Dr. Roshan Process Optimization Paper

7/25/2019 Dr. Roshan Process Optimization Paper

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PROCESS OPTIMIZATION AS A TOOL IN THE ANALYSIS

OF STEEL CASTING DEFECTS

ByDr.Hathibelagal Roshan

Chief MetallurgistMaynard Steel Casting Company

Milwaukee, WI 53217


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PROCESS OPTIMIZATION AS A TOOL IN THE ANALYSISOF STEEL CASTING DEFECTS

Abstract:

Several technical papers are available on casting defects in the literature. However, quality costsin steel 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 thecustomer acceptance specifications, it is not only necessary to identify the process parametersrelated to the specific defects, but also it is necessary to identify the levels of these parameters toproduce acceptable castings. Metal casting process has several sub-processes, which in turnhave 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 thefoundries to identify the process variables and their levels responsible for the defects. The use ofa process optimization tool which uses foundry production data that can be collected on thecastings on a regular basis in the identification of the process variables and their levels will bepresented and discussed in this paper. This is useful in converting production data intoactionable information that leads to minimization of quality costs in steel foundries.

Introduction:

Process optimization is the identification and control of the input process parameters (Factors) toachieve the desired output (Response) in any process. Metal casting process is a complexprocess with several sub-processes, such as patternmaking, mold and coremaking, melting andpouring, heat treatment and cleaning and finishing. Six Sigma methodologies have beenattempted in steel foundries to minimize the casting defects and improve profitability. Six Sigmauses DMAIC methodology to improve the processes. Six sigma heavily focuses on statisticalanalysis as it is data driven and is a methodical approach that drives the process improvementsthrough statistical measurements and analyses. In view of the large number of factors that areresponsible for the casting defects, the general statistical approach is not always the best. Analternate and more elegant pattern recognition approach is found to be appropriate for the metalcasting related issues. It is suggested that the foundries follow the Six Sigma methodology withthe exception that a pattern recognition process optimizer is used instead of the conventionalDesign of Experiments.

DMAIC Methodology:

Figure 1 shows the process map of the casting process. It is a SIPOC diagram. On the left handside, all the inputs which are Xs are presented and on the right hand side all the outputs arepresented which are Ys. These Ys include the casting defects.

Casting defects which result in rework and scrap are a major issue in steel foundries. It isestimated that about 5 to 10% of the revenues of the foundries are lost in internal and externalfailure costs. For a 100 million dollar company a reduction of even 1% of these costs will result ina savings of 1 million dollars per year.

In the Define phase of Six Sigma methodology, a project charter is made with specificmeasurable goals. This is the critical phase of the methodology where the appropriate goal isspecified based on the resources available to successfully complete the project. There will not beany change in the Define phase in the present suggested route. The project goal in the projectcharter specifies the big Y that will be optimized.

Figure 2 shows an example of the CTQ flow diagram. This is commonly used to identify the Yswhich are specified in the project charter.


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In the Measure phase a tool known as Cause and Effect diagram is used to identify all thefactors that are responsible for the casting defects. An example of the Cause and Effect diagramis shown in figure 3. Here the effect Inclusions is the Y and all the causes are Xs.

Essentially what matters is the recognition of all the factors that are responsible or related to theappropriate casting defects. This can be represented in the form of a relation:Y is a function of various Xs. This is shown in figure 4 as Y = F(X)

Cause and Effect matrix shown in figure 5 is another useful tool in identifying all the relevant Xsrelated to the Y of interest in the project. As can be seen from this diagram there are nearly 25Xs that are related to a single Y, namely Inclusions.

Figure 6 shows an example of Failure Mode and Effects Analysis diagram which filters thepotential Xs into critical Xs.

In the conventional Six Sigma approach all the potential Xs are filtered into critical Xs as shown infigure 7 in the various phases of the DMAIC methodology.

In the Improve phase of DMAIC methodology, Design of Experiments are extensively used toidentify the factors and their levels that are related to the response variable namely the castingdefect.

Limitations of DOE as applied to foundry processes.

1. In the DOE, the number of experiments needed depends on the number of factors. In the2K design, each factor is varied over two levels and the number of experiments neededare 2

Kwhere 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 numberof experiments needed increase exponentially. As can be seen in figure 1, where thereare a large number of factors, it becomes very cumbersome to carry out design ofexperiments. Even if fractional factorial designs or Taguchi design of experiments areused , the number of experiments needed become very large.

2. In the DOE, there is a need to carry out controlled experiments to collect the requireddata and it could interrupt regular production.

3. In the DOE, the level of factors have to be with a considerable difference in order to havemeaningful results.

4. The DOE assumes known distributions to the unknown foundry processes, as such theresults could be biased.

5. The DOE requires people with a reasonable expertise in the use to statistical techniquesto design the experiments and interpret the results.

Y IS A FUNCTION OF X:

In the analysis of casting defects, the casting process is optimized to minimize the defects. Theconcept of Y = F (X) is used in the analysis.Ys: Responses :These are the manifestation of the process. These are measures of the output of the process.

Casting Defects are appropriate responses which need to be minimized in the optimization ofcasting processExamples: Inclusions, Misrun, Shrinkage, Cracks etcXs : Factors :Process variables which have an effect on YThese are the parameters which control the output of a processExamples: Pouring Temp, Pouring Time, Compactability, Gating Ratio, Operator, Shift, Designchanges


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PROCESS OPTIMIZATION:It is the identification and control of the input process Parameters (Factors) to achieve the desiredoutput (Response) in any process

PROCESS OPTIZATION SOFTWARE (MetaCause)

MetaCause Process optimization software (www.metacause.com) is a pattern recognition

optimization software based on three powerful concepts (1)namely:1. Receiver Operator Characteristics (ROC)2. Relationship Hyper-Surface3. Interactions and Impossible Probability

In general, the relationships between the factors and responses can be representedschematically as shown in figure 8. The various factors and responses are inter-related as shownin this diagram with connecting lines.

The process optimisation software recognizes the patterns and identifies their significance asshown in figure 9. The thick line represents a positive effect of the factor on the responsevariable. The dotted line represents a negative effect of the factor on the response variable andthe dotted line with dots indicates that the factor has no effect on the response variable. The

thickness of these lines indicates the weightage of the factor over the response variable.

Figure 10 shows a typical user friendly output of the process optimisation software to interpret theresults of the analysis.

ADVANTAGES OF PROCESS OPTIMIZATION SOFTWARE:

1. It uses the actual production data that is collected during the operation of the process. It usesall the factors and all the responses, rather than the filtered factors in conventional statisticaltechniques.2. It can handle up to 50 factors and 25 responses at a time which is not practical with theexisting statistical tools.3. The results are based on sound scientific principles based on scientific concepts of Receiver

Operator Characteristic, Relationship Hyper-Surface and Interactions and Impossible Probability.

CASE STUDY:

Failure of test results for mechanical properties is one of the issues in steel foundries. An attemptis made to determine the relationship between the chemistry of the heats to the mechanicalproperties of the test blocks. The data collected is shown in figure 11. The various responsesidentified in this analysis include, UTS, YS, %El, %RA, Charpys at 40F, Charpys at 70F,Fracture at surface and Fracture at the center. The various factors considered include, thechemistry of the heat with elements: C, Mn, Si, S, P, Ni, Cr, Mo, Al, Cu, Ca, Ti and also Hardnessof the test block in BHN. 20 observations were made in this case study relating the variousfactors to the responses.

The results of the analysis are shown in figures 12 to 14. Figure 12 shows the importanceweighting interpretation. 100% refers to theoretical maximum, 65% and above indicates veryimportant process setting. Generally this is easily recognizable by the experts by viewing thedata itself.55% refers to highly influential setting and 50% refers to moderately important setting.The ranges between 50 and 55% is difficult to be identified by the expert by viewing the rawdata.45% is the basic threshold for the important setting and 40% refers to noisy data and has norelation to the response variable, by itself but may be significant as interaction variable withanother factor which needs to be examined further. This figure also shows the various names ofresponse variables, namely the mechanical properties including the charpy impact properties.Figure 13 shows the optimal process setting for the response variable Charpy value CVN1.This slide has the results of the analysis showing the importance weighting of various


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factors on the response variable CVN1. The software also gives the importance weight of thefactors for all the response variables chosen in the analysis. This result shows that low settingsof Mo and C have an importance weighting of over 50% and is significant to control to achieve theoptimum results. The other factors are not as significant compared to Mo and C.

Figure 14 shows the process settings to avoid. This slide indicates that low setting of Al and highsetting of C should be avoided to achieve the optimum results. The other factors listed in thisslide are not significant.

Figure 15 shows the factors that have no significant effect on the response variable. The processoptimization software gives three types of results, namely: the factors with optimal processsetting, the factors with process settings to avoid and the factors that have no significant effect onthe response variable of interest.

Using the above information the factors and the their settings are selected to achieve theoptimum performance of the response variables.

Recommended Steps in Casting Defect Analysis:

1. Use Six Sigma DMAIC Methodology.2. 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 afterthe project is successfully completed. It is desirable not select project of the type Boil theOcean and with a large time frame. It is desirable to identify projects that have a timeframe of 6 weeks. It is desirable keep the project goal to one defect type or one part withall possible defects. This will be the big Y for the project. Pareto Charts with differentlevels are extensively used to identify the relevant projects.

3. 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 desirabledevelop methods to quantify the defect. Use Cause and Effect Diagram to identify all thefactors Xs that can be related to the response variable Y. The Xs should have a metricthat is measurable. If necessary, Cause and Effect matrix can be used to filter a few ofthe factors.

4. 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.5. In the Improve phase validate the results based on the results obtained in the Analyze

phase and calculate the financial savings.6. In the Control phase, develop a control plan and reaction plan and give to the process

owner.

Summary:

Metal casting process is a complex process with several sub-processes. Six Sigmamethodologies are commonly used to optimize the process and minimize casting defects.However, the conventional statistical tools available today are not adequate to be effective inanalyzing the casting defects and optimize the processes to minimize the impact on cost ofquality. The reason for these include: the statistical techniques assume known distributions to theunknown foundry processes; the need for specially designed experiments; the need for carryingout a very large number of experiments in view of the large number of factors; the need to carryout specially designed experiments on a limited number of castings and the need to filter thepotential factors into a manageable number of factors. A process optimization software based onpattern recognition is found to be suitable to optimize foundry processes and to minimize thecasting defects. A six sigma methodology is presented to address the issue of casting defects insteel foundries with the exception that a pattern recognition process optimization software is usedinstead of DOE techniques.


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