Deepak Chowdhary

Deepak Chowdhary

Managing Director, MPM PVT LIMITED

Green Sand Casting - Part 1

Optimising Additives in Foundries by Using Dose-By-Need

| Author/ Editor: Deepak Chowdhary / Isabell Page

Data analysis can be an essential decision-making aid for foundries to control sand processes from estimate-based or experiential additive dosage to near accurate variable, dose-by-need additions for better casting outcomes.

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The science of data analytics is transforming industry, manufacturing, processes and services globally.
The science of data analytics is transforming industry, manufacturing, processes and services globally.
( Source: Pixabay / CC0 )

Smart Factories are on everyone's lips and are becoming more and more popular. Large automobile manufacturers such as AUDI AG have already implemented Smart Factories and are making intelligent use of data. These are essential parameters for smart factories:

  • The Integration of Data Analytics
  • Machine Learning
  • IoT by integrating Sensors
  • SCADA and Line Speed sand properties and process data
  • Near real time data based analytics for process optimization

Predictive and Prescriptive Data decision support, delivering highly accurate outcome predictions has brought a paradigm shift in Manufacturing, Sports, Aerospace, Bio-Chem, Health and Process industry. The rapid proliferation and advancement in technologies of Information Technology such as cloud computing has enabled industries to generate and store huge data. In addition, the digitalization of process automation has empowered Industries to collect and transmit near and real time data. Cloud-based computing power ensures data availability without geographical and physical limitations. By leveraging the power of data-based analytics and machine learning, industries have already started generating significant tangible and intangible profits using ever advancing data science technologies and methods.

It is a matter of concern that modern foundries with all their modern machinery and technically demanding castings processes are unfamiliar with the power of analytics; and are heavily dependent on human experience, which is rapidly shrinking, especially in the greyest of all areas in foundry practice which is arguably, Green Sand Molding. Almost 80% castings are being produced globally using Green Sand-Casting process, owing to its advantage of low operating cost, and effortless availability of low-cost raw materials. Sand is also the highest contributor to casting defects due to inherent process variables.

Left to experiential, human-interface based decisions, controlling green sand in today's modern high volume, high density and high-speed molding systems, is like relying on intuitive reactions to post facto occurrences of defects.

How Data Changes Industries

The science of data analytics is transforming industry, manufacturing, processes and services globally. BOEING as an example saves millions in fuel and maintenance cost by leveraging data analytics for their turbines: For example, in the last few years, GE started to notice that some of its jet aircraft engines were beginning to require more frequent unscheduled maintenance. “If you only look at an engine's operating parameters, it just tells you there's a problem,” but by pulling in massive amounts of data and using fleet analytics, GE was able to cluster engine data by operating environment. The company learned that the hot and harsh environments in places like the Middle East and China clogged engines, causing them to heat up and lose efficiency, thus driving the need for more maintenance. GE learned that if it washed the engines more frequently, they stayed much healthier. “We're increasing the lifetime of the engine, which now requires less maintenance, and we think we can save a customer an average of $ 7 million of jet airplane fuel annually because the engine's more efficient, [...]. And all of that was done because we could use data across every GE engine across the world and cluster fleet data.”1

Foundries unfortunately lose valuable time in denial that they are a conservative industry that needs a change in thinking. Sophisticated moulding lines are no longer enough. But what drives change? On the one hand, the generation of highly trained specialists is ageing exponentially. On the other hand, experience-based know-how is disappearing rapidly. Modern Metallurgical Curriculum do not stress on operational management or the background and "why" of processes and their inputs. They are more concerned with the "what" of outcomes or events. A consistent integration of modern data-driven analytics and decision support, machine learning and deep learning technologies to limit the human interface to only intuitive and human-intelligent control is therefore the absolute need of the hour; leaving machine-learning and AI to do the complex and heavy lifting of process consistency. Process Consistency, Repeatability and Accuracy, Sustainably and Scale-ably is the way forward for the metal casting industry. Foundries too can now take resolute steps in getting aboard the Industry 4.0 spaceship. Data Driven Green Sand Optimization is one significant process which helps foundries either make better castings or suffer crippling casting defects.

Predictive and Prescriptive Data decision support, delivering highly accurate outcome predictions has brought a paradigm shift in Manufacturing, Aerospace and many more.
Predictive and Prescriptive Data decision support, delivering highly accurate outcome predictions has brought a paradigm shift in Manufacturing, Aerospace and many more.
( Source: SANDMAN - MPM INFOSOFT PRIVATE LIMITED )

Cloud Computing for Green Sand Systems

For example, with a software product solution based on cloud computing for green sand system optimization Industry 4.0 can be implemented in foundries. This software application leverages historical data and continuously learns from current data to predict influencing sand properties which need correction to optimize to help produce better castings with lower rejections and dose-by-need, variable and optimized sand additive consumption. The software also integrates IoT using sensor and SCADA based technology to generate real time data. The Machine learning algorithm establishes a cause and effect relationship between sand parameter, casting rejections and additives consumption data; and offers optimized solutions for significantly accurate process control outcomes. On deep granular levels, the predictive modeling can provide optimal sand properties and corresponding optimal rejections corresponding to each Casting and rejection types. Furthermore, a unique and complex model framework for prescriptive analytics, predicts dose-by-need additive quantity for each batch of sand reducing over/under dosing of the system sand. This allows foundries to operate their sand system in an optimum and dynamically balanced manner which is both sustainable as well as scalable. The concept is supported with a case-study to validate the usefulness of the software in profit making by process optimization. This internationally patented methodology keeps the process repeatable, adaptable, scalable and dynamically stable for better quality castings, improved productivity, and on-time castings supply.

References

1) Laura Winig, in MIT Sloan Management Review, 2016, available at http://sloanreview.mit.edu/case-study/GE.

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About the author

Deepak Chowdhary

Deepak Chowdhary

Managing Director, MPM PVT LIMITED

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Spotlightmetal; EnginSoft; Godfrey & Wing Inc.; Pixabay; ; SANDMAN - MPM INFOSOFT PRIVATE LIMITED; Siemens; Hochschule Landshut; Daimler; Hornet; Altair Engineering GmbH; Photo by Ramón Salinero; ANDREAS STIHL AG & Co. KG; gemeinfrei; Dr. Ing. h.c. F. Porsche AG