- Assured metallurgical quality
- Add recycled materials safely
- Get the best cost/quality ratio
Metallic charges, often coming from recycled materials, have increasingly higher contents of micro-alloying elements that impact the metallurgy and structure of castings. An illustrative example can be the growing concentration of minor elements, such as titanium, aluminium and Zinc from steel coatings, which, even if their final concentration in the melt is too low to affect the structure, the morphology of graphite or the precipitation of carbonitrides, can promote the apparition of pinholes. The effect of these elements is added and can generate reactions with the humidity of the mold, generating gaseous evolutions that cause defects in the surface or subcutaneous layers of the cast parts.
The design of the optimal metal fillers from the stock of raw materials available in the plant in real time and from the target metal quality is key to ensuring the quality of the components and improving the efficiency and costs of production.
Foundry Recipe Optimizer, developed by AZTERLAN, combines Digital Twins of the process and Artificial Intelligence to offer foundries a solution to design the perfect casting based on two key criteria:
- A metallurgical quality sufficient to manufacture without defects
- Manufacturing without defects at the lowest possible cost
Optimize your metal, your melt area and your raw materials management
- Assure the quality of your components
- Assure the the reliability and efficiency of the manufacturing process
- Reduces rejection due to metallurgical causes
- Minimizes the cost of raw materials and alloys needed to produce the base metal
- Achieve significant savings in the consumption of loading materials (ingot, scrap, etc.) and alloy materials
- Optimizes the overall yield of the process
- Reduce your energy consumption
- Improves the overall sustainability of the casting process
This is how FR Optimizer works
In real time, FR optimizer is fed with this data from different areas of the process:
- Stock of materials and consumables
- Manufacturing orders
- Limits and controls associated with the manufacturing process
- Composition and other characteristics (origin, price…) of raw materials
- Quality controls of manufactured products
- Other variables of the manufacturing process (ovens, treatments, logistics, etc.)
Predictive Control Models or PCMs are advanced control systems that, based on mathematical models, allow predicting future behavior or the result of processes involving several interconnected parameters.
Applied to the digital twins of the process fed in real time, PCMs allow identifying actions that lead to the desired result.
The adaptive capacity of Artificial Intelligence applied to the predictive capacity of MPCs allows the identification of possible combinations of variables to achieve predefined target results.
In addition to the metallurgical quality and cost values, these combinations can be adjusted or weighted with respect to other values or parameters to obtain the optimal combination in terms of time, environmental factors, production capacity, availability of raw materials, etc.
“The Foundry of the Future is that of connected processes that is able to take advantage of the knowledge generated inside the company at the same time that it keeps learning from its manufacturing and market experience.
Along with a deep knowledge of metallurgy, Industry 4.0 technologies, advanced data management systems and machine learning technologies have landed in this industry to transform the way castings are conceived”.
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