A Pattern Allowance Advisor Tool for Steel Castings
Open Access
- Author:
- Deo, Mandar Vighnahari
- Graduate Program:
- Industrial Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- May 12, 2005
- Committee Members:
- Robert Carl Voigt, Committee Chair/Co-Chair
Paul H Cohen, Committee Member
Timothy William Simpson, Committee Member
Irene Johnston Petrick, Committee Member - Keywords:
- pattern dimension prediction
pattern allowance prediction
STeel casting shrink rules - Abstract:
- Steel casting features can be expected to have different size than the pattern features from which they were made. This difference between the casting dimension and the pattern dimension is termed as “pattern allowance”. Currently, no pattern allowance estimation techniques exist. Lack of pattern allowance estimation techniques results in longer lead times, expensive reengineering cycles, and lost market share. Due to inability of the foundries to predict the pattern dimension correctly and casting dimensional variability, castings are designed with extra stock (machining allowance), which is subsequently removed by machining. However, due to increasing machining costs, more and more foundry customers are demanding smaller machining allowance. Casting and pattern dimensional data is collected from production steel foundries along with other process and geometry variables. Statistical analysis is conducted to ensure data integrity through gage repeatability and reproducibility studies and outlier analyses. Dimensional data is then classified according to alloy type, restraint type, location of parting line, and molding method. A new empirical model has been developed to predict pattern allowances for steel castings depending on the abovementioned variables. This model predicts average, minimum, and maximum pattern dimensions based on median, 10th percentile and 90th percentile values of an artificial variable called “Shrinkage Error”. This model is then validated by collecting more pattern allowance data and comparing the predicted pattern dimensions. A software tool called “Pattern Allowance Advisor” has been developed for the implementation of the pattern dimension prediction model. In addition to helping the pattern designers in predicting the pattern dimensions, this software is also designed to serve as a comprehensive casting dimensional data management tool. Furthermore, the software also offers capability of customizing and calibrating the pattern dimension prediction model for a specific foundry through learning from the inspection data. Pattern Allowance Advisor software is acquired by several steel foundries. Application of the new empirical model through Pattern Allowance Advisor has resulted in an increase in first article casting dimensional conformance from 52% to 81% and a reduction in dimensional reengineering cycles from 3.5 to 1.1 during testing in production steel foundries. Overall reduction in total centering errors is also achieved. These improvements have resulted in reduction in lead time and dimensional reengineering costs.