visualization Archives - AUTOMATION INSIGHTS

Machine Vision: A Twenty-first Century Automation Solution

Lasers, scanners, fingerprint readers, and face recognition is not just science fiction anymore. I love seeing technology only previously imagined become reality through necessity and advances in technology. We, as a world economy, need to be able to verify who we are and ensure transitions are safe, and material and goods are tracked accurately. With this need came the evolution of laser barcode readers, fingerprint identification devices, and face ID on your phone. Similar needs have pushed archaic devices to be replaced within factory automation for data collection.

When I began my career in control engineering the 1990s high tech tools were limited to PLCs, frequency drives, and HMIs. The quality inspection data these devices relied on was collected mostly through limit switches and proximity sensors. Machine vision was still in it’s expensive and “cute” stage. With the need for more information, seriously accurate measurement, machining specs, and speed; machine vision has evolved, just like our personal technology has, to fill the needs of the modern time.

Machine vision has worked its way into the automation world as a need to have rather than a nice to have. With the ability to stack several tools and validations on top of each other, within a fraction of a second scan we now have the data our era needs to stay competitive. Imagine an application requiring you to detect several material traits, measure the part, read a barcode for tracking, and validate a properly printed logo screened onto the finished product. Sure, you could use several individual laser sensors, barcode readers and possibly even a vision sensor all working in concert to achieve your goal. Or you could use a machine vision system to do all the above easily with room to grow.

I say all of this because there is still resistance in the market to move to machine vision due to historical high costs and complexity. Machine Vision is here to stay and ready for your applications today. Think of it this way. How capable would you think a business is they took out a carbon copy credit card machine to run a payment for you? Well, think of this before you start trying to solve applications with several sensors. Take advantage of the technology at your fingertips; don’t hold on to nostalgia.

Industry 4.0: What It Is and How It Improves Manufacturing

Industry 4.0 is a common buzzword that is thrown around along with IIoT and Process visualization but what does that mean and how is it integrated into a manufacturing process? Industry 4.0 refers to the fourth industrial revolution. The first dealing with mechanization and the use of steam and water power, the second referring to mass production using assembly lines and electrical power, and the third referring to automated production and the use of computers and robots. Industry 4.0 takes us a step beyond that to smart factories that include automation and machine learning. Again, buzzwords that can be hard to visualize.

A commonplace example of this would be self-driving cars. They are autonomous because they don’t need a person operating them and they take, in real time, information about their surroundings and use that to determine a course of action. But how can this type of technology affect a manufacturing process?

Industry 4.0 requires data to be analyzed. This is where IO-Link comes into play. With IO-Link, you are able to get information from a sensor more than than just an output signal when it detects a part. A photoelectric sensor is a good example of this. The basic way a photoelectric sensor works an output is given depending on the amount of light being received. If the sensor happens to be in a dirty/dusty environment, there could be dirt collecting on the lens or floating in the air which effects the amount of light being received. An IO-Link (smart) sensor can not only fire an output when detection occurs but can give information about the real time gain of the sensor (how much light is being received). If the gain drops below a certain amount because of dirt on the lens or in the air, it can send another signal to the controller indicating the change in gain.

Now that we have more data, what are we going to do with it?

We now have all of this data coming from different parts of the machine, but where does it go and what do we do with it? This is where process visualization comes into play. We are able to take real time data from a machine and upload it to a database or system that we can monitor outside of the plant floor. We can know if a machine is running properly without having to physically see the machine. The information can also give us indications about when something might fail so preventative maintenance can take place and reduce downtime.

As more manufacturing processes are becoming automated, machines are becoming more and more complex. A machine might be needed to run 6-7 different lines rather than just 1 or 2 which can involve things like tool change or settings changes. Then, more checks need to be in place, so the right process is running for the right part. Industry 4.0 is how we are able to gather all this information and use it to increase efficiency and productivity.

Boost Size-Change Efficiency with IO-Link Magnetic Encoders and Visualization

In many industries, especially in Packaging, the need to minimize capital equipment costs drives engineers to implement low-cost, manual methods of size change (also called format change) on their machinery. In most cases, this means hand-driven cranks with mechanical dial pointers and/or mechanical revolution counters.

While cost is saved on the procurement side, cost is also shifted over to the operational side. Plant management is left with the task of keeping accurate records of various machine set-ups needed to run different products, as well as the task of training machine operators to perform all machine set-ups correctly. It doesn’t always go as smoothly as expected, and machine reformatting can result in longer downtime than planned, machine stoppages, and possibly excessive scrap.

The key to size-change improvement is capturing the linear movements of the machine components and bringing them into the control system, and then providing “smart” visual feedback to the machine operator during setup. For capturing machine position, a robust and cost-effective magnetic linear encoder is ideal. However, traditional linear encoders deliver an A-B quadrature incremental signal, which requires re-homing upon start-up or after a power loss. What’s needed is an absolute encoder signal, but that brings other challenges such as the cost and complexity of implementing an absolute signal like SSI (Synchronous Serial Interface).

Fortunately, there’s a new encoder interface BML SL1 Absolute Magnetic Encoder with IO-Link option that eliminates the problem of non-absolute feedback and the hassle of absolute position signal interface: IO-Link. IO-Link is a multi-vendor, non-proprietary, device-level serial digital interface that can be aggregated onto today’s Ethernet industrial networks. Magnetic linear encoders are now available that feature absolute position indication combined with the ease and convenience of the IO-Link communication protocol.

Now we just need to provide visual feedback to the machine operator regarding which direction and how far to turn the hand cranks. Once smartlight_18x18_300dpi again, IO-Link provides the answer in the form of an IO-Link-enabled, fully programmable multi-segment LED stack light. When a new machine set up is required, the position parameters are stored in the controller. The controller communicates over IO-Link to the LED stack lights, indicating to the operator which dials need to be turned and in which direction. For example, a horizontally mounted stack light could be lit red on the right half, indicating that the dial needs to be turned to the right. As the position moves closer to the proper setting, the red segments count down until the entire stack light goes green, indicating that the correct position for that axis has been reached. No paper records to maintain and store, and very little training required with the intuitive operator visualization.