Several technical papers are available on casting defects in the literature. However, quality costs in foundries are still mainly due to the rework and scrap related to casting anomalies, which are not acceptable to customers. In order to minimize the castings which do not meet the customer acceptance specifications, it is not only necessary to identify the process parameters related to the specific defects, but also it is necessary to identify the levels of these parameters to produce acceptable castings. Metal casting process has several sub-processes, which in turn have a number of process variables, which could influence the occurrence of defects in castings. Conventional statistical techniques with design of experiments involves too much work for the foundries to identify the process variables and their levels responsible for the defects.
Limitations of DOE as applied to foundry processes:
- In the DOE, the number of experiments needed depends on the number of factors. In the 2K design, each factor is varied over two levels and the number of experiments needed are 2K where K is the number of factors. For 3 factors, 8 experiments are needed and for 4 factors, 16 experiments are needed. As the number of factors increase, the number of experiments needed increase exponentially. As can be seen in figure 1, where there are a large number of factors, it becomes very cumbersome to carry out design of experiments. Even if fractional factorial designs or Taguchi design of experiments are used , the number of experiments needed become very large.
- In the DOE, there is a need to carry out controlled experiments to collect the required data and it could interrupt regular production.
- In the DOE, the level of factors have to be with a considerable difference in order to have meaningful results.
- The DOE assumes known distributions to the unknown foundry processes, as such the results could be biased.
- The DOE requires people with a reasonable expertise in the use to statistical techniques to design the experiments and interpret the results.
Recommended Steps in Casting Defect Analysis:
- Use Six Sigma DMAIC Methodology.
- In the Define phase, create a Project Charter with clearly idenfied measurable goal. It is desirable to indicate a financial saving of at least $3000.00 per month continuously after the project is successfully completed. It is desirable not select project of the type ”Boil the Ocean” and with a large time frame. It is desirable to identify projects that have a time frame of 6 weeks. It is desirable keep the project goal to one defect type or one part with all possible defects. This will be the big Y for the project. Pareto Charts with different levels are extensively used to identify the relevant projects.
- In the Measure phase, benchmark the status of the big Y before the project commences. Quantification of the defect is an important step in the measure phase. It is desirable develop methods to quantify the defect. Use Cause and Effect Diagram to identify all the factors Xs that can be related to the response variable Y. The Xs should have a metric that is measurable. If necessary, Cause and Effect matrix can be used to filter a few of the factors.
- In the Analyze phase use MetaCause process optimization software to identify the factors with optimal settings, with negative effect and with no effect on the Y.
- In the Improve phase validate the results based on the results obtained in the Analyze phase and calculate the financial savings.
- In the Control phase, develop a control plan and reaction plan and give to the process owner.
The examples of template to collect data relating to six sigma projects are given in the file: datasheets-mar2009.xls. If you send me your data in a format suitable to your project, I shall be glad to analyze your data and send you the results free of cost. I shall also assist you in formulating your initial phases of Define and Measure phases in designing the Project Charter, Cause and Effect Matrix, and FMEA.
shirin@execpc.com
3-1-2009