What to expect from the foundry of the future?

13/11/2021
New information technologies have been impacting and re-forming every sectors in recent years. Foundry sector, of course, is not an exception.
CNC milling machine
CNC milling machine

Internet of Things in forging new foundry

One of the best known terms in smart manufacturing is Industrial Internet of Things: the application of tools from the consumer world, the Internet, for industrial purposes, such as the connection of sensorized machinery. Many plant manufacturers now provide these solutions already perfectly integrated, also allowing them to benefit from tax breaks. One of the most complicated challenges is managing the resulting complexity, building processes and integrated flows to feed a single and transversal database. Thanks to this immense Data Lake, it is possible to manage the entire corporate production world from an overall view, making decisions based on organic data analysis and synthetic KPIs. The collection of large amounts of data is also an enabling factor for Artificial Intelligence algorithms, which for some years have no longer been just a fascinating research topic but a concrete opportunity for the industrial ecosystem, especially through the branch of machine learning, the so-called Machine Learning.

Machine Learning

Metal casting simulations have so far had to rely entirely on mesh-based numerical methods such as finite element or finite-difference. Computer simulations based on these methods have helped to improve the quality of castings to the extent that nowadays, using casting simulation software is an everyday practice for casting engineers. Despite the positive impact that those simulations have had, simulating realistic solidification models with a spatial resolution that is high enough to fully resolve the physical phenomena incorporated in the simulated model is still computationally intractable. Therefore, engineers are left with no choice but to rely on models that make oversimplifying assumptions. 

An application of Machine Learning to overcome this challenge is Predective Quality Analytics: the link between process parameters and product quality is deepened in order to anticipate the onset of problems or, in the extreme case, waste. The tool is a predictive model that acts during a continuous process, such as a steel or cast iron casting, providing indications about the quality of the product during its production, monitoring the values ​​provided by the sensors on the 4.0 foundry plants. The advantage is the anticipation of the qualitative feedback that is generally obtained only downstream of the process, in the testing and certification phase of the products before sale.
 
 

The value of integration into the 4.0 foundry

In an increasingly competitive market where in order to slightly raise one's quality standards it is necessary to produce great effort, the ability to apply advanced technologies to the multitude of data collected allows you to find new ways to achieve the goal, and place yourself in positions of advantage to customer's eyes.

It is crucial that companies seize the opportunities provided by digitalization, undertaking transformation actions with a global vision, relying on partners capable of "seeing" throughout the production process and helping the transition to smart manufacturing. Continuing to limit oneself to single specific interventions, on a single plant rather than on a single process, can generate a local benefit but certainly does not capture the disruptive change favored by global digital interconnection.

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