DataProphet's AI-as-a-Service manufacturing solutions have dramatically reduced defect and scrap rates, increased rolled throughput yield, and optimized process maintenance at foundries around the world.
Your foundry’s production and quality data hold the secrets to process consistency and stability. DataProphet PRESCRIBE translates this data and inherent process knowledge into continuous prescriptions to bring your foundry within or very near to its optimum operating region.
DataProphet’s Expert Execution System enhances energy efficiency, increases yield, and reduces emissions and waste to support sustainable manufacturing strategies. We guarantee rapid optimization of any foundry, immediate impact, and return on investment within a year.
Improved availability, performance, and production quality of your foundry machinery results in optimized rolled throughput yield, thus avoiding rework.
Stabilize and optimize processes to improve the energy consumption and environmental footprint of your iron foundry.
Optimize process parameters across furnaces, alloy tolerances, sand molding, or die casting to eliminate scrap.
The application of advanced - supervised and unsupervised - machine learning methods has a demonstrated impact on critical foundry processes, including low pressure die casting (LPDC); green sand casting; melting lines, and sand plants - plus iron, steel, and aluminum operations. Our Expert Execution Systems empower each operator, engineer, and plant manager to follow deep learning prescriptions for proactively modifying the parameters most likely to maintain the optimal global performance of the foundry - ahead-of-time.
You do not need a mature or robust data environment, data experts, or expensive technology to get immediate value from prescriptive analytics. DataProphet understands the complex interactions between foundry sub-processes and prescribes optimal setpoints, process parameters, and tolerances for each casting, process, or outcome.
DataProphet PRESCRIBE ingests historical data and continuously learns from current data to predict parameter corrections and optimize molding process outcomes, with fewer defects, less variability, and optimized additive consumption.
Even though single sub-processes like molding or pouring are within tolerance levels, there is still a risk of defects occurring. That is because single processes do not usually cause defects. Rather, it is a suboptimal combination of specific parameters that result in scrap. DataProphet PRESCRIBE learns how parameters influence each other and prescribes the optimal machine settings for minimum defects and high yield.
Defective units incur high shipping and environmental costs in foundries. Using DataProphet PRESCRIBE, Atlantis Foundries optimized process control parameters to achieve zero external engine block defects for the first time in the company’s history. It also reduced waste; rework; internal scrap; energy consumption, and carbon emissions, while shaving 2% off total business costs – without any process changes.
Some clients have optimized the energy efficiency of their foundries and reduced scrap to near-zero using the process parameters suggested by DataProphet PRESCRIBE.
Explore how AI and machine learning are transforming manufacturing through the reduction of defects, rework, and scrap.
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Our partners know their industry. DataProphet’s years of experience with the processes driving production in foundries qualify us to adapt our AI-driven solutions to continually benchmark your plant and product metrics. Partnering with DataProphet means scaling competitive advantage.
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