Executive Summary:
The Inventor of “SANDMAN”, Mr. Deepak Chowdhary is also the Founder Owner of MPM Private Limited, Nagpur, India’s pioneering manufacturer and Technical Application experts of Lustrous Carbon additives for use in Green sand molding.
Being a first-generation entrepreneur, he has devoted the entire 35+ years of his career in understanding the complexities of green sand molding process and its control in ferrous foundries.
The consequence of which is “SANDMAN”; the world’s first Data Analytic Software for optimisation of green sand with a view to reducing repetitive casting defects and optimizing additive consumption.
The University of Johannesburg Metal Casting Technology Station (UJ-MCTS), as part of the FOUNDRY 4.0 initiative has introduced SANDMAN in South Africa as part of their technology demonstration and transfer objectives. The team from the UJ-MCTS, Kulani Mageza and Farai Banganayi went on a series of visits to introduce the SANDMAN green sand data analytics software to green sand foundries in South Africa.
NALEDI foundry taken Thought Leadership in implementing the software, yielding them tremendous benefits. The sand related scrap dropped from a baseline figure of 5.14% to 1.02%, a drop of 80% in a period of 3 months.
NALEDI foundry General Manager, Mr. Coenie de Jaeger and process manager, Mr. Francy Matoto took a lead in introducing this transformative software and are excited about the benefits achieved from this proactive decision.
ELEMENTS OF INDUSTRY 4.0 INTEGRATION FOR FOUNDRY PROCESS OPTIMIZATION AND PROFIT MAKING:
- Real-time Data transmission using IOT integration and One click accessibility – SMC and SCADA, sensors integrated data
- Digital transformation to Foundry 4.0: Data Visualization and Analysis like BI features – Rejections dashboard, Process Control Charts, Cp, Cpk metrics
- Machine Learning & AI driven solutions for Process Optimization – Predictive and Prescriptive Modeling for rejections and consumption optimization
- SMART Monitoring of processes – Real-time monitoring using LED display with business intelligence tools and inter-active dashboards
- Automated alerts/SMS – Mobile alert and e-mail for HIP and additives predictions
The study presents the state of green sand parameters, its process control and correlation to related rejections. Analysis of the data indicates that casting rejection has reduced from 5.14% (before SANDMAN period) to 1.02% (for Feb 2019).
This Case Study clearly brings out the usefulness of SANDMAN Software. It provides a tool to understand the dynamic and continuous state of process optimization of the sand system and predicts the corrective action required to control the sand parameters. The resultant optimization would lead to reduction in rejection and improving casting outcomes, thereby saving repetitive losses and improving profitability. The achieved lower rejection can be maintained with continual use of SANDMAN.
Preamble
This case study is based on the sand parameters, casting rejections and additives consumption data provided by NALEDI Foundry
The study utilizes SANDMAN’s predictive analytics to suggest optimal values of the sand parameters with a view to reducing casting defects. The SANDMAN’s prescriptive analysis further provides dose-by-need additives recommendations to operate around the optimal condition.
What SANDMAN Does:
- Identify the key sand parameters critical to sand related casting rejection.
- Prescribe an optimal operational range for the sand parameters.
- Provide tools for monitoring sand parameters to track the effect of actions taken on the green sand system.
- Dose-by-need additives prescription for each load to achieve target optimal sand properties.
- Avoid over-dosing and under-dosing of the system resulting in optimized additive consumption.
What SANDMAN Does Not Do:
- It does not presume nor claim that it is a standalone casting rejection reduction software.
- It does not claim nor does it propose to solve rejection issues on a standalone basis. Rejections can occur due to variations in input such as raw material quality and/or changes made in the process/machinery which are not captured by the foundry either as a test or as annotations.
- It does not handle casting rejections arising from metallurgical issues, core related issues, tooling and methoding issues in the present version. Metal, Core-sand and other relevant analytics are under active development.
Rejection Data Statistics
- The weight-wise and production number-wise rejection distribution for last five month shows a reducing trend for rejections.
- The dashboard has a feature wherein comparison of rejection % can be viewed by casting number or by casting weight grouped by Days / Weeks / Months for all types of rejection categories i.e. sand/metal/core/others.
- Example: The month of February 2019 has lowest rejection % amongst the months.
- The Defect-wise distribution chart below, for the implementation period, shows Sand Drop as highest contributor.
Comparison of Sand Drop/inclusion Foundry Stage (rejections by No.)
Sand inclusions was a major defect before SANDMAN implementation (started from 22nd Oct’2018). After SANDMAN implementation Sand Drop rejection reduced by 90% in Nov’18, Dec’18, Jan’19, Feb’19 compared to Oct’18.
Savings-Additives Consumption
SANDMAN optimized additives consumption which has resulted in a saving of 338,364 ZAR. The savings were calculated by comparing the additives consumption after 22nd Oct’2018, with respect to the baseline discussed at the time of implementation.
Savings-Sand Rejection
SANDMAN helped in controlling sand related rejections which has resulted in an estimated saving of 1,385,294 ZAR. The savings were calculated by comparing with the baseline established at the time of implementation.
Summary
✓ Lower Rejection Helped foundry to control rejections. Sand Drop rejection reduced by 90% after the SANDMAN implementation (Nov’18 to Feb’19).
✓ Reduced Consumption Lower consumption observed after SANDMAN implementation.
✓ Quality Insights SANDMAN features like High Influence Diagnostics and SPC charts provides additional insights which helps foundry to monitor Sand System closely.