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.
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.
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.To 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.
IO-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.
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!
We have all gotten that dreaded phone call or email…the customer received their order, but there was a significant problem:
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
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:
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.
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.
Picture this scenario. You, your spouse, or one of your kids happens to be riding one night in the middle of nowhere when a tire blows on the car. First, we can only hope that your loved one remembered the lesson they received on how to change a flat tire in a pinch (if we gave it to them in the first place), because on this particular night, there’s no cell coverage where they’re at, AAA isn’t going to get to them very quickly, there isn’t a can of Flat Fix in the trunk, and there isn’t much traffic on the road they’re traveling on for a good Samaritan to likely show up any time soon (the scenario is extreme, but not impossible). The jack kit sitting under the spare tire is going to seem pretty doggoned important, don’t you think?
We take a lot for granted these days and for those of us who have been involved in the world of factory automation for many years, getting to work with customers to help solve Error-Proofing challenges on the plant floor is like one big “Class Trip” every single day! It’s kind of like providing our customers with “toys for adults”. And it’s a real hoot. We get to see how stuff is made, get the opportunity to help manufacturers build better products through our Error-Proofing sensing technologies and learn over time which end products to buy and which ones to shy away from! We also quickly realize the extreme importance of the DETAIL! Like the components in the emergency jack kit! What if the main handle was missing when you or your relative went to jack up the car? What if there wasn’t any grease on the main lift shaft threads and the car couldn’t be raised? What if other parts were missing from the kit? Not a good scenario.
Balluff has the opportunity to share some of the company’s proven Error-Proofing Techniques in a Seminar at Fabtech on November 14, 2011 in McCormick Place in Chicago, Illinois. The session is segmented into two areas:
Automated/Robotic Weld Cell Process Improvement. We continue to see a great deal of need in this arena. When the economy tanked in 2007/2008, many companies inside and outside of the Automotive Industry were on the edge and many good, talented people were let go. In some cases, the people whose jobs were eliminated had many years of experience in maintenance and in manufacturing engineering. When volumes of work came back, so did the problems associated with weld cell nesting, Poka-Yoke, clamp sensing because of loading impact, weld debris hostility and other issues related to peripheral sensing devices in weld cells; in many cases, without the experienced personnel to reduce time in consumption used to address a wide range of problems. In this session, we will discuss and provide examples of proven techniques aimed squarely at these productivity and time-wasting problems that will return significant ROI for many customers.
Error proofing your manufacturing processes can be as easy as 1, 2, 3. You should be able to freely deploy error proofing in all appropriate locations in your plants without concerns regarding costs and long-term support or stability. It all starts by first identifying your trouble spots, then implementing a detection method, and finally establishing a process to handle the discrepancy. Let’s discuss the detection methods using sensors, as well as the process, for handling discrepancies.
By utilizing sensors as opposed to vision systems or other passive approaches, the cost of implementation and maintenance is reduced. With the new generation of low-cost lasers, sensors are now more affordable and easier to implement. Radio Frequency Identification (RFID) brings new opportunities for handling non-conforming products. By tagging the individual part, assembly, or lot, products can be directed to the appropriate rework or scrap area.
These methods will allow you to implement more error proofing in your manufacturing lines to save thousand of dollars in scrap or rework and avoid the potential for costly containment.
I am experiencing the future of tradeshows; a networking & educational conference without the travel, the expense, and the suit! I can sit at my desk and make contact with future vendors and customers. The online database GlobalSpec hosts multiple times per year industry specific virtual tradeshow events. There are presentations and exhibitors. A place to sit and drink virtual coffee with your peers and of course the token giveaway raffles.
Today I am working the Balluff booth in the Sensors and Switches Virtual show. It is a collection of companies and attendees from many different industries. I really enjoy these events because we can contact quickly with potential customers and potential vendors right from the comfort of our conference room and at a much reduced cost. Here you can see our hard working staff chatting with customers.