 for aluminum, copper, and brass, while the reduction factors for ferrous metals are zero. Using ferrous-only or non-ferrous-only sensors eliminates the need to adjust the mounting distance of a standard inductive sensor to detect a ferrous metal over a non-ferrous metal.

Overall, selecting the right sensor for your application depends on the type of metals you need to differentiate and detect. If you are dealing with ferrous and non-ferrous metals, you can use a standard inductive sensor, but you need to be aware of the reduction factor for each metal type and adjust the mounting distance accordingly. If you need to detect only one type of metal, however, a ferrous-only or a non-ferrous-only sensor is the better option. These sensors are specially designed to ignore the other metal type, eliminating the need to adjust the mounting distance.

By understanding the differences between ferrous and non-ferrous metals and the capabilities of different sensors, you can optimize the metal detection system for maximum efficiency and accuracy.

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Considerations When Picking UHF RFID

If you’ve attempted to implement an ultra-high frequency (UHF) RFID system into your facility, you might have run into some headaches in the process of getting things to work properly. If you are looking to implement UHF RFID, but haven’t had the chance to set things up yet, then this blog might be beneficial to keep in mind during the process.

UHF RFID and what it can do

UHF RFID is a long-range system with a focus on gaining visibility in the supply chain or manufacturing process. It can track multiple ID tags in a set area/distance (depending on the read/write head you select). The RFID field is emitted by an antenna that propagates an electromagnetic field, which will “ping and power up” a tag with data saved on it. Commonly, warehouses use it for logistics, supply chain tracking, warehouse pallet tracking, equipment tracking, or even for luggage tracking. As amazing as this technology sounds, there are environmental factors that can cause the system to not work to its full potential.

Factors affecting RFID system performance

Different materials or environments can affect the performance of your RFID system. Each tag antenna is set to a specific frequency, and some materials or environments can influence the radiation pattern. This can be something as simple as the material on which the tag is mounted to something more complex, such as how the signal is going to bounce off the walls or the ground. Below are some common issues people run into when implementing RFID.

    • Absorption: Absorption occurs when an object in the field absorbs part of the radio frequency energy emitted from the reader antenna. Cardboard, conductive liquids, and tissue (human bodies or animals) are examples of materials that can absorb some of the RF energy. One way to think of this is to imagine a sound booth in a recording studio. The booth is covered in foam to absorb sound. This is a similar philosophy for UHF RFID. You need to consider materials that absorb that energy.
    • Reflection: When there are distortions of the RF field, reflection can occur. As you may imagine, certain materials, such as metals, can cause the waves emitted from the antenna to distort or “reflect” in ways that cause performance losses. This could be metal machinery or fixings between the reader and the tags, a group of metal pipes, and mounting on metal containers. If you choose to do a deeper dive, there are other performance factors that can be impacted by the path of the signal, such as zones in which the tag can’t be reached (even if the tag is in the reader’s field), or the tag and the reader are not aligned properly.
    • Detuning: Detuning occurs when the radio frequency between the tag and reader is changed in the process. Since you pair specific readers to specific tags at a specific frequency, you don’t want your environment to cause a change in the specific frequencies. Certain materials, such as cardboard, metals, tissue, and plastics, can cause an impedance that can “un-match” your reader and tags based on the RF not matching up.

Luckily for you, many companies who specialize in RFID can help ensure you pick the right system for your application. Some will even go visit your site to evaluate the environment and materials that will be involved in the process and recommend the right readers, antennas, tags, and accessories for you.

Although not all UHF RFID applications seem complex, there are many small things that can affect the entire operation. When you are picking your system, make sure you keep in mind some of these effects, and if you are unsure, call in a professional for some assistance.

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Tackling the Most Demanding Applications With Precision Sensors

Standard industrial sensors can solve a lot of automation challenges. Photoelectric, capacitive, and inductive technologies detect presence, distances, shapes, colors, thicknesses, and more. To satisfy these general applications, there are a few reputable manufacturers in the market that design and produce such products. In many instances, it is possible to interchange them from manufacturer to manufacturer, due to similar mounting patterns, technical specifications, connectors, and even common pin assignments.

But some applications require more precision – where standard sensors will not do.  Some examples include:

    • The target may be too small or difficult material to detect
    • The target may move very slowly, or very quickly
    • The target may have a minimal displacement, as in valve feedback
    • The sensor must have low mass, for high-acceleration applications
    • The sensor location has severe space constraints or material constraints

Applications that must detect particles that can’t be seen with the naked eye, or something as small as sensing the thin edge of a silicon wafer or the edge of a clear glass microscope slide, require sensors with exceptional precision.

Many precision sensing applications require a custom-designed sensor to meet the customer’s expectations. These expectations typically involve a quality sensor with robust attributes, likely coupled with difficult design parameters, such as high switch-point repeatability, exceptional temperature stability, or exotic materials.

What constitutes a precision sensing application? Let’s take a look.

Approximately 70% of all medical decisions are based on lab results. Our doctors are making decisions about our health based on these test outcomes. Therefore, accurate, trustworthy results, performed quickly, are priorities. Many tests rely on pipetting, the aspirating and dispensing of fluids – sometimes at a microscale level – from one place to another. Using a manual pipette is a time-consuming, labor-intensive process. Automating this procedure reduces contamination and eliminates human errors.

To satisfy the requirements of an application such as this requires a custom-manufactured LED light source, with a wavelength chosen to best interact with the fluids, and an extremely small, concentrated light emission that approaches laser-like properties (yet without the expense and power requirements of the laser). This light source verifies pipette presence and dispensing levels, with a quality check of the fluids dispensed down to the nanoliter scale.

So, the next time you face an application challenge that cannot be tackled with a standard sensor, consider a higher precision sensor and rest assured you will get the reliability you demand.

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Why Choose an IO-Link Ecosystem for Your Next Automation Project?

By now we’ve all heard of IO-Link, the device-level communication protocol that seems magical. Often referred to as the “USB of industrial automation,” IO-Link is a universal, open, and bi-directional communication technology that enables plug-and-play device replacement, dynamic device configuration, centralized device management, remote parameter setting, device level diagnostics, and uses existing sensor cabling as part of the IEC standard accepted worldwide.

But what makes IO-Link magical?

If the list above doesn’t convince you to consider using IO-Link on your next automation project, let me tell you more about the things that matter beyond its function as a communications protocol.

Even though these benefits are very nice, none of them mean anything if the devices connected to the network don’t provide meaningful, relevant, and accurate data for your application.

Evolution of the IO-Link

IO-Link devices, also known as “smart devices,” have evolved significantly over the years. At first, they were very simple and basic, providing data such as the status of your inputs and outputs and maybe giving you the ability to configure a few basic parameters, such as port assignment as an input or an output digitally over IO-Link. Next, came the addition of functions that would improve the diagnostics and troubleshooting of the device. Multi-functionality followed, where you have one device under one part number, and can configure it in multiple modes of operation.

Nothing, however, affected the development of smart devices as much as the introduction of IIoT (Industrial Internet of Things) and the demand for more real-time information about the status of your machine, production line, and production plant starting at a device level. This demand drove the development of smart devices with added features and benefits that are outside of their primary functions.

Condition monitoring

IO-Link supplies both sensor/actuator details and secure information
IO-Link supplies both sensor/actuator details and secure information

One of the most valuable added features, for example, is condition monitoring. Information such as vibration, humidity, pressure, voltage and current load, and inclination – in addition to device primary function data – is invaluable to determine the health of your machine, thus the health of your production line or plant.

IO-Link offers the flexibility to create a controls architecture independent of PLC manufacturer or higher-level communications protocols. It enables you to:

    • use existing low-cost sensor cabling
    • enhance your existing controls architecture by adding devices such as RFID readers, barcode and identification vision sensors, linear and pressure transducers, process sensors, discrete or analog I/O, HMI devices, pneumatic and electro-mechanical actuators, condition monitoring, etc.
    • dynamically change the device configuration, auto-configure devices upon startup, and plug-and-play replacement of devices
    • enable IIOT, predictive maintenance, machine learning, and artificial intelligence

There is no other device-level communications protocol that provides as many features and benefits and is cost-effective and robust enough for industrial automation applications as IO-Link.

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Demystifying Machine Learning

Machine learning can help organizations improve manufacturing operations and increase efficiency, productivity, and safety by analyzing data from connected machines and sensors, machine. For example, its algorithms can predict when equipment will likely fail, so manufacturers can schedule maintenance before problems occur, thereby reducing downtime and repair costs.

How machine learning works

Machine learning teaches computers to learn from data – to do things without being specifically told how to do them. It is a type of artificial intelligence that enables computers to automatically learn or improve their performances by learning from their experiences.

machine learning stepsImagine you have a bunch of toy cars and want to teach a computer to sort them into two groups: red and blue cars. You could show the computer many pictures of red and blue cars and say, “this is a red car” or “this is a blue car” for each one.

After seeing enough examples, the computer can start to guess which group a car belongs in, even if it’s a car that it hasn’t seen before. The machine is “learning” from the examples you show to make better and better guesses over time. That’s machine learning!

Steps to translate it to industrial use case

As in the toy car example, we must have pictures of each specimen and describe them to the computer. The image, in this case, is made up of data points and the description is a label. The sensors collecting data can be fed to the machine learning algorithm in different stages of the machine operation – like when it is running optimally, needs inspection, or needs maintenance, etc.

Data taken from vibration, temperature or pressure measures, etc., can be read from different sensors, depending on the type of machine or process to monitor.

In essence, the algorithm finds a pattern for each stage of the machine’s operation. It can notify the operator about what must be done given enough data points when it starts to veer toward a different stage.

What infrastructure is needed? Can my PLC do it?

The infrastructure needed can vary depending on the algorithm’s complexity and the data volume. Small and simple tasks like anomaly detection can be used on edge devices but not on traditional automation controllers like PLCs. Complex algorithms and significant volumes of data require more extensive infrastructure to do it in a reasonable time. The factor is the processing power, and as close to real-time we can detect the machine’s state, the better the usability.

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Embedded vision – What It Is and How It Works

Embedded vision is a rapidly growing field that combines computer vision and embedded systems with cameras or other imaging sensors, enabling devices to interpret and understand the visual world around them – as humans do. This technology, with broad applications, is expected to revolutionize how we interact with technology and the world around us and will likely play a major role in the Internet of Things and Industry 4.0 revolution.

Embedded vision uses computer vision algorithms and techniques to process visual information on devices with limited computational resources, such as embedded systems or mobile devices. These systems use cameras or other imaging sensors to acquire visual data and perform tasks on that data, such as image or video processing, object detection, and image analysis.

Applications for embedded vision systems

Among the many applications that use embedded vision systems are:

    • Industrial automation and inspection
    • Medical and biomedical imaging
    • Surveillance and security systems
    • Robotics and drones
    • Automotive and transportation systems

Hardware and software for embedded vision systems

Embedded vision systems typically use a combination of software and hardware to perform their tasks. On the hardware side, embedded vision systems often use special-purpose processors, such as digital signal processors (DSPs) or field-programmable gate arrays (FPGAs), to perform the heavy lifting of image and video processing. On the software side, they typically use libraries or frameworks that provide pre-built functions for tasks, such as image filtering, object detection, and feature extraction. Some common software libraries and frameworks for embedded vision include OpenCV, MATLAB, Halcon, etc.

It’s also quite important to note that the field of embedded vision is active and fast moving with new architectures, chipsets, and software libraries appearing regularly to make this technology more available and accessible to a broader range of applications, devices, and users.

Embedded vision components

The main parts of embedded vision include:

    1. Processor platforms are typically specialized for handling the high computational demands of image and video processing. They may include digital signal processors (DSPs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs).
    2. Camera components refer to imaging sensors that acquire visual data. These sensors can include traditional digital cameras and specialized sensors such as stereo cameras, thermal cameras, etc.
    3. Accessories and carrier boards include the various additional hardware and components that interface the camera with the processor and other peripherals. Examples include memory cards, power supplies, and IO connectors.
    4. Housing and mechanics are the physical enclosures of the embedded vision system, including the mechanics that hold the camera, processor, and other components in place, and the housing that protects the system from external factors such as dust and water.
    5. The operating system runs on the processor. It could be a custom firmware or a general-purpose operating system, like Linux or Windows.
    6. Application SW is the software that runs on the embedded vision system to perform tasks such as image processing, object detection, and feature extraction. This software often uses a combination of high-level programming languages, such as C++, Python, and lower-level languages, like C.
    7. Feasibility studies evaluate a proposed solution’s technical and economic feasibility, identifying any risks or possible limitations that could arise during the development. They are conducted before the development of any embedded vision systems.
    8. Integration interfaces refer to the process of integrating the various components of the embedded vision system and interfacing it with other systems or devices. This can include integrating the camera, processor, and other hardware and developing software interfaces to enable communication between the embedded vision system and other systems.

Learn more here about selecting the most efficient and cost-effective vision product for your project or application.