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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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Focusing on Machine Safety

Machine safety refers to the measures taken to ensure the safety of operators, workers, and other individuals who may come into contact with or work in the vicinity of machinery. Safety categories and performance levels are two important concepts to evaluate and design safety systems for machines. A risk assessment is a process to identify, evaluate, and prioritize potential hazards and risks associated with a particular activity, process, or system. The goal of a risk assessment is to identify potential hazards and risks and to take steps to prevent or mitigate those risks. The hierarchy of controls can determine the best way to mitigate or eliminate risk. We can use this hierarchy, including elimination, substitution, engineering, and administrative controls, and personal protective equipment (PPE), to properly mitigate risk. Our focus here is on engineering controls and how they relate to categories and performance levels.

Performance level

The performance level (PL) of machine safety components is a measure of the reliability and effectiveness of safety systems. Defined as EN ISO 13849-1 standard by the International Organization for Standardization (ISO), it is based on the probability of a safety system failing to perform its intended function. Performance levels are designated by the letters “a” through “e” with PLa being the lowest level of safety and PLe being the highest. Assessing the safety function of the machinery and evaluating the likelihood of a dangerous failure occurring determines the performance level.

Four levels of protection

The categories of machine safety components refer to the four levels of protection required to ensure the safe operation of machinery, as defined by the ISO. Figure 1 below shows how the measured risk determines the performance level and category of circuit performance.

    • Category 1: The occurrence of a fault can lead to loss of the safety function. Single channel safety circuit.
    • Category 2: The occurrence of a fault can lead to loss of the safety function between checks. Single channel safety circuit with monitoring.
    • Category 3: When a single fault occurs, the safety function is always performed. Some faults, but not all, can be detected, but the accumulation of those undetected faults can lead to the loss of the safety function. This category can be implemented using control reliable devices in a dual channel redundant safety circuit that includes monitoring.
    • Category 4: When a fault occurs, the safety function is always performed. Faults will be detected in time to prevent a loss of the safety function and is implemented using control reliable devices in a dual channel redundant safety circuit that includes monitoring.

Using control reliable devices is crucial in Category 3 and 4 safety circuits. One example of a control reliable device is a safety relay that mechanically interlocks the control contacts to the auxiliary contacts. Being mechanically interlocked means when the relay changes states the auxiliary contact will also changes states. Another example of a control reliable device is a safety PLC. A standard PLC is not rated to control safety functions because it is not control reliable and a malfunction could lead to the loss of a safety function.

 

The selection of the appropriate category and performance level for devices used to mitigate a risk in a machine is crucial for ensuring the safety of operators and other individuals. While it is important to note that the purpose of this blog is to provide information, it is not enough to qualify individuals to design or test safety systems. In summary, the category of machine safety defines the level of protection required for safe operation, while the performance level measures the reliability and effectiveness of safety systems.

Now let us go automate with a focus on safety!

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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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Miniature Sensors With Monumental Capabilities

Application requirements solved by miniature optical sensors.Application requirements solved by miniature optical sensors.The requirement for miniature optical sensors to meet the demands of medical and semiconductor automation equipment often exceeds the capabilities of standard self-contained optical sensors. In some cases, other industry application requirements can be best solved by these same miniature optical sensors with advanced capabilities. So, what do these optical sensors offer that makes them so much better?

Application requirements solved by miniature optical sensors.Applications

Let’s begin with some of the applications that require these capabilities: medical applications, such as lab-on-a-chip microfluidics, liquid presence or level in drip chambers or pipettes, turbidity, drop detection, and micro or macro bubble detection, to name a few. Semicon applications include wafer presence on end-effectors, wafer mapping, wafer centering, and wafer presence in transfer chambers. Other applications that benefit from these sensors include packaging pharmaceuticals, detecting extremely small parts, and spray detection. In addition, these sensors are frequently used in customer-specific designs because they can be customized for specific applications.

Application requirements solved by miniature optical sensors.These sensors require an amplifier which sometimes is not popular with design engineers. They are associated with additional cost and extra work during installation; however, the remote amplifier offers real advantages. The optical function is separate from the control unit which allows it to be incorporated into an extremely tiny sensor head. Since the LEDs are mounted in the sensor heads, we now have a small wired connection back to the amplifier. Unlike fiber optics, this wired connection to the emitting LED and receiver allows for very minimal or no bending radius because of the cable in use.

Features

The new generation of amplifiers offers tremendous flexibility with advanced features, including:

    • OLED displayoptical sensors.
    • Intuitive menu structure
    • LEDs for status, communication, and warnings
    • Teaching/Parametrization
    • Single-point, two-point, window, dynamic, and tracking operating modes
    • Multiple teach modes: direct, dynamic, external, automatic and I/O-Link
    • Selectable power modes
    • Selectable outputsminiature optical sensors
    • Selectable speed settings
    • Auto-sync up to 8 amplifiers
    • Configurable delays and hysteresis
    • Compatible with existing all sensor heads

The sensor heads or optical heads come in a wide variety of housings, including the ability to customize them to meet specific requirements. And they are available in small precision LEDs, photodiodes, phototransistors, and complete laser modules according to a patented manufacturing process. Due to the high optical quality, additional lenses or apertures are no longer necessary.

Application requirements solved by miniature optical sensors.A multitude of special characteristics completely differentiates these sensors from the products made by standard optical sensor manufacturers. The range of products includes extraordinary miniature optical sensors as standard products, optimally adapted customized solutions, and precision optoelectronic components, such as LEDs, photodiodes, and laser modules. High optical quality, and unique modular designs, in connection with the greatest possible manufacturing flexibility, guarantee solutions that are exactly adapted to the respective problems and needs of the users.

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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.