2nd Australian International Conference on Industrial Engineering and Operations Management

Reducing Molding Sand Variation at a Job Shop Casting Facility

Kuldeep Agarwal & Ashley Folden-Ecker
Publisher: IEOM Society International
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Track: Lean Six Sigma
Abstract

A job shop casting facility in Southern Minnesota utilizes the green sand molding process in the production of ductile iron castings. The foundry’s green sand is produced by mixing recycled molding sand with water, clay, seacoal, and other ingredients in a muller. Maintaining consistent sand properties through the molding process is critical to the production of quality iron castings. One of the primary measured and controlled characteristics of which indicates molding sand quality is compactability. Compactability is measured in-line automatically on every batch of sand; the results are used as the sole factor to determine water additions for the subsequent batch of sand. While this formula frequently produces sand that is sufficient for molding, various factors exist outside of this calculation that can affect the outcome of the next sand batch. These factors are currently not accounted for in the calculation of water additions and as such, some amount of inconsistency is generated by the current process. Inconsistent molding sand may result in the production of scrap castings as well as downtime at the molding machines. This project utilizes the DMAIC improvement process to reduce variation in molding sand by measuring, analyzing, and improving upon the parameters used in the green sand control process. The desired outcome of the project is to produce consistently stable sand by predicting and controlling water addition rate (therefore resulting compactability) based on the measured inputs. Reducing batch-to-batch and overall variation in molding sand will lead to a reduction in sand-related scrap and downtime.

Published in: 2nd Australian International Conference on Industrial Engineering and Operations Management, Melbourne, Australia

Publisher: IEOM Society International
Date of Conference: November 14-16, 2023

ISBN: 979-8-3507-1732-7
ISSN/E-ISSN: 2169-8767