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Efficient Automation for Data-Driven Foundries

Nov. 1, 2024
Digitalizing and automating a green sand line reduces scrap, increases output and uptime, and requires fewer experienced staff. Then, you can analyze the data it generates to improve your process further.

Many foundries are still operating their pouring lines almost completely manually. If operators make even the smallest errors in timing or machine settings, scrap increases, production slows, or the line stops altogether. Increasingly, however, to maximize casting quality, output, and uptime, some foundries are implementing data-driven process automation.

By seamlessly interfacing together the automated systems handling molding, pouring, cooling, and shakeout – known as machine-to-machine (M2M) integration – the casting process becomes faster and consistently produces higher-quality components.

Automating some parts or all of a process helps get the most out of investments like fast molding machines, and the data gathered builds a digital picture of operations. Comprehensive M2M integration also requires fewer experienced operators with years of specialized experience. With fewer workers involved overall, operations become safer too.

How does M2M work?

To synchronize all or part of a green sand line, first you need to digitalize the machinery. That means connecting interfaces to machine PLCs to support bidirectional data transfer. Another key requirement is some form of central process “bookkeeper” that knows the real-time position and status of every mold and can share that data with other machines.

DISA’s Monitizer® | CIM (computer integrated modules) system is one way to do this. It offers a library of digital interfaces plus rich functionality, including recipe management and preventive maintenance. A broad range of machines can be connected.

Standardized and well-documented interfaces make it easy to connect almost any new machine on the moulding line. Existing interfaces include Convitec shakeout systems and pouring automation from pourTECH, Selcom, Koins, Fujiwa, and HWS.

Digitalizing makes equipment ready for more ambitious projects that bring multiple, previously standalone machines together to work in harmony. It also opens the door to a digital picture of production, real-time line monitoring and data-driven insights that can help radically improve performance.

The CIM system logs every mold produced and, with its central clock, generates a stream of time-stamped data that, second by second, lets it calculate the position of each mold as it moves down the line. Exchanging this real-time data with other machines’ control systems underpins exact synchronization.

This central system gathers much other vital information – current mold size, thickness post-squeeze, or “ok/not ok to pour” – from the molding machine and shares it with other line equipment. In turn, it receives and stores quality-critical parameters for each mold. For example, the automatic pouring unit shares pouring temperature and time, and whether pouring and inoculation were successful for each mold.

Automating different parts of the line

Green sand lines typically just need supervision during steady production. But following pattern changes or other stoppages, manual input is required. As just-in-time production is the norm now, foundries must cope with more, much shorter runs.

Pattern changing itself used to be the bottleneck but equipment like DISA’s Automatic Pattern Changer (APC) can change plates in as little as one minute. That does not leave much time to set up the rest of the line for new molds and castings – and this is where automation and synchronization come into their own.

Automating mold transport to control in-mold cooling time following pouring is a good example and is probably the most widely used M2M application seen today.

After a pattern change or other stoppage, poured castings will have cooled more than usual so the line can run faster for a while, perhaps at the maximum speed of the molding machine. Go too slow and you are wasting time, but if you run too fast you risk sending castings into shakeout and cleaning while they are still too hot. In fact, it’s almost impossible to manually set the ideal line speed required to maximize production after a halt. A line speed far below optimum is the only safe manual option.

But if the system controller knows where each mold is to within a fraction of an inch, how long production has stopped, and all the other relevant parameters, it can easily calculate how fast the molding machine and transport can run after a stoppage – and then exactly when to slow down again so the molds poured post-stoppage have enough time to cool before shakeout.

Shakeout and casting cooling

After a pattern change, the operator must move from the molding machine down to the shakeout control unit, manually change to the new settings once the first new castings start appearing, then go all the way back to the molding machine.

With automation, the shakeout table or vibration conveyer control system (PLC) “knows” to change its settings right when the first new mold reaches it after a pattern change. For a vibration conveyer, that means each casting type gets exactly the right speed and vibration frequency it needs. Manually set the frequency too low and those castings can pile up, causing stoppages.

The same applies to sand and casting cooling automation. Run manually, the operator must move to the end of the line, choose the settings – water dosage, cooling time – and apply them at the right moment. Too little water and the castings are too hot, operators cannot remove gating systems after shakeout, and the castings get damaged during shotblasting. Too much water creates a pile of mud in the drum and the line must stop while someone shovels it clear.

With automation, the cooling drum’s sand/iron/water ratio and cooling time for each casting – the “recipe” – is defined once in the database and set correctly every time that casting is produced. Once again, that changeover occurs just as the first new casting reaches the drum.

Sorting before shakeout

Automation’s built-in mold tracking also recognizes where any groups of bad molds are on the line. Conventionally, if metal testing shows a bad magnesium treatment, many perfectly good castings must be discarded to avoid shipping scrap to customers. That’s because they look identical to good castings and operators only have a vague idea of where the bad batch starts and finishes on the line. In fact, it’s common to isolate two batches before and two batches after a bad batch.

With digitalization, operators can mark those molds as bad in the system, then make sure to sort them out before they are mixed up in shakeout. That might be as simple as a red/green light that tells manual sorters when to remove bad castings, but sorting can be completely automated too. That way, when the first bad mold arrives, the central system triggers a robot arm or other device at end of the cooling transport to start isolating them.

The same can be done with other bad molds, perhaps one with an improperly placed core. The operator can immediately mark it as bad at the molding machine, knowing it will be safely sorted out before shakeout.

Pouring … the acme of automation

To keep pace with the high speed of vertical molding, more foundries have adopted automated pouring, along with techniques like pouring two molds simultaneously (known as “double index pouring”). Automated pattern changes increase efficiency further, but then manually repositioning the pouring device after a pattern change may create a bottleneck.

Fully synchronizing pouring and molding – seamless pouring  is the answer and is particularly valuable for foundries challenged by multiple shorter runs. Coordinating both functions in all possible situations requires deep M2M integration and, again, the key is to know exactly where each mold is all the time. That lets the pouring unit calculate where and when to pour the next mold and, if required, adjust the pouring device’s position accordingly.

 

This is relatively simple during steady production. The mold string moves forward by the same distance after each pour – the mold thickness – and the pouring device stays in the same position. Compensating for any minor variation in mold thickness requires only small adjustments.

But for vertical molding, when the pattern changes, so does mold thickness. That’s because the DISAMATIC process varies mold thickness to keep the sand-to-iron ratio constant and account for the pattern heights. That enhances quality and minimizes resource consumption. But after a pattern change, the molding line will be producing one mold thickness while the pouring unit is filling another.

During the transition period, the automated system must adjust the pouring position each time it pours the remaining molds from the previous pattern. If the new molds are thicker, the molding machine will occasionally have to wait while two molds are poured. If the new molds are thinner, the pouring unit also must be able to skip pouring for one molding cycle.

Seamless pouring at Ortrander

German foundry Ortrander Eisenhütte operates three DISA molding lines, daily producing around 100 metric tons of castings in short runs of one hour or sometimes even less. It changes patterns frequently.

Ortrander found that positioning the pouring device manually and pouring molds was too slow, required more operators, and was prone to errors like overpours. Its staff eventually became tired, lost concentration, and made mistakes like adding slack.

Now Ortrander’s furnaces, molding lines, and pouring are all digitally controlled and synchronized, running almost completely automatically.

Following a pattern change, the pourTECH pouring controller calculates where to position the pouring device based on data from the CIM system. It knows exactly when the first new mold reaches the pouring device and automatically switches to the new pouring sequence. If the pouring device ever reaches the end of its travel, the molding machine pauses while the pouring device repositions itself – all automatically.

Changeover time has dropped from 4.5 minutes to under two. With between eight and 12 pattern changes, pattern-changing occupies around 30 minutes per shift – less than half the time prior to automation.

Seamless pouring’s extra consistency plus more ability to optimize the process has cut scrap by 20%. Only two people run the whole line, instead of three previously; during some shifts, three people operate two lines. Besides monitoring, all they do is select the next pattern, manage sand mixing, and transport melt.

Although operators do require some training for automated operations, the extra process information it supplies aids correct decision-making so fewer experienced staff are needed. In future, the machine may make all the decisions itself.

The data dividend

Synchronizing automated molding with other sub-processes like pouring, cooling, and shakeout delivers a faster process with lower scrap. Add automatic pattern changes and the line effectively runs itself with minimal manual input.

Each foundry will need a slightly different, tailored solution but the technology is well proven. First launched in the 1990s and constantly updated since then, Monitizer | CIM has been adopted in some form by around half of DISA customers.  The M2M integrations described here, including seamless pouring, are currently operational in multiple locations around the world, and are well within the reach of all modern foundries.

Along with functions like recipe management and process alarms, digitalization also provides an Industry 4.0-compliant foundation for data collection, storage, reporting and analytics. Ortrander collects around a thousand parameters for every mold it pours. Foundry managers can view detailed reports and drill down into the data, to identify the root causes of complex, interlinked casting problems.

If surface inclusions appear in castings, supervisors can immediately check for out-of-tolerance parameters. Or they can gauge how pouring level and temperature affect mold filling for each individual pattern.

The process database is also the starting point for automated analytics, like machine learning and AI. AI-driven, full process optimization is proven to radically improve performance; multiple foundries have reported scrap reductions of 40% or more using the Monitizer | PRESCRIBE service.

In the past, a foundry’s greatest assets were its patterns and the experience of its workforce. Now, with wider automation allied to Industry 4.0 systems, digital proficiency is rapidly becoming the third pillar of casting success.

About the Author

Dr. Per Larsen | Innovation Manager

Dr. Per Larsen is Product Portfolio and Innovation Manager at DISA Industries A/S, part of Norican Group.