Paper-thin margins. An aging workforce. Increasingly stiff competition from the stamping house across town, not to mention countless overseas suppliers. These are just a few of the obstacles that stamping houses must overcome if they’re to remain productive and profitable, and in many cases, stay in business.
It’s a statement that’s just as true for small job shops dealing with the vagaries of high-mix, low-volume work as it is for automakers pumping out body panels for next year’s F-150. And yet, there’s hope.
One part of the solution is to equip stamping presses and other fabricating equipment with advanced sensing technology; in some cases, shops are using AI-equipped software to analyze the resulting data in search of improvement opportunities. It’s that whole Industrial Internet of Things (IIoT) thing.
Many manufacturers are all in on these novel technologies; others continue to rely on their years of experience to keep the floor operational, backed by no small amount of intuition and the tribal knowledge that will sadly fade away as veteran operators retire. Stamping Productivity spoke with three companies at the forefront of this space about what’s possible, what’s holding shops back and where the data actually leads.
Starting at the press
For most stamping shops, the entry point into monitoring has always been die protection – keeping an expensive, carefully engineered piece of tooling from becoming an even more costly repair bill. Jim Finnerty, product manager at Wintriss Controls Group LLC in Acton, Mass., has been making this case for more than four decades and continues to do so, but suggests that the real opportunity today lies beyond damage control.
That’s because modern press controls don’t just halt the machine when something goes wrong – they record every start and stop, every job change, every downtime reason and every switch between run, program and initialization modes in a machine-specific event log.
“If you want to find the root cause of what happened, you can go back in time and look at it,” Finnerty says.
He shares an example that’s humorous in hindsight, if not at the time of the actual event. The supervisor for a small shop in Wisconsin arrived one morning to find a coil straightener literally dragged across the floor and pressed up against the machine. According to the third shift operator, it had “just happened,” yet the event log told a different story. A coil end had hung up in the straightener, triggering a high-loop fault and rather than investigating the problem, the operator simply reset the alarm and attempted to start the press – multiple times.
“Each stroke pulled the straightener closer until it broke loose from its anchors,” Finnerty explains. “But because the data showed every stop, reset and restart in sequence, there was none of the guesswork or finger-pointing that would normally occur.” He adds, laughing, “The operator had to work hard to make this one happen.”
Connecting success
Visibility like this is possible thanks to a control that manages the press and the processes around it. Wintriss’s SmartPac Pro is said to act as a central platform that supports die protection as well as tonnage monitoring and programmable limit-switch outputs. The result is exact timing windows for sensors, feeders and auxiliary devices throughout the stroke, ensuring the press stops before a bad hit.
There’s more to it than process control, however. Through its ShopFloorConnect production tracking software and ShopFloorConnect Machine Interface (SMI), customers can pull uptime, downtime and setup data, not only from the press but from other pieces of production equipment – press brakes and laser cutters, for instance – and use it to construct a comprehensive view of the floor’s activities. And because the SMI requires only two basic signals such as machine running and cycle complete, it’s considered a practical solution even for legacy equipment.
“We poll every machine once a second, so we’re getting all of this information in real time,” Finnerty says. “We know when it hasn’t been running, why it hasn’t been running and who to contact to get it running.”
The “why” comes from operator-selected reason codes, entered within a preconfigured time threshold after each stoppage. The system automatically logs these codes in a centralized database and can trigger additional actions, such as sending a text or email alert. The result is a detailed, time-stamped record of what just happened (or didn’t) and why, down to a near “whodunit” level of traceability.
These capabilities can also drive immediate production improvements. For one high-mix, low-volume facility, operators routinely waited for QC approval after completing a setup. By configuring the system to automatically alert inspectors and escalate the notification if nobody responds within a preset window, the shop eliminated roughly 10 min. per setup. With multiple presses running across two shifts, this translated to nearly 800 min. of recovered production time daily.
Yet Finnerty cautions against going hog wild on reason codes, recommending that shops keep menus relatively short – a handful of categories, perhaps, not dozens.
“I had one customer that wanted to specify 120 downtime reason codes,” he said. “When I said they needed one more, they asked, ‘What’s the 121st?’ I told their committee, ‘Trying to figure out what downtime reason to select.’”
Whatever the number of reason codes and the reasons for using them, the reason behind all this is clear: These types of data collection systems can reveal things management would never find otherwise. For instance, a high-mix, low-volume shop discovered that its setup times were wildly inconsistent across operators, with one operator able to set up a press in a fraction of the time it took everyone else.
“So, they had him train the crew,” Finnerty says. “That’s the kind of gift that keeps on giving.”
Sounding off
Where Wintriss focuses on controlling the press and logging what it’s doing, Marposs and its Brankamp monitoring systems focus on what the tooling and raw material are physically experiencing. As Joe Bortolameolli, product manager for forming and stamping at Marposs’s Auburn Hills, Mich., office explains, Marposs equipment can monitor inputs and outputs, and Brankamp sensors listen to and measure the forces traveling through the die set itself.
“We’re basically measuring the waves that propagate through the metal, similar to when you pound on a table and someone across the room can hear and even feel it,” Bortolameolli explains. “The lower frequencies produce forming forces while the higher, ultrasonic signals are typically used to detect slugs as they mistakenly form on the top of the material.”
The advantage here is placement flexibility. Because sound propagates efficiently through hardened stripper plates and other tool components, sensors don’t need to sit directly beneath a slug’s drop point – the system can detect abnormal conditions from elsewhere in the die, allowing manufacturers to monitor conditions without invasive, often expensive, sensor installations.
Bortolameolli notes that Brankamp systems are found on everything from high-tonnage presses running as slow as 12 parts per min. to more than 2,000 parts per min. on progressive work. At these speeds, continuous monitoring becomes both essential and beneficial.
He points to one customer that struggled for years with a recurring tooling issue they were unable to isolate.
“Within a few days of having our system in place, they pinpointed the source of the problem,” he says. “That’s the kind of application where tool monitoring really pays for itself. And in many cases, the value isn’t just preventing scrap – it’s about reducing the time it takes to diagnose and correct problems.”
Splitting hairs
As suggested, the same sensing approach applies to deep-drawing and forming operations. In large automotive body-panel applications, for example, material splits can go unnoticed, especially when a crack begins forming at one station but later gets trimmed away.
“Sometimes you don’t see the split, but it started to form long before it became visible,” Bortolameolli notes.
Beyond real-time quality assurance and the ability to shut down a machine if irregularities arise, these systems also create a permanent record of the forces behind each hit, allowing manufacturers to retrieve readings from a specific press on a specific date, at a specific time.
“A stamping house can look back at the data and say, ‘On this date and shift, a bad part was detected and may have got through,’ and then take the appropriate corrective action,” Bortolameolli says.
Such readings also support trend analyses that tell a longer story: in other words, the same tooling compared job over job, the same machine tracked across months. Gradual drift – signals that bounce more than they once did, indicating increased mechanical slop – begins to appear in the curves long before it becomes obvious on the shop floor.
This kind of proactive “let’s stop the train before it goes off the tracks” capability helps explain why an increasing number of OEMs are adopting press monitoring – both within their own plants and beyond. In some cases, automakers aren’t merely encouraging suppliers to implement press monitoring; they’re helping fund it.
“The big companies are telling them to outfit their equipment with Brankamp systems and saying, ‘We see the benefit in it, and we’ll pay for that in your plant,’” he says. “When your customer sees the value and is willing to support it, adoption tends to follow.”
More than sensors
Press monitoring protects tooling and safeguards part quality. The larger question is how that data translates into business outcomes – scheduling accuracy, on-time delivery, customer commitments and profitability. That’s where MachineMetrics, based in Northampton, Mass., enters the discussion.
“Our customers aren’t just looking to monitor machines – they’re looking to monitor production overall,” says Graham Immerman, chief revenue officer. “It speaks to a broader audience with very specific execution problems to solve.”
MachineMetrics positions itself as an intelligent MES and AI-powered monitoring platform for discrete manufacturers, connecting CNC equipment, EDMs and other machine tools with ERP systems and a database filled with operator knowledge. More on that shortly.
Yet stamping presents a distinct set of challenges. Immerman points to a higher concentration of legacy presses, higher volume production, and perhaps most relevant to high-speed stamping, the rate at which a quality or tooling problem can cascade through thousands of parts.
Regardless, the technical starting point is simple: Basic production tracking requires little more than a cycle signal and confirmation that a part is complete. From there, shops can layer in die ID signals, quality anomalies and controller modes. But machine data alone isn’t enough.
“Operational context is equally important to the real-time data from the machine – in fact, it’s the ultimate counterpart,” Immerman notes.
What does “operational context” mean in practice? It means knowing which part is running, against which work order and toward its delivery date. MachineMetrics accomplishes this by using prebuilt connectors that integrate directly with machine data on one side and ERP on the other. When an operator selects a work order, the system automatically ties the machine activity to the corresponding production order – no additional data entry required.
That distinction matters. Asking operators to maintain a second system on top of the ERP system they already use is a reliable way to undermine adoption.
“It isn’t the correct answer to deploy a monitoring tool that becomes a separate system operators have to engage with,” Immerman says. “That creates double data entry, increasing the risk of errors and wasting time.”
Beyond OEE
With operational context in place, overall equipment effectiveness (OEE) becomes actionable rather than merely reportable. Instead of saying, “Our OEE is 75 percent,” Immerman suggests asking a different question: “We had an order that was late – what was our OEE during that run?” If the answer is 100 percent availability, 100 percent performance and 50 percent quality, the diagnosis is straightforward. The machine was running. The die was producing parts. Too many of those parts failed inspection.
The implication is clear: The focus shouldn’t be on uptime, but on identifying quality issues during the run – before they cascade into missed shipments.
The loss of experienced operators – those who could diagnose a press by sound or by the look of a part – is where Immerman sees the stamping industry’s greatest long-term vulnerability. MachineMetrics’ response is what it calls “action-first AI,” centered on a knowledge hub that ties standard operating procedures, process documentation, machine manuals and corrective actions directly to machine events. When something goes wrong, the operator doesn’t just receive an alert – they receive guidance.
“The purpose of monitoring processes and monitoring machines isn’t to create dashboarding,” Immerman says, “it’s to create proactive actions that help you improve your productivity.”
The pitch to frontline workers, Immerman emphasizes, shouldn’t lead with AI. It should lead with problems solved: no more handwritten shift notes, no more manual ERP entry and no more flipping through a machine manual in the middle of a breakdown.
“What if I can solve all those problems for you?” he says. “Then at least you give yourself the best chance of adoption.”
The impact, Immerman argues, can be transformative. “It’s like being blind but finally being able to see for the first time. The companies that hesitate risk missing the low-hanging fruit that delivers early wins and builds momentum for deeper investment.”
With the technology described in this article, today’s stamping houses are boasting some pretty cutting-edge strategies. Read more about what the stamping industry is achieving in our archive of stamping-related articles.
