by Andrew Brooks
Automating quality control starts with a solid business case and buy-in
Automation has reached into every phase of modern manufacturing, delivering huge improvements–quantitative and qualitative–right across the board. But that doesn’t mean it has penetrated the industry to the fullest possible extent. In particular, smaller shops with fewer resources often continue to rely on manual processes that a small, inexpensive dose of automation could make much more efficient.
Quality management is one area where automation has truly exceptional potential, offering repeatability and precision that human operators can’t match. And automating quality management doesn’t have to be an all-or-nothing proposition, where shop owners have to invest thousands or millions, and reinvent their operations, to achieve significant improvements. It can begin with simple, inexpensive, non-disruptive steps. But as with any improvement, the key to automating quality management lies in doing the homework first.
In a 2015 AutomationWorld article, automation experts from Siemens described five key elements in ensuring that the process of automating quality inspection is successful:
- Establishing clear and accurate information about what’s being measured–the critical part data that needs to be captured and the exact product specifications to be met.
- Tracking and tracing products and their pass/fail scores, and being able to retrieve that information at any point.
- Making sure that the right instrumentation is used, and that there’s enough of it to monitor everything being produced.
- Staying in control of where stuff goes, and making sure nothing moves before the required inspection–or some other action–has been performed on it.
- Communication: two-way networking between devices and systems to monitor and control processes and parameters.
With the possible exception of the fifth point, which does require greater investment and a higher degree of technical sophistication, this list is valid for any kind of QM/metrology automation on any scale.
Automating quality management can start by adding automation to an otherwise entirely manual inspection process, for example where an operator measures a part manually, writes the data down on the spot and later enters it again into a back-end software system via a tablet or PC.
But it’s essential to make the business case first. Automation–of quality management or any other process–shouldn’t be viewed as a goal in itself.
“It’s very critical to work back from the problem you’re trying to solve,” says Don Manfredi, Integrated Solutions Group, Hexagon Manufacturing Intelligence. “The number one thing I tell people about automation is: don’t automate just because someone told you that you should. Make sure the process and the problem you’re trying to solve lends itself to automation.”
The motives for automating quality management are the same as they are for any kind of automation, varying according to the level of technical sophistication:
- to eliminate human performance of repetitive, low-value-added tasks and free employees for more important work;
- to eliminate potential human error, as well as the variables human performance can introduce into the measurement process;
- to increase the speed and efficiency of quality management;
- to increase accuracy; to gather greater amounts of data; to enable the data gathered during quality management processes to be fed back into the production process automatically to make changes and adjustments.
In a shop with a lot of manual processes, the word “automation” tends to sound a lot like “layoffs.” That’s why Manfredi emphasizes that it’s vital to get buy-in from the start.
“You have to sell your vision for what you’re trying to accomplish with the automation, and you have to sell it at every level. This is an internal sales job–this is not a vendor doing it for you. You have to meet with every level at every possible chance and tell them not just what you’re doing, but also why. You’re not going to be able to do an automation project of any magnitude without the support of everybody.”
That support will be more forthcoming–at least on the shop floor–if the initial target for automation is the kind of low-value labour most operators probably don’t enjoy doing in the first place, such as loading and unloading parts, says Dr. David Chang, technical sales manager, CMM, measurement and automation products, Renishaw.
“The loading and unloading of parts as they’re being inspected is a very repetitive task, and it doesn’t add a lot of value to my part,” Chang says. “It is something that I have to do, but I can
easily automate that process. I pay an initial price for the integration, and from that point the machine can do it.”
This is the best kind of automation: the use of machines to perform drudge work, freeing up human operators for more advanced, rewarding tasks requiring the kind of intuition, judgment and experience machines can’t supply.
Step by step
Even small shops are already using automation in one form or another. “Many are still doing touch-trigger measurement on manual or automated CMMS–very precise, very slow, very accurate measurement. That’s a form of automation, because you write a program, you push a button and the CMM goes out and measures,” Manfredi says.
“But if you want to take it a step further, you could automate the part loading and unloading on the CMM. The part could be automatically shuttled from where it gets manufactured into the CMM tooling. Or you could have someone hand-cart the part to the CMM room and then plug the cart into the back end of the fixture, and the automation takes over. You’re typically seeing automation for movement, for picking and placing, that kind of thing. That’s where automation starts for metrology.”
One precondition is good information. “You need good data, i.e. starting with the right tool for the job,” says Gord Homann, sales team coordinator, Mitutoyo Canada Inc. “Making sure it’s accurate and repeatable enough for you to make decisions regarding process adjustments. Without good data you can’t make reliable decisions about your process.”
Homann uses the example of measuring shaft diameter, where the options are using a Vernier gauge or a micrometer. “You’ll get a measurement value from each device, but traditionally a micrometer is more accurate, so I’ll get data that’s accurate enough relative to the tolerance of what I manufacture. Then, when I make decisions to adjust my process, I can be confident that those are reliable decisions. That’s the first step. You have to make the right tool choice, whether it’s a Vernier, a micrometer, sensors, indicators, a laser, all the way up to CMMs.”
Another step can be moving up to a digital micrometer that provides SPC data output directly to a handheld device on the shop floor, such as a tablet. The tablet can be equipped with relatively simple data analysis software to analyze trends and patterns.
“The operator’s decision is entering the data into the system,” Homann says. “The software is analyzing the data and what’s trending in the process, and then doing tool offsets to the tool offset table. The next step can be integrating that digital tool–it could be an indicator or a laser scan micrometer–into the process, so that every time a part comes along in the queue, the measurement is taken automatically and that data is pulled into the system. There’s no operator involvement.”
The most important human involvement as the automation becomes more sophisticated is the up-front engineering input and decision making required to set up the process. In the above example, the decisions include what kind of gauging is to be used, and how collected data will be communicated in real time to the controllers in the manufacturing cell to do the tool offsets.
A job shop can start by simply automating just the inspection process, Chang says. Once a comfort level has been established and the results that the automation generates are known to be reliable, the loop can be closed by linking directly back to the CNC machine to make adjustments on the tool in real time as the tool wears. It’s a big improvement over the manual processes many shops are still using.
“In traditional shops you see operators using hand tools like calipers, micrometers, Vernier height gauges. Each operator might use the caliper slightly differently, with a slightly different force to the device, so you get variations,” Chang says. “The repeatability you get with the device is way better than having an operator doing it manually. You’re taking variables out of the system. You’re taking human error out of the system.” SMT