Whether it’s the front office or the back office, in the breakroom, bathroom and everywhere in between, people are talking about “big data.” For machine shops and sheet metal fabricators, the primary sources of this data are the CNC machinery, auxiliary equipment and robots used to manufacture products, which utilize increasingly capable suites of sensors to transmit continuous status updates and operational data.
You can thank the Industrial Internet of Things (IIoT) for these capabilities. Manufacturers so equipped gain real-time visibility into every aspect of their various processes, overall equipment effectiveness (OEE) is no longer an educated guess, preventive maintenance becomes more preventive, and managers aren’t asking operators to explain events that occurred two days or two weeks past, as everything from perishable tooling to work instructions and inspection procedures become more manageable and effective.
All this big data might be a wonderful thing, but it gets even better when used to paint a bigger picture for strategic decision making. In other words, rather than answering the tactical “When should we replace the spindle bearings?” and “Why was lathe number five not making parts at 8 p.m. last night?” types of questions.
Doing so requires a level of integration that many shops still struggle to achieve, however. Data must be shared—not siloed—with the enterprise resource planning (ERP) system talking to the product lifecycle management (PLM) software and the computer-aided design and manufacturing (CAD/CAM) software communicating with the tool management system (TMS), etc. There are no secrets in this interoperable environment, especially from the managers and others tasked with acting on the wealth of continuous improvement opportunities that big data presents.
The foundation for this future nirvana begins with the wiring of the shop floor, an accomplishment that is not as widespread as one might think, followed by the gathering and analysis of production data.
“The first step is connecting machinery for performance monitoring and OEE calculations, which we call machine data acquisition (MDA). The second is condition monitoring, where we understand machine and equipment health. But there’s also the need to get work order-related information to the production area, which we typically do through DNC (direct or distributive numerical control) to avoid people carrying thumb drives around, as well as tool management, where we exchange information such as cutting tool usage and offset data with CNC machinery,” said Andreas Steinhorst, startup head for interoperability at Plano, Texas-based Siemens Digital Industries Software. Steinhorst also noted that the latter of these tasks is made simpler with a single, comprehensive platform that covers the lion’s share of a shop’s needs, and that also provides “hooks” or application programming interfaces (APIs) that enable easy integration with other systems where needed.
Corsin Buerer, head of production and quality products at Siemens Digital Industries Software, agreed. “For instance, a CNC programmer typically creates work packages that include four sets of information vital to the shop floor: the part being machined; the resources needed to process it; the incoming materials; and documentation such as the NC program and work instructions. To make this exchange as efficient as possible, though, we believe you must connect it all by having a manufacturing resource library that operates in a PLM context. This allows you to easily manage everything related to a given work order, from the cutting tools and workholding to the toolpaths, gaging, automation and more.”
Very little of what Buerer described falls under the oft-overused big data umbrella, but it is representative of a machining environment with its digital house in order. It’s also indicative of the industry’s call for robust, fully integrated manufacturing systems—a need that Siemens and other software providers continue to address.
One of these is Predator Software Inc. of Beaverton, Ore., where Director of Automation and OEM Relations Mike Rogers suggests that linking multiple software systems to a single database is an excellent first step in the pursuit of integrated data. In Predator’s case, this means nearly a dozen standalone applications that perform functions like DNC, production data management (PDM), tool and fixture management (or Predator Tracker) and manufacturing data collection (MDC).
Tools like these have become standard fare on many shop floors, and as many have found, making them all play in the same sandbox can be challenging. But as Rogers is quick to point out, “Everything in our system works from the same database, so you can do all the heavy lifting—entering the machines, operators, part numbers and so on—just once. After that, adding functionality to the core group becomes quite easy.”
MDC is perhaps most relevant to the big data discussion, since it is responsible for real-time machine monitoring and data collection. “Our MDC system can monitor a wide range of shop floor equipment. In addition to traditional CNC machinery, we recently installed it on a six-spindle screw machine, for instance, and even an autoclave oven. In each instance, we collect whatever data is available, but at a minimum gather metrics like on/off cycles and production rates, all of which is quite valuable.”
He’ll also tell you what anyone who’s attended a machine tool exhibition recently already knows: the number of machine monitoring and analytics platforms has soared over recent years.
Security should be a top concern for anyone traveling down the big data superhighway, especially considering that CNC machine controls often use outdated operating systems.
“Whether it’s a mom and pop shop or a Fortune 500 manufacturing company, it’s not unusual to find machine tools running Windows 95 and Windows NT. There’s no antivirus and no security patches or updates available like there are with newer systems,” said Rogers. “Imagine the look on the IT manager’s face when you plug one of those into the network for the first time. That’s why it’s critical that any software and server gathering data from these machines provides 128-bit encryption and a robust firewall.”
As senior director of the Autodesk Developer Network (ADN), Jim Quanci is well aware of the need for security in software systems. He’s also well aware of the need for integration, which is at least part of the reason why the San Rafael, Calif.-based developer created the Autodesk Platform Services and charged it with providing an environment “for software developers seeking to connect their proven tools and technologies—including CAD, CAM and PLM from a mix of technology providers—to the rest of the enterprise.
“It’s interesting to observe the conversations we have with customers today,” Quanci continued. “Most of them are focused more on helping and supporting the business, rather than simply maintaining machine uptime or efficiency. And that’s a good thing.”
Performing analytics on a machine tool is quite useful, he added, as this helps manufacturers optimize their processes. However, it’s crucial to understand how this optimization will benefit the business overall. For instance, it’s essential to know how many of the manufacturer’s resources are operational at any given time, which ones are experiencing issues such as bearing problems, vibration or power disruption and how all of these different issues impact the overall performance. “Addressing these concerns requires making informed decisions about which machines to repair or replace and understanding the implications for the business in terms of capability and productivity over the short and long term.”
Those decisions extend well beyond the production floor. For instance, feeding relevant information back to the sales team is critical, since they need to inform customers as to when they can expect delivery, whether it’s a machine, a batch of parts or a replacement component.
“It’s critical to create a single, accessible platform where the right people can access the relevant information to make informed decisions and set realistic expectations,” Quanci said. “Achieving this requires addressing issues at the shop floor level and understanding how these affect job planning, material and tooling procurement and much more. Providing visibility of these day-to-day activities to the sales team, maintenance personnel and other stakeholders is key—this way, everyone can collaborate and address potential concerns, such as scheduling machine downtime for necessary repairs or maintenance, to ensure smooth operations and continued customer satisfaction.”
As Buerer of Siemens indicated, the people setting up and programming CNC machine tools need significant amounts of data to do their jobs—data that is often managed in disparate systems. Predator’s Mike Rogers seconded that obstacle with his comments about the benefits of multiple systems tied to a single database.
Paul Van Metre, co-founder of ProShop USA in Bellingham, agrees on both counts. Although he labels it ERP, the company’s software system extends well into MES territory, offers “deeply embedded” quality management system (QMS) and computerized maintenance management system (QMMS) capabilities, and he has partnered with several well-known software providers, Mastercam and QuickBooks among them.
“When we first began developing ProShop ERP, we didn’t want spreadsheets, we didn’t want paper, we didn’t want attachments,” Van Metre said. “We wanted everything to be browser based and native to the application. As a result, we now have a digital manufacturing ecosystem that provides literally everything a machinist or programmer might need to set up a job, prove out their G code and achieve first article buy off.”
Here again, manufacturers are increasing their efficiency by integrating systems and sharing data, whether it’s the data streaming out of a modern CNC machine tool, the data obtained while measuring parts with connected calipers and coordinate measuring machines (CMMs) or the data shared among the shop’s TMS, MES and CAM systems.
That said, it’s critical that everyone using these systems and the communal data within understands the term “system of record,” or the need to make one software platform the master to which the others defer. Van Metre offered an example that any machine shop or tool and die maker would understand: cutting tool data that touches the programming office, the tool crib, the shop floor, and the purchasing department.
“In most cases, the system of record is the one that holds the inventory, which is the ERP software,” he said. “Unfortunately, many of the available platforms out there don’t have the level of integration or depth of information needed by the people using those tools, which is why we designed our system with the manufacturing floor first in mind.”
Datanomix of Nashua, N.H., has partnered with companies like ProShop, Caron Engineering and Hexagon, all with the shared goal of integrating their products wherever possible and wherever it makes sense. Co-founder and CEO John Joseph, together with co-founder, president and CTO Greg McHale, offered several insights to shops looking to gain a strategic, data-based advantage over their competitors.
“The evolution of production monitoring software for precision manufacturers has historically focused on two goals,” said McHale. “The first of these was telling people whether their machines were on or off, and while that was nicer than knowing nothing, it wasn’t much nicer than knowing nothing. The next step was to have humans annotate why the machine tool was down—the ERP industry calls them reason codes. The theory was that people could figure out how to eliminate that problem and keep the machine running more consistently, but that approach does not work for the vast majority of manufacturers that simply cannot deal with their everyday challenges and throw complicated data entry into the mix, too, and then wait 90 days to see if they gathered meaningful reasons.”
Datanomix based its business model on the premise that reason codes and human intervention are the wrong evolutionary trajectory for data and manufacturing, Joseph added. “If we’re always going to rely on an operator to tell us why the machine’s down—and in some cases, why they’re not doing their job—we’re all toast. That’s why we flipped this paradigm on its head and said, ‘No, what you actually need to do is derive as much insight as possible from the machine, and only the machine.’”
Anyone who’s entered reason codes or implemented the software that requires them might be scratching their head right now over how this is possible; CNC machinery has become quite advanced over recent years, but it’s not yet capable of thought or human-like discernment. How, then, can it be expected to answer the types of questions described at the start of this article?
“The underlying assumption is that reason codes are necessary because the software doesn’t understand how a job is supposed to run, and therefore cannot possibly tell you whether something is running well or poorly,” said McHale. “However, we’ve developed an adaptive algorithm that establishes a standard for optimal performance across various metrics, such as cutting time, probing time, touch time and the number of planned stoppages per cycle, among others. Through G-code analytics, we can determine the cause of each stoppage and the expected duration for each event. Once you have these elements of job performance, you fully understand your process capability without putting any additional burden on your operators.”
However one determines the reasons for machine downtime, the next question should be, “Okay, what are we going to do about it?” Cory Carpenter, a digital machining specialist at Mebane, N.C.-based Sandvik Coromant, has a suggestion: call your local Sandvik cutting tool representative.
“While we offer a product and solution that helps clients gather the necessary information to raise awareness and prioritize continuous improvement events, we also address a common concern among customers, which is their fear of being overwhelmed by data and unsure of how to make use of it effectively,” he said. “Our unique position as manufacturers allows us to provide a more comprehensive understanding and guidance for our clients, ensuring that they can fully leverage the data and insights our product offers to improve their operations.”
One of the results of this conversation might be to host a productivity improvement program (PIP), a team-oriented activity that involves not only a full analysis of the problem or opportunity and a suggested course of action, but also a guided implementation plan for verification. If needed, Sandvik Coromant can provide additional support functions and even engage in process development projects for their customers utilizing their CAD/CAM/Engineering Design resources or incorporating the company’s Lean Consultancy services.
“In the past, gathering and analyzing data required considerable time and effort,” said Carpenter. “Companies would spend countless hours on time studies, examining chip-to-chip times, cycle times and other variables. And once they collected the data, it would take a week to input it into Excel, another week to format it, and then an additional few weeks to discuss the findings, secure a budget and initiate a project.”
With solutions like Sandvik Coromant’s Machining Insights, real-time data collection and analytics have changed all that, he noted. Manufacturers can now access the necessary information instantly, allowing for more timely discussions and decision making. “This not only expedites the process of reaching conclusions and driving results, but also minimizes the headaches associated with manual data collection and analysis,” Carpenter explained. “Ultimately, it enables businesses to be more agile and responsive, leading to faster improvements and increased competitiveness.
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