The aluminium foundry industry continues to face pressure to accelerate innovation, reduce costs, and maintain consistent product quality. In this context, the Research & Development (R&D) process, product testing, and new product release often become bottlenecks that consume significant time and resources. Traditional approaches—relying heavily on trial and error—are no longer competitive in today’s digital era.
Labcentric, as a digital transformation partner for industry, introduces MoltenPlanner, an AI-powered prediction solution for molten aluminium composition, designed to accelerate the entire product development cycle.
Challenges in Molten Product R&D
Developing molten aluminium products—especially for specific grades like ADC12, LM6, or AC4C—typically involves lengthy processes:
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Determining raw material mix formulas.
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Conducting melting and sampling.
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Analyzing the composition using a spectrometer (OES).
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Evaluating the results and repeating the process if needed.
This process can take hours or even days, especially when using scrap with high variability. Each trial batch means energy usage, machine time, and potentially wasted material.
The Solution: Predicting Molten Composition with AI
With a data-driven approach, the above process can be drastically optimized. Labcentric’s MoltenPlanner acts as a digital assistant that predicts molten aluminium composition before melting takes place, simply by entering the types and proportions of raw materials.
The AI model in MoltenPlanner is trained using historical production data (input materials, OES results, process parameters) to map complex relationships between material inputs and chemical outputs. This enables users to get highly accurate predictions without physically melting the material.
Key Features of MoltenPlanner
MoltenPlanner offers more than just AI predictions. It’s built for seamless production-floor integration with practical features such as:
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Automatic Composition Prediction
Estimates Si, Fe, Cu, Mg, Zn, Ti, and other elements based on entered material blends. -
Material Blend Simulation
Users can virtually try different material ratios and instantly see the impact on final composition. -
Cost Optimization Suggestions
The system recommends material combinations that meet specifications at the lowest possible cost. -
Import Certificates of Analysis (CoA)
Input CoA data directly from Excel or PDF files to ensure accurate actual values in prediction. -
Integration with Scales and Barcode Scanners
Supports digital scales and barcode readers to improve input accuracy. Scan materials and the system auto-reads actual weights. -
Integration with OES and LIMS
Predictions can be instantly compared with actual OES results, and all data is logged into the LIMS for traceability and batch evaluation. -
Lightweight, Intuitive Web Interface
Designed to be easy for both operators and engineers, with no need for internet or external servers.
Production Floor Impact
Companies implementing MoltenPlanner have reported notable benefits, such as:
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Reduction in trial batches by up to 60%
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Decision-making time reduced from 1 hour to under 5 minutes
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Improved product quality consistency
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Raw material cost efficiency through optimized scrap use
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Better documentation and traceable batch data
Who Should Use MoltenPlanner?
MoltenPlanner is designed for aluminium melting operations of all sizes, including:
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Scrap-based or primary ingot foundries
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R&D and metallurgy quality assurance divisions
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Automotive, electronics, and machinery component manufacturers
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Alloy suppliers or foundries offering custom blends
Conclusion
MoltenPlanner is more than just software—it’s a leap forward in the digitalization of molten aluminium production. By replacing trial-and-error with predictive, integrated processes, manufacturers can save time, reduce costs, and improve end-result reliability.
Now is the time for Indonesian foundries to adopt smart technologies and compete in the Industry 4.0 era. MoltenPlanner is ready to be your digital transformation partner.