Automation-ready spray and the smart factory's data layer

For most of the history of automotive stamping and paintshop operations, the fluid and lubrication systems that keep presses, dies and coatings running have occupied a peculiar blind spot. They sit downstream of the robots that plant managers photograph for investor presentations, and upstream of the scrap data that finance departments scrutinise line by line. A nozzle, a pump, a felt roller. Commodity items, replaced when they wear out are rarely interrogated for what they might tell a plant about its own performance.

But that is changing, and not because spray equipment has suddenly become prestigious, but because the smart factory runs on data, and a fluid system that cannot report what it is doing is effectively invisible to the systems built to optimise around it.

Ask Brian Curtis, Stamping Lubrication Field Spray Expert at Spraying Systems Co., where fluid management sits on a plant's priority list when ‘Smart Factory’ investment is planned. "Most of the time it is the case that OEMs and Tier 1s are having quality issues with the current process. So naturally, they start looking at ways to update the process," he says.

So “automation-ready spray” tends to arrive as the solution to a problem a plant has already diagnosed elsewhere, rather than as a standalone capital pitch. Nor is it always the same department that champions it, with the push coming from a mix of quality, health and safety, and from manufacturing itself.

What "automation-ready" means at the nozzle

The phrase "automation-ready" is used loosely across production and supply chain, applied to everything from a single sensor bolted onto legacy kit to a fully networked control architecture. But at the component level, Sam Scully, Project Engineer at Spraying Systems Co., is precise about what it actually requires.  "Nozzles, pumps and valves need to be able to connect to our AutoJet controllers and respond to triggers to turn on and off and vary duty cycle. This way the system can be controlled down to the level of fluid being applied to the product," he says.

That last clause is the significant one. Control down to the level of fluid applied to the product is a different proposition from control down to the level of a spray bar switching on and off, the difference between a system that behaves consistently on average and one that behaves consistently at every point on an automotive part's surface.

Data as language: talking to the wider factory

Automation-ready hardware is only useful if it can converse with the systems around it, and here the architecture is more conventional than the term "smart factory" might suggest. Scully explains that "Autojet spray system controllers can interface with local PLCs at the factory, sending data on nozzle on/off time, duty cycle, application rate, and other fluid delivery variables."

Spray control speaks the same dialect as the plant's programmable logic, which is what allows it to be absorbed into existing manufacturing execution systems without a parallel infrastructure project.

The variables Scully lists may be unglamorous to the untrained eye, but they are the raw material of predictive maintenance, and consumption benchmarking. A fluid system that cannot generate this data cannot participate in either, however sophisticated the rest of the line becomes.

Catching wear before it accumulates costs

One concrete illustration of what that data enables - is nozzle wear, a failure mode that in an unmonitored system tends to be caught only once its consequences, wasted material and inconsistent coverage, have already appeared. "When a nozzle wears over time, the orifice opens up and starts to allow too much flow through the nozzle, wasting valuable material,” says Scully. “Flow and pressure sensors connected to AutoJet controllers in the smart spray system can help users detect when the flow coming out of a nozzle has increased out of the acceptable range.”

And the mechanism is simple - which is part of the point. Wear does not need a sophisticated diagnostic model to be caught early. It needs a sensor watching the right variable continuously, and a threshold that flags deviation before it becomes drift, and drift becomes waste.

The commercial case, and where it gets complicated

For plant managers, the more pressing question is how automation-ready spray stacks up against other items competing for the same capital, whether robotics or an MES rollout. Curtis' answer is that the comparison, once made properly, tends to resolve itself. "We discuss ROIs. When a customer sees how much costly material they are currently wasting by not applying the correct amount of fluid, or overspraying onto equipment and plant floors, automation becomes an easy ROI by eliminating these problems," he says.

But of course, the timelines involved are wide, and Curtis is candid about the scope, rather than quoting a single headline figure. In considering the commercial case, "ROIs can range from a few months to 18 months," he says, a spread that reflects how much the underlying economics depend on fluid cost, production volume and the severity of the waste being eliminated.

What tends to slow that conversation down, in Curtis' experience, is not, in fact, the allocation of capital, but a misplaced assumption about complexity. "Many factory employees think that automating a spray process will cause more issues than it actually solves. In reality, however, these systems are much less cumbersome and easier to use than most people assume," he says. The perception gap sits not in the business case, but in the imagined operational burden of managing it.

Retrofitting without ripping up the line

Not every plant weighing this decision is building a new line however - and retrofitting automation onto equipment never designed for it, carries its own engineering logic. "Retrofitting automated spray systems onto non-automated lines can be difficult, but it is not impossible,” says Scully.

“The main challenge is making sure that all of the equipment is compatible. Any nozzle can be connected to a smart spray controller, and even systems that are completely manual with an open pipe or brush and no nozzle can be transformed into an automated line.” A very dynamic solution.

And that compatibility question grows more demanding as the fluid itself gets harder to handle, and Scully treats line speed, viscosity and geometry as a set, rather than as isolated variables. "All of these factors heavily impact the settings that need to be used for the automated spray line,” he says.

“Speed variation in the line has to be linked to duty cycle so that the application is the same no matter the line speed. Fluid viscosity is critical in selecting the nozzle and setting the pressure and flow parameters. If you have a header with nozzles, part geometry dictates which nozzles you turn on and off at which time in order to only apply fluid where it is needed and nowhere else. If the spray is on the end of a robotic arm, application rates need to be varied depending on which area of the part geometry the robot is spraying onto."

Design agility has kept pace with that complexity, largely through additive manufacturing. Scully says, "3D printing greatly increases the number of iterations of a design that can be manufactured and tested in a short amount of time. Before 3D printing, at our facility making prototype nozzles for automotive applications usually took 1-2 weeks for one iteration.

“Now, using 3D printers at our facility, (also known as additive manufacturing), I often print 10-15 iterations of a design overnight and have it ready for testing in our lab the very next day. This allows more flexible nozzle design and application of more creative design ideas," he says.

The gap between a system on paper and one on the floor

None of this guarantees that a specification sheet translates into reliable production performance, and it is here that Scully draws his sharpest distinction, between a system that is installed and one that actually works.

"In many lubrication applications for sheet metal stamping, a smart system has to be very dialled in, to the particular application to properly work. You can have a whole spray system installed and ready to go - but for the system to actually perform reliably, oftentimes it is best to have a Spraying Systems expert come on-site and help the factory users dial in the parameters such as duty cycle, pressure, and flow. This way, even if there are changes in the production parameters, the system will apply the correct amount of fluid, every time," he says.

That observation cuts against a tempting assumption, that connectivity and control hardware are themselves the solution. They are, however, the precondition for one. The tuning that follows is where the theoretical capability of an automation-ready system becomes a practical, actionable, measurable one.

Taken together, the picture that emerges from Curtis and Scully’s expertise is not that conventional spray systems have been quietly generating smart-factory data all along. Rather, it is that adding the right automation, sensing and connectivity is turning a previously low-visibility process into a measurable part of the production system.

Once equipped with compatible nozzles, pumps, valves, controllers and sensors, the spray system can report variables such as flow, pressure, duty cycle and application rate to the wider factory. That creates the visibility needed to identify wear, reduce material waste, maintain consistent application and respond to changes in production conditions.

Curtis' own sense of what separates the operations that capture real value is clear enough: product savings, better quality, and consistent spray and flow rates, delivered not as a one-off installation but as a continuously monitored process.

Fluid management was never inherently outside the smart factory. It has simply been one of the less visible processes to be connected to it. As stamping and paintshop operations become increasingly data-driven, bringing that layer of automation and monitoring into the fluid system can turn an overlooked consumable process into another source of actionable production data.