Beyond the Human Eye

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

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

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

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

Figure 1
Figure 1
Figure 2
Figure 2

Error Proof Stamping Applications with Pressure Sensors

When improving product quality or production efficiency, manufacturing engineers typically turn to automation solutions to error proof and improve their application. In stamping applications, that often leads to adding sensors to help detect the presence of a material or a feature in a part being formed, for example, a hole in a part. In the stamping world, this can be referred to as “In-Die Sensing” or “Die Protection.” The term “Die Protection” is used because if the sensors do not see the material in the correct location when forming, then it could cause a die crash. The cost of a die crash can add up quickly. Not only is there lost production time, but also damage to the die that can be extremely costly to repair. Typically, several sensors are used throughout the die to look for material or features in the material at different locations, to make sure the material is present to protect the die. Manufacturing engineers tend to use photoelectric and/or inductive proximity sensors in these applications; however, pressure sensors are a cost-effective and straightforward alternative.

In today’s stamping applications, manufacturing engineers want to stamp parts faster while reducing downtime and scrap. One growing trend in press shops is the addition of nitrogen on the dies. By adding nitrogen-filled gas springs and/or nitrogen gas-filled lifters, the press can run faster and cycle parts through quicker.

Typically, the die is charged with nitrogen before the press starts running parts. Today, many stamping plants rely on an analog dial gauge (image 1) to determine if there is sufficient nitrogen pressure to operate safely. When a new die is set in the press, someone must look at the gauge and make sure it is correct before running the press. There is no type of signal or feedback from this gauge to the PLC or the press; therefore, no real error proofing method is in place to notify the operator if the pressure rating is correct or even present before starting the press. If the operator starts running the press without any nitrogen for the springs, then it will not cycle the material and can cause a crash.

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Another, likely more significant problem engineers face is a hole forming in one of the hoses while they are running. A very small hole in a hose may not be noticeable to the operator and may not even show up on the analog dial gauge. Without this feedback from the gauge, the press will continue to run and increase the likelihood that the parts will be stamped and be out of specification, causing unnecessary scrap. Scrap costs can be quite large and grow larger until the leak is discovered. Additionally, if the material cannot move through the press properly because of a lack of nitrogen pressure to the springs or lifters, it could cause material to back up and cause a crash.

By using a pressure sensor, you can set high and low pressure settings that will give an output when either of those is reached. The outputs can be discrete, analog, or IO-Link, and they can be tied to your PLC to trigger an alarm for the operator, send an alert to the HMI, or even stop the press. You can also have the PLC make sure pressure is present before starting the press to verify it was adequately charged with nitrogen during set up.

Adding an electronic pressure sensor to monitor the nitrogen pressure is a simple and cost-effective way to error proof this application and avoid costly problems.

Improve Error Proofing with IO-Link and IoT-Enabled Sensors

Though error-proofing sensors and poka yoke have been around for decades, continuing advancements related to the Industrial Internet of Things (IIoT) are making both more accessible and easier to maintain.

Balluff - The IO-Link Revolution!

Designed to eliminate product defects by preventing human errors or correcting them in real time, poka yoke has been a key to a lean manufacturing process since it was first applied to industrial applications in 1960. Today, error proofing relies far less on manual mechanisms and more on IoT-enabled error proofing sensors that connect devices and systems across the shop floor.

IoT is enabling immediate control of error-proofing devices such as sensors. This immediacy guards against error-proofing devices being bypassed, which has been a real problem for many years. Now, if a sensor needs adjustment it can be done remotely. A good example of this is with color sensors. When receiving sub-components from suppliers, colors can shift slightly. If the quality group identifies the color lot as acceptable but the sensor does not, often the color sensor is bypassed to keep production moving until someone can address it, creating a vulnerable situation. By using IoT-enabled sensors, the color sensor can be adjusted remotely at any time or from any location.

The detection of errors has been greatly improved by integrating sensors directly into the processes. This is a major trend in flexible manufacturing where poka yoke devices have to be adjusted on-the-fly based on the specific product version being manufactured. This means that buttons or potentiometers on discrete sensors are not adequate. Sensors must provide true data to the control system or offer a means to program them remotely. They must also connect into the traceability system, so they know the exact product version is being made. Connections like this are rapidly migrating to IO-Link. This technology is driving flexible manufacturing at an accelerated rate.

IO-Link enables sensors to process and produce enriched data sets. This data can then be used to optimize efficiencies in an automated process, increase productivity and minimize errors.

Additionally, the easily expandable architecture built around IO-Link allows for easy integrations of poka yoke and industrial identification devices. By keeping a few IO-Link ports open, future expansion is easy and cost effective. For poka yoke, it is important that the system can be easily expanded and that updates are cost-effective.

Using RFID to Create Transparency in Production

To meet today’s requirements for fast delivery and infinite flexibility, many productions are already set up as flow production with work steps distributed to workstations. As a result, products can be individually adapted in order to optimally meet customer requirements.

The basic prerequisite for this is to continuously know where a product is in the process. Additionally, information should be available about the next workstation and the subsequent work step. Without technical assistance, the required information can only be generated by the employee with much effort. Additionally, you run the risk of production steps being confused and time delays occurring in the production process. One solution to meet the requirements with minimum effort and maximum reliability is to install automated product recognition by using an RFID system.

 
Automated product recognition with an RFID system

To install an RFID system one important prerequisite must be fulfilled. Each product that is planned to be tracked needs a compatible RFID data carrier. This enables an individual connection between the order number and the product, which is then stored in a database.

During the product creation, the stored connection is called up multiple times. Each time it is supplemented by further information. In this way product traceability can be ensured. The connection is initiated by an antenna of the RFID system, which recognizes the data carrier and its ID. The resulting data shows which product is at the workplace, the time stamp, the place of recognition and the order number, all of which are noted in the database.

image 1
Communication between RFID system, database and production employee

 

Reduction of error rate and increase of efficiency in the production

In addition to ensuring traceability, the installation of an RFID system can also significantly reduce the failure rate in the production. The connection to the database allows information to move in two ways. On one hand additional information is provided, while on the other further information is created that can be processed by other systems.

The storage of the time stamp enables an analysis of the duration of each work step. This makes the identification of potential ways to improve in the production possible. If this analysis and the implementation of the system is done consequently, the efficiency in the production can be improved continuously.

 

Improve Your Feeder Bowl System (and Other Standard Equipment) with IO-Link

One of the most common devices used in manufacturing is the tried and true feeder bowl system. Used for decades, feeder bowls take bulk parts, orients them correctly and then feeds them to the next operation, usually a pick-and-place robot. It can be an effective device, but far too often, the feeder bowl can be a source of cycle-time slowdowns. Alerts are commonly used to signal when a feed problem is occurring but lack the exact cause of the slow down.

feeder bowl

A feed system’s feed rate can be reduced my many factors. Some of these include:

  • Operators slow to add parts to the bowl or hopper
  • Hopper slow to feed the bowl
  • Speeds set incorrectly on hopper, bowl or feed track
  • Part tolerance drift or feeder tooling out of adjustment

With today’s Smart IO-Link sensors incorporating counting and timing functions, most of the slow-down factors can be easily seen through an IIoT connection. Sensors can now time how long critical functions take. As the times drift from ideal, this information can be collected and communicated upstream.

A common example of a feed system slow-down is a slow hopper feed to the bowl. When using Smart IO-Link sensors, operators can see specifically that the hopper feed time is too long. The sensor indicates a problem with the hopper but not the bowl or feed tracks. Without IO-Link, operators would simply be told the overall feed system is slow and not see the real problem. This example is also true for the hopper in-feed (potential operator problem), feed track speed and overall performance. All critical operations are now visible and known to all.

For examples of Balluff’s smart IO-Link sensors, check out our ADCAP sensor.

The Need for Data and System Interoperability in Smart Manufacturing

As technology advances at a faster pace and the world becomes flatter, manufacturing operations are generally focused on efficient production to maximize profitability for the organization. In the new era of industrial automation and smart manufacturing, organizations are turning to data generated on their plant floors to make sound decisions about production and process improvements.

Smart manufacturing improvements can be divided roughly into six different segments: Predictive Analytics, Track and Trace, Error Proofing, Predictive Maintenance, Ease of Troubleshooting, and Remote Monitoring.IOLink-SmartManufacturing_blog-01To implement any or all of these improvements requires interoperable systems that can communicate effectively and sensors and devices with the ability to provide the data required to achieve the manufacturer’s goals. For example, if the goal is to have error free change-overs between production cycles, then feedback systems that include identification of change parts, measurements for machine alignment changes, or even point of use indication for operators may be required.  Similarly, to implement predictive maintenance, systems require devices that provide alerts or information about their health or overall system health.

Traditional control system integration methods that rely heavily on discrete or analog (or both) modes of communication are limited to specific operations. For example, a 4-20mA measurement device would only communicate a signal between 4-20mA. When it goes beyond those limits there is a failure in communication, in the device or in the system. Identifying that failure requires manual intervention for debugging the problem and wastes precious time on the manufacturing floor.

The question then becomes, why not utilize only sensors and devices with networking ability such as a fieldbus node? This could solve the data and interoperability problems, but it isn’t an ideal solution:

  • Most fieldbuses do not integrate power and hence require devices to have separate power drops making the devices bulkier.
  • Multiple fieldbuses in the plant on different machines requires the devices to support multiple fieldbus/network protocols. This can be cost prohibitive, otherwise the manufacturer will need to stock all varieties of the same sensor.
  • Several of the commonly used fieldbuses have limitations on the number nodes you can add — in general 256 nodes is capacity for a subnet. Additional nodes requires new expensive switches and other hardware.

IOLink-SmartManufacturing_blog-02IO-Link provides one standard device level communication that is smart in nature and network independent, thus it enables interoperability throughout the controls pyramid making it the most suitable choice for smart manufacturing.

We will go over more specific details on why IO-Link is the best suited technology for smart manufacturing in next week’s blog.

 

What’s best for integrating Poka-yoke or Mistake Proofing sensors?

Teams considering poka-yoke or mistake proofing applications typically contact us with a problem in hand.  “Can you help us detect this problem?”

We spend a lot of time:

  • talking about the product and the mistakes being made
  • identifying the error and how to contain it
  • and attempting to select the best sensing technology to solve the application.

However this can sometimes be the easy part of the project.  Many times a great sensor solution is identified but the proper controls inputs are not available or the control architecture doesn’t support analog inputs or network connections.  The amount of time and dollar investments to integrate the sensor solution dramatically increases and sometimes the best poka-yoke solutions go un-implemented!”

“Sometimes the best poka-yoke solutions go un-implemented!”

Many of our customers are finding that the best controls architecture for their continuous improvement processes involves the use of IO-Link integrated with their existing architectures.  It can be very quickly integrated into the existing controls and has a wide variety of technologies available.  Both of these factors make it the best for integrating Poka-yoke or Mistake Proofing due to the great flexibility and easy integration.

Download this whitepaper and read about how a continuous improvement technician installed and integrated an error-proofing sensor in 20 minutes!

Eliminating Manufacturing Errors Begins with Identifying Trouble Spots

We have all gotten that dreaded phone call or email…the customer received their order, but there was a significant problem:

  • ErrorProofingTagsMissing part
  • Wrong color
  • Leaking seal
  • Improper assembly
  • Too lose…or too tight
  • Incomplete processing, e.g. missing threads
  • Something is damaged
  • Missing fluids or fluids at wrong level
  • …and so on

Assuming that we have reliable suppliers delivering quality parts that meet the required specifications…everything else that can (and often does) go wrong happens inside our own facilities. That means that solving the issues is our responsibility, but it also means that the solutions are completely under our control.

During the initial quality response meetings, at some point the subjects of “better worker training” and “more attention to detail and self-inspection” may come up. They are valid subjects that need to be addressed, but let’s face it: not every manufacturing and assembly problem can be solved by increased worker vigilance and dedication to workmanship. Nor, for that matter, is there the luxury of time or capacity for each worker to spend the extra time needed to ensure zero defects through inspection.

It is often more effective to eliminate errors at their source before they occur, so that further human intervention isn’t required or expected.

Some things to look for when searching for manufacturing trouble spots:

  • Are all fasteners present and properly tightened, in the proper torque sequence
  • Correct machine setup: is the right tool or fixture in place for the product being produced?
  • Manual data entry: does the process rely on human accuracy to input machine or product data?
  • Incorrect part: is it simply too hard to determine small differences by visual means alone?
  • Sequencing error: were the parts correct but came together in the wrong sequence?
  • Mislabeled component: would the operator realize that part is wrong if it was labeled incorrectly in the first place? Sometimes where the error has impact and where it actually occurred are in two different places.
  • Part not seated correctly: is everything is correct, but sometimes the part doesn’t sit properly in the assembly fixture?
  • Critical fluids: is the right fluid installed? Is it filled to the proper level?

Once the trouble spots have been identified, the next step is to implement a detection and/or prevention strategy. More information on the error proofing process is available on the Balluff website at www.balluff.us/errorproofing

When to use a Vision Sensor for Error-Proofing Applications

Vision sensors are powerful Poka-Yoke tools ideal for error proofing your process. However, traditional sensors still solve more applications at a much lower cost. So, how do you decide when to jump up to a vision sensor? There are three application categories that require the use of a vision sensor, which include:

  

  1. Parts are not well fixtured: If the part is not contained in a fixture, or there is no opportunity to bring the part into an inspection station that has better tolerance, then a vision system is the best choice.
    Example: parts directly on moving conveyor belt.

    Parts on free conveyor
    Parts on free conveyor

    Continue reading “When to use a Vision Sensor for Error-Proofing Applications”

Linear Position Sensor Case Study

When we talk to people about applications for continuous linear position sensors, we often point out the advantages that can be realized by “upgrading” a machine and/or a process by incorporating continuous position feedback. In this post, I’d like to offer up a case in point. This “Application Spotlight” showcases the real and tangible advantages that can be realized by using continuous linear position sensors, such as:

• Improving machine/process efficiency
• Reducing set-up and changeover time
• Reducing planned downtime
• Error-proofing the process

So, you see, we’re not just making this stuff up! Download this case study here.

Centering Steel Fed into Press
Centering Steel Fed into Press