EFFICIENT AUTOMATION FOR DATA-DRIVEN FOUNDRIES

Shakeout and casting cooling

After a pattern change, the operator has to move from the moulding 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 moulding machine.

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

The same applies to sand and casting cooling automation. Run manually, the operator has to 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 can’t 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 has to 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 mould tracking also knows where any groups of bad moulds are on the line. Conventionally, if metal testing shows a bad magnesium treatment, many perfectly good castings have to 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 moulds 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 mould 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 moulds, perhaps one with a wrongly set core. The operator can immediately mark it as bad at the moulding machine, knowing it will be safely sorted out before shakeout.

Pouring – the acme of automation

To keep pace with the high speed of vertical moulding, more foundries have adopted automated pouring along with techniques like double index pouring (pouring two moulds simultaneously). Automated pattern changes further increase efficiency, but then the bottleneck often becomes manually repositioning the pouring device after a pattern change.

Fully synchronizing pouring and moulding – 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 knowing exactly where each mould is all the time. That lets the pouring unit calculate where and when to pour the next mould and, if required, adjust the pouring device’s position to suit.

During steady production, that’s relatively simple. The mould string moves forward by the same distance after each pour ­– the mould thickness – and the pouring device stays in the same position. Compensating for any minor variation in mould thickness requires only small adjustments.

But for vertical moulding, when the pattern changes, so does mould thickness. That’s because the DISAMATIC process varies mould thickness to keep the sand-to-iron ratio constant and take pattern heights into account. That enhances quality and minimizes resource consumption but, after a pattern change, the moulding line will be producing one mould 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 moulds from the previous pattern. If the new moulds are thicker, the moulding machine will occasionally have to wait while two moulds are poured. If the new moulds are thinner, the pouring unit must also be able to skip pouring for one moulding cycle.

Seamless pouring at Ortrander

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

Ortrander found manually positioning the pouring device and pouring moulds 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 its furnaces, moulding 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 mould reaches the pouring device and automatically switches to the new pouring sequence. If the pouring device ever reaches the end of its travel, the moulding 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 pre-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 the previous three; 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 automation, the extra process information it supplies aids correct decisionmaking, so fewer experienced staff are needed. In future, the machine may make all the decisions itself.