RFID Replaces Bar Codes Efficient Asset Tracking

Bar code technology has been around for many years and is a tried and true means for tracking asset and product movement, but it has its limitations. For example, a bar code reader must have an unobstructed view of the bar code to effectively scan. And the bar code label cannot be damaged, or it is then unreadable by the scanner.

In more recent years, additional RFID technologies have been more readily available for use to accomplish the same task but with fewer limitations. Using RFD, a scanner may be able to read tags that are blocked by other things and not visible to the naked eye. UHF RFID can scan multiple tags at the same time in a single scan, whereas most bar codes need to be scanned individually. This, therefore, increases efficiency and reduces the time required to perform the scans.

Then, of course, there is the human factor. RFID can help eliminate mistakes caused by human error. Most bar code scanning is done with hand scanners held by workers since the scanner has to be in the exact position to see the bar code to get a good scan. While manual/hand-held scanning can be done using RFID, most times a fixed scanner can be used as long as the position of the RFID tag can be guaranteed within certain tolerances. These tolerances are much greater than with a bar code scanner.

With the advent of inexpensive consumable RFID labels, the ease and cost of transitioning to RFID technology has become more feasible for manufacturers and end users. These labels can be purchased for pennies each in rolls of several thousand at a time.

It should be noted that several companies now produce printers that can actually code the information on a RFID label tag while also printing data, including bar codes, on these label tags so you have the best of both worlds. Tags can be scanned automatically and data that can be read by the human eye as well as a bar code scanner.

Some companies have expressed concern about the usage of RFID in different countries due to local regulations regarding the frequencies of radio waves causing interferences.

This is not an issue for HF  and UHF technology. HF is an ISO standard (ISO 15693) technology so it applies to most everywhere. For UHF, which is more likely to be used due to the ability to scan at a distance and scan multiple tags at the same time, the only caveat is that different areas of the world allow scanners to only operate in certain frequencies. This is overcome by the fact that almost all UHF tags that I have encountered are what are called global tags.

This means these tags can be used in any of the global frequency ranges of UHF signals. For example, in the North America, the FCC restricts the frequency range for UHF RFID scanners to 902-928 MHz, whereas MIC in Japan restricts them to 952-954 MHz, ETSI EN 300-220 in Europe restricts them to 865-868 MHz, and DOT in India restricts them to 865-867 MHz. These global tags can be used in any of these ranges as they work from 860 to 960 MHz.

On the subject of UHF, it should be mentioned that in addition to the frequency ranges restricted by various part of the world, maximum antenna power is also locally restricted.

For more information on RFID for asset tracking, visit https://www.balluff.com/local/us/products/product-overview/rfid/

 

Ensure Food Safety with Machine Vision

Government agencies have put food manufacturers under a microscope to ensure they follow food safety standards and comply with regulations. When it comes to the health and safety of consumers, quality assurance is a top priority, but despite this, according to The World of Health Organization, approximately 600 million people become ill each year after eating contaminated food, and 420,000 die.

Using human manual inspection for quality assurance checks in this industry can be detrimental to the company and its consumers due to human error, fatigue and subjective opinions. Furthermore, foreign particles that should not be found in the product may be microscopic and invisible to the human eye. These defects can lead to illness, recalls, lawsuits, and a long-term negative perception of the brand itself. Packaging, food and beverage manufacturers must realize these potential risks and review the benefits of incorporating machine vision. Although machine vision implementation may sound like a costly investment, it is small price to pay when compared to the potential damage of uncaught issues. Below I explore a few benefits that machine vision offers in the packaging, food and beverage industries.

Safety
Consumers expect and rely on safe products from food manufacturers. Machine vision can see through packaging to determine the presence of foreign particles that should not be present, ensuring these products are removed from the production line. Machine vision is also capable of inspecting for cross-contamination, color correctness, ripeness, and even spoilage. For example, bruises on apples can be hard to spot for the untrained eye unless extremely pronounced. SWIR (shortwave infrared) illumination proves effective for the detection of defects and contamination. Subsurface bruising defects become much easier to detect due to the optimization of lighting and these defected products can be scrapped.

Uniformity of Containers
Brand recognition is huge for manufacturers in this industry. Products that have defects such as dents or uneven contents inside the container can greatly affect the public’s perception of the product and/or company. Machine vision can detect even the slightest deformity in the container and ensure they are removed from the line. It can also scan the inside of the container to ensure that the product is uniform for each batch. Vision systems have the ability optimize lighting intensity, uniformity, and geometry to obtain images with good contrast and signal to noise. Having the ability to alter lighting provides a much clearer image of the point of interest. This can allow you to see inside a container to determine if the fill level is correct for the specific product.

Packaging
Packaging is important because if the products shipped to the store are regularly defected, the store can choose to stop stocking that item, costing the manufacturer valuable business. The seal must last from production to arrival at the store to ensure that the product maintains its safe usability through its marked expiration date. In bottling applications, the conveyors are moving at high speeds so the inspection process must be able to quickly and correctly identify defects. A facility in Marseille, France was looking to inspect Heineken beer bottles as they passed through a bottling machine at a rate of 22 bottles/second (80,000 bottles/hour). Although this is on the faster end of the spectrum, many applications require high-speed quality checks that are impossible for a human operator. A machine vision system can be configured to handle these high-speed applications and taught to detect the specified defect.

Labels

It’s crucial for the labels to be printed correctly and placed on the correct product because of the food allergy threats that some consumers experience. Machine vision can also benefit this aspect of the production process as cameras can be taught to recognize the correct label and brand guidelines. Typically, these production lines move at speeds too fast for human inspection. An intuitive, easy to use, machine vision software package allows you to filter the labels, find the object using reference points and validate the text quickly and accurately.

These areas of the assembly process throughout packaging, food and beverage facilities should be considered for machine vision applications. Understanding what problems occur and the cost associated with them is helpful in justifying whether machine vision is right for you.

For more information on machine vision, visit https://www.balluff.com/local/us/products/product-overview/machine-vision-and-optical-identification/.

 

 

Turning Big Data into Actionable Data

While RFID technology has been available for almost seventy years, the last decade has seen widespread acceptance, specifically in automated manufacturing. Deployed for common applications like automatic data transfer in machining operations, quality control in production, logistics traceability and inventory control, RFID has played a major role in the evolution of data collection and handling. With this evolution has come massive amounts of data that can ultimately hold the key to process improvement, quality assurance and regulatory compliance. However, the challenge many organizations face today is how to turn all that data into actionable data.

Prominent industry buzzwords like Industry 4.0 and the Industrial Internet of Things (IIOT) once seemed like distant concepts conjured up by a marketing team far away from the actual plant floor, but those buzzwords are the result of manufacturing organizations around the globe identifying the need for better visibility into their operations. Automation hardware and the infrastructure that supports it has advanced rapidly due to this request, but software that turns raw data into actionable data is still very much in demand. This software needs to provide interactive feedback in the form of reporting, dashboards, and real time indicators.

The response to the demand will bring vendors from other industries and start-ups, while a handful of familiar players in automation will step up to the challenge. Competition keeps us all on our toes, but the key to filling the software gap in the plant is partnering with a vendor who understands the needs on the plant floor. So, how do you separate the pretenders from the contenders? I compiled a check list to help.

Does the prospective vendor have:

  • A firm understanding that down time and scrap need to be reduced or eliminated?
  • A core competency in automation for the plant floor?
  • Smart hardware devices like RFID and condition monitoring sensors?
  • A system solution that can collect, analyze, and transport data from the device to the cloud?
  • A user-friendly interface that allows interaction with mobile devices like tablets and phones?
  • The capability to provide customized reports to meet the needs of your organization?
  • A great industry reputation for quality and dependability?
  • A chain of support for pre-sales, installation, and post-sales support?
  • Examples of successful system deployments?
  • The willingness to develop or modify current devices to address your specific needs?

If you can check the box for all of these, it is a safe bet you are in good hands. Otherwise, you’re rolling the dice.

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.

Which RFID Technology is Best for Your Traceability Application?

There are a lot of articles on using RFID for traceability, but it’s hard to know where to begin. Examples of traceability include locating an important asset like a specific mold that is required to run a machine or verifying a specific bin of material required to run production. Spending time looking for these important assets leads to lost time and production delays. RFID can help but understanding the different RFID capabilities will narrow down the type of RFID that is required.

Not all RFID technology is the same. Each RFID technology operates differently and is categorized by the frequency band of the radio spectrum, such as low frequency, high frequency and ultra-high frequency. In low and high frequency RFID, the read range between RFID tag and reader antenna is measured in millimeters and inches. The read range on ultra-high frequency (UHF) RFID technology can range from one meter to 100 meters. Typically, inventory traceability is done using ultra-high frequency band of the radio frequency spectrum, due to the need to read the asset at a further distance so it does not interfere with the production flow. Also, there are cases where there needs to be a reading of multiple tags in an area at the same time to determine where an asset is located. UHF RFID technology allows for simultaneous reading of multiple RFID tags from a single antenna reader.

There are two types of UHF RFID, passive and active.  Passive UHF RFID means that the RFID tags themselves have no additional power source. The UHF reader antenna sends out an electromagnetic wave field, and the RFID tags within the electromagnetic field have an internal antenna that receives the energy which activates the integrated circuit inside the tag to reflect the signals back to start communicating. The read distance between the passive RFID tag and antenna reader is determined by several factors, such as the size of the electromagnetic wave field generated out of the reader antenna and the size of the receiver antenna on the RFID tag. Typical read ranges on passive UHF systems can be anywhere from one to 12 meters, where the larger the power and RFID tag, the longer the range.

Active UHF RFID systems do not require the tag to reflect signals back to communicate because the active RFID tag has its own transmitter and internal battery source. Because of this, with active UFH RFID you can get read ranges of up to 100 meters. There are active tags which wake up and communicate when they receive a radio signal from a reader antenna, while others are beacons which emit a signal at a pre-set interval. Beacon active tags can locate in real time the location of the asset that the RFID tag is attached to. However, a downfall to active RFID tags is the battery life on the tag. If the battery is dead, then the asset will no longer be visible.

Figure 1

Once the strengths and weaknesses of each type of UHF RFID system is known, it’s easier to work with the constraints of the system. For example, the application in Figure 1 shows a reader antenna for reading bins of material placed a few feet away so that its’ not in the way of production. A passive UHF RFID system will work in this case, due to the distance between the antenna and the RFID tag on the bin a few feet away. There is no need to worry about battery life on the passive RFID tag.

Figure 2

If the exact location of a production mold is required in a large facility, then using an active UHF RFID system is likely a better fit. Incorporating an active RFID tag that sends out a beacon at a fixed interval to a data center ensures the location of all assets are always known. With this setup, the exact location of the mold can be found at any time in the facility.

Examining the different types of RFID technology can help determine the correct one to use in a traceability application. This includes analyzing the pros and cons of each technology and seeing which one is the best fit for the application.

The Right Mix of Products for Recipe-Driven Machine Change Over

The filling of medical vials requires flexible automation equipment that can adapt to different vial sizes, colors and capping types. People are often deployed to make those equipment changes, which is also known as a recipe change. But by nature, people are inconsistent, and that inconsistency will cause errors and delay during change over.

Here’s a simple recipe to deliver consistency through operator-guided/verified recipe change. The following ingredients provide a solid recipe-driven change over:

Incoming Components: Barcode

Fixed mount and hand-held barcode scanners at the point-of-loading ensure correct parts are loaded.

Change Parts: RFID

Any machine part that must be replaced during a changeover can have a simple RFID tag installed. A read head reads the tag in ensure it’s the correct part.

Feed Systems: Position Measurement

Some feed systems require only millimeters of adjustment. Position sensor ensure the feed system is set to the correct recipe and is ready to run.

Conveyors Size Change: Rotary Position Indicator

Guide rails and larger sections are adjusted with the use of hand cranks. Digital position indicators show the intended position based on the recipes. The operators adjust to the desired position and then acknowledgment is sent to the control system.

Vial Detection: Array Sensor

Sensor arrays can capture more information, even with the vial variations. In addition to vial presence detection, the size of the vial and stopper/cap is verified as well. No physical changes are required. The recipe will dictate the sensor values required for the vial type.

Final Inspection: Vision

For label placement and defect detection, vision is the go-to product. The recipe will call up the label parameters to be verified.

Traceability: Vision

Often used in conjunction with final inspection, traceability requires capturing the barcode data from the final vials. There are often multiple 1D and 2D barcodes that must be read. A powerful vision system with a larger field of view is ideal for the changing recipes.

All of these ingredients are best when tied together with IO-Link. This ensures easy implantation with class-leading products. With all these ingredients, it has never been easier to implement operator-guided/verified size change.

How RFID Can Error-Proof Appliance Assembly

Today, appliance manufactures are using RFID more frequently for error proofing applications and quality control processes.

Whether the appliance assembly process is automatic or semiautomatic, error-proofing processes using RFID are as important as the overall assembly processes. Now, RFID systems can be used to tell a PLC how well things are moving, and if the products and parts are within spec. This information is provided as an integral part of each step in the manufacturing process.

RFID systems installed throughout the manufacturing process provide a way of tracking not only what has happened, but what has gone right. RFID records where something has gone wrong, and what needs to be done to correct the problem.

Appliance manufacturers often need to assemble different product versions on the same production line. The important features of each part must be identified, tracked and communicated to the control system. This is most effectively done with an RFID system that stores build data on a small RFID tag attached to a build pallet. Before assembly begins, the RFID tag is loaded with the information that will instruct all downstream processes the correct parts that need to be installed.

Each part that goes into the appliance also has a RFID tag attached to it. As the build pallet moves down the assembly conveyor to each station, the tag on the build pallet is read to determine what assembly and error proofing steps are required. Often, this is displayed on an HMI for the operator. If the assembly requires testing, the results of those tests can be loaded into the data carrier for subsequent archiving. The operator scans the tag on each part as it is being installed. That data is then written to the tag on the build pallet. For example, in the washing machine assembly process, the washing machine body sits on the build pallet, and as it moves from station to station, the operators install different components like electronic boards, wiring harnesses, and motors. As each one of these components is installed, its RFID tag is scanned to make sure it is the correct part. If they install the wrong part, the HMI will signal the error.

RFID technology can also be used to reduce errors in the rework process. RFID tags, located on either on the assembly or the pallet, store information on what has been done to the appliance and what needs to be done. When an unacceptable subassembly reaches the rework area, the RFID tag provides details for the operator on what needs to be corrected. At the same time, the tag can signal a controller to configure sensors and tools, such as torque wrenches, to perform the corrective operations.

These are just a few examples of how appliance manufactures are using RFID for error proofing.

For more information, visit https://www.balluff.com/local/us/products/product-overview/rfid/.

Machine Vision: 5 Simple Steps to Choose the Right Camera

The machine vision and industrial camera market is offering thousands of models with different resolutionssizes, speeds, colors, interfaces, prices, etc. So, how do you choose? Let’s go through 5 simple steps which will ensure easy selection of the right camera for your application. 

1.  Defined task: color or monochrome camera  

2.  Amount of information: minimum of pixels per object details 

3.  Sensor resolution: formula for calculating the image sensor 

4.  Shutter technology: moving or static object 

5.  Interfaces and camera selector: lets pick the right model 

STEP 1 – Defined task  

It is always necessary to start with the size of the scanned object (X, Y), or you can determine the smallest possible value (d) that you want to distinguish with the camera.

For easier explanation, you can choose the option of solving the measurement task. However, the basic functionality can be used for any other applications.

In the task, the distance (D) between the centers of both holes is determined with the measurement accuracy (d). Using these values, we then determine the parameter for selecting the right image sensor and camera.

Example:
Distance (D) between 2 points with measuring accuracy (d) of 0.05 mm. Object size X = 48 mm (monochrome sensor, because color is not relevant here)

Note: Monochrome or color?
Color sensors use a Bayer color filter, which allows only one basic color to reach each pixel. The missing colors are determined using interpolation of the neighboring pixels. Monochrome sensors are twice as light sensitive as color sensors and lead to a sharper image by acquiring more details within the same number of pixels. For this reason, monochrome sensors are recommended if no color information is needed.

STEP 2 – Amount of information

Each type of application needs a different size of information to solve. This is differentiated by the minimum number of pixels. Lets again use monochrome options.

Minimum of pixels per object details

  • Object detail measuring / detection       3
  • Barcode line width                                           2
  • Datamatrix code module width                4
  • OCR character height                                    16

Example:
The measuring needs 3 pixels for the necessary accuracy (object detail size d). As necessary accuracy (d) which is 0.05 mm in this example, is imaged on 3 pixels.

Note:
Each characteristic or application type presupposes a minimum number of pixels. It avoids the loss of information through sampling blurs.

STEP 3 – Sensor resolution

We already defined the object size as well as resolution accuracy. As a next step, we are going to define resolution of the camera. It is simple formula to calculate the image sensor.

S = (N x O) / d = (min. number of pixels per object detail x object size) / object detail size

Object size (O) can be describe horizontally as well as vertically. Some of sensors are square and this problem is eliminated 😊

Example:
S = (3 x 48 mm) / 0.05 mm = 2880 pixels

We looked at the available image sensors and the closest is a model with resolution 3092 x 2080 => 6.4Mpixels image sensor.

Note:
Pay attention to the format of the sensor.

For a correct calculation, it is necessary to check the resolution, not only in the horizontal but also in the vertical axis.

 

STEP 4 – Shutter technology

Global shutter versus rolling shutter.

These technologies are standard in machine vision and you are able to find hundreds of cameras with both.

Rolling shutter: exposes the motive line-by-line. This procedure results in a time delay for each acquired line. Thus, moving objects are displayed blurrily in the resulting motive through the generated “object time offset” (compare to the image).

Pros:

    • More light sensitive
    • Less expensive
    • Smaller pixel size provides higher resolution with the same image format.

Cons:

    • Image distortion occurs on moving objects

Global shutter: used to get distortion-free images by exposing all pixels at the same time.

Pros:

    • Great for fast processes
    • Sharp images with no blur on moving objects.

Cons:

    • More expensive
    • Larger image format

Note:
The newest rolling shutter sensors have a feature called global reset mode, which starts the exposure of all rows simultaneously and the reset of each row is released simultaneously, also. However, the readout of the lines is equal to the readout of the rolling shutter: line by line.

This means the bottom lines of the sensor will be exposed to light longer! For this reason, this mode will only make sense, if there is no extraneous light and the flash duration is shorter or equal to the exposure time.

STEP 5 – Interfaces and camera selector

Final step is here:

You must consider the possible speed (bandwidth) as well as cable length of camera technology.

USB2
Small, handy and cost-effective, USB 2.0 industrial cameras have become integral parts in the area of medicine and microscopy. You can get a wide range of different variants, including with or without housings, as board-level or single-board, or with or without digital I/Os.

USB3/GigE Vision
Without standards every manufacturer does their own thing and many advantages customers learned to love with the GigE Vision standard would be lost. Like GigE Vision, USB3 Vision also defines:

    • a transport layer, which controls the detection of a device (Device Detection)
    • the configuration (Register Access)
    • the data streaming (Streaming Data)
    • the handling of events (Event Handling)
    • established interface to GenICam. GenICam abstracts the access to the camera features for the user. The features are standardized (name and behavior) by the standard feature naming convention (SFNC). Additionally, it is possible to create specific features in addition to the SFNC to differentiate from other vendors (quality of implementation). In contrast to GigE Vision, this time the mechanics (e.g. lockable cable connectors) are part of the standard which leads to a more robust interface.

I believe that these five points will help you choose the most suitable camera. Are you still unclear? Do not hesitate to contact us or contact me directly: I will be happy to consult your project, needs or any questions.

 

 

Be Driven by Data and Decrease Downtime

Being “driven by data” is simply the act of making decisions based on real data instead of guessing or basing them on theoretical outcomes. Why one should do that, especially in manufacturing operations, is obvious. How it is done is not always so clear.

Here is how you can use a sensor, indicator light, and RFID to provide feedback that drives overall quality and efficiency.

 

Machine Condition Monitoring

You’ve heard the saying, “if it ain’t broke, don’t fix it.” However, broken machines cause downtime. What if there was a way to know when a machine is getting ready to fail, and you could fix it before it caused downtime? You can do that now!

The two main types of data measured in manufacturing applications are temperature and vibration. A sudden or gradual increase in either of these is typically an indicator that something is going wrong. Just having access to that data won’t stop the machine from failing, though. Combined with an indicator light and RFID, the sensor can provide real-time feedback to the operator, and the event can be documented on the RFID tag. The machine can then be adjusted or repaired during a planned maintenance period.

Managing Quality – A machine on its way to failure can produce parts that don’t meet quality standards. Fixing the problem before it affects production prevents scrap and rework and ensures the customer is getting a product with the quality they expect.

Managing Efficiency– Unplanned downtime costs thousands of dollars per minute in some industries. The time and resources required to deal with a failed machine far exceed the cost of the entire system designed to produce an early warning, provide indication, and document the event.

Quality and efficiency are the difference makers in manufacturing. That is, whoever makes the highest quality products most efficiently usually has the most profitable and sustainable business. Again, why is obvious, but how is the challenge. Hopefully, you can use the above data to make higher quality products more efficiently.

 

More to come! Here are the data-driven topics I will cover in my next blogs:

  • Part inspection and data collection for work in process
  • Using data to manage molds, dies, and machine tools

Buying a Machine Vision System? Focus on Capabilities, Not Cost

Gone are the days when an industrial camera was used only to take a picture and send it to a control PC. Machine vision systems are a much more sophisticated solution. Projects are increasingly demanding image processing, speed, size, complexity, defect recognition and so much more.

This, of course, adds to the new approach in the field of software, where deep learning and artificial intelligence play a bigger and bigger role. There is often a lot of effort behind improved image processing, however,  some people, if only a few, have realized that part of it can already be processed by that little “dummy” industrial camera.

I will try to briefly explain to you in the next few paragraphs how to achieve this in your application. Thanks to that, you will be able to get some of these benefits:

  • Reduce the amount of data
  • Relieve the entire system
  • Generate the maximum performance potential
  • Simplify the hardware structure
  • Reduce the installation work required
  • Reduce your hardware costs
  • Reduce your software costs
  • Reduce your development expenses

How to achieve it?  

Try to use more intelligent industrial cameras, which have a built-in internal memory sometimes called a buffer. Together with FPGA (field programmable gate array) they will do a lot of work that will appreciate your software for image processing. These functions are often also called pre-processing features.

What if you have a project where the camera must send images much faster than the USB or Ethernet interface allows?

For simple cameras, this would mean using a much faster interface, which of course would make the complete solution more expensive. Instead, you can use the Smart Framer Recall function in standard USB and GigE cameras, which generates small preview images with reduced resolution (thumbnails) with an extremely accelerated number of frames per second, which are transferred to the host PC with IDs. At the same time, the corresponding image in full resolution is archived in the camera’s image memory. If the image is required in full resolution, the application sends a request and the image is transferred in the same data stream as the preview image.

The function is explained in this video.

Is there a simpler option than a line scan camera? Yes!

Many people struggle to use line scan cameras and it is understandable. They are not easy to configurate, are hard to install, difficult to properly set and few people can modify them. You can use an area scan camera in line scan mode. The biggest benefit is standard interface: USB3 Vision and GigE Vision instead of CoaXPress and Cameralink. This enables inspection of round/rotating bodies or long/endless materials at high speed (like line scan cameras). Block scan mode acquires an Area of Interest (AOI) block which consists of several lines. The user defines the number of AOI blocks which are used to create one image. This minimizes the overhead, which you would have instead when transferring AOI blocks as single images using the USB3 Vision and GigE Vision protocols.

The function is explained in this video.

Polarization has never been easier

Sony came with a completely new approach to — a polarized filter . Until this new approach was developed, everyone just used a polarization filter in front of the lens and combined it with polarized lighting. With the polarized filter, above the pixel array is a polarizer array and each pixel square contains 0°, 45°, 90°, and 135° of polarization.

 

What is the best part of it? It doesn’t matter if you need a color or monochrome version. There are at least 5️ applications when you want to use it:

  • Remove reflection – > multi-plane surfaces or bruise/defect detection
  • Visual inspection – > detect fine scratches or dust
  • Contrast improvement -> recognize similar objects or colors
  • 3D/Stress recognition -> quality analysis
  • People/vehicle detection -> using your phone while driving

Liquid lens is very popular in smart sensor technology. When and why do you want to use it with an Industrial camera?  

 

Liquid lens is a single optical element like a traditional lens made from glass. However, it also includes a cable to control the focal length. In addition, it contains a sealed cell with water and oil inside. The technology uses an electrowetting process to achieve superior autofocus capabilities.

Benefits to the traditional lenses are obvious. It doesn’t have any moving mechanical parts. Thanks to that, they are highly resistant to shocks and vibrations. Liquid lens is a perfect fit for applications where you need to observe or inspect objects with different sizes and/or working distances and you need to react very quickly. One  liquid lens can do the work of multiple-image systems.

To connect the liquid lens, it requires the RS232 port in the camera plus a DC power from 5 to 24 Volt. An intelligent industrial camera is able to connect with the camera directly and the lens uses the power supply of the camera.