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1. Improving Casting Porosity with Data Mining Creed Darling Marcin Kuta Kyle Saginus ENMA 6060 Innovation & Technology December 7, 2009
2. Overview 1.0 Business Understanding 2.0 Data Exploration and Description 3.0 Modeling 4.0 Evaluation 5.0 Deployment 2
3. 1.0 Business Understanding 1.1 Business Objectives 1.1.1 Background 1.1.2 Business Objectives 1.1.3 Business Success Criteria 1.2 Assess Situation 1.3 Risks and Contingencies 1.4 Costs and Benefits 1.5 Data Mining Goals 1.5.1 Data Mining Goals 1.5.2 Data Mining Success Criteria 3
4. 1.1.1 Background A casting with high gas porosity is considered defective In the process of casting, defects are put back in the “melting pot” The loss of capital from defects is mostly overhead and reduced throughput Defects have to be reduced to reduce waste and increase throughput without increasing foundry capacity 4
5. 1.1.2 Business Objectives Primary Business Objective: Reduce gas porosity in castings to Improve quality of foundry castings Increase throughput Reduce waste Secondary Business Objective: Compare our data mining results obtained in this project against previous findings. 5
6. 1.1.3 Business Success Criteria Measurable Outcomes: Lower operating cost Improve throughput without additional capital expenditures Reduce waste 6
7. 1.2 Assess Situation Inventory of Resources 3 students on team Dataset with 39 attributes and 172 samples The team has access to Weka software Requirements, Assumptions, and Restraints The project deadline is December 7, 2009 The results need to state which variables should be altered and to what degree in order to improve quality of castings, reduce cost, reduce waste, and to improve throughput without increasing foundry capacity. 7
8. 1.3 Risks & Contingencies Communication with project sponsor might be difficult. The sponsor has published articles using data mining on our dataset Limited knowledge of casting process Data is data Our tools might limit our findings We have many different algorithms that can be employed 8
9. 1.4 Costs & Benefits There is no cost to execute this project as the project sponsor provided the data There is no direct financial benefit to the team; however, the information gained can help the project sponsor to understand critical variables that affect the quality of casting in a foundry environment, which can then increase throughput for the foundry 9
10. 1.5.1 Data Mining Goals To determine the variables (of the 39 provided) that have the greatest impact on reducing gas porosity in the sand castings in order to increase the quality of the cast part. 10
11. 1.5.2 Data Mining Success Criteria Compare our data to the findings of the project sponsor Our results agree with our sponsors but add something new 11
12. 2.0 Data Exploration and Description 2.1 Data Collection and Description 2.2 Data Quality 2.3 Data Selection 2.4 Data Integration 12
13. 2.1 Data Collection and Description Project sponsor collected over 6500 points of data 39 attributes and 172 samples Attribute Types: Elements in the final casting composition Information regarding the casting process Chronological data regarding the cast Data is a mixture of numerical and nominal 13
14. 2.2 Data Quality Upon review of a number of articles addressing casting procedures, it appears the data collected by the team’s sponsor is complete and it does not contain any significant errors. Further analysis of the provided data may reveal areas of investigation that could be broadened to provide complete findings. 14
15. 2.3 Data Selection In an article published by the sponsor it was discovered that 11 of the attributes were found to be unnecessary in the model Trials were run using our data mining tools with the attributes included and removed and only a small difference in error was observed We removed the eleven variables from the dataset 15
17. 2.4 Data Integration Created two new attributes from existing attributes Total Impurities – summation of FeMnSi, FeSi, FeCaSi, and Ca Total Al, Si, P – summation of Al, Si, P No formating issues were found with the data 17
18. 3.0 Modeling 3.1 Modeling Techniques 3.2 Build Model 3.3 Test Model 3.4 Assess Model 18
19. 3.1 Modeling techniques Experiments were conducted with each of the following algorithms to discover our best model One Rule Classifier Naïve Bayes Classifier J48 Decision Tree Classifier Multilayer Perception Neural Network Training sets were used with some discretization The dataset with the 11 attributes removed and integrated dataset were used for all of the experiments 19
20. 3.2 Build Model The experiments using the J48 Decision Tree classifier resulted in the best model for our dataset This algorithm was chosen to be used for analyzing our dataset The following slides show the details of building the model 20
24. 3.4 Assess Model 24 From the Weka output we can see that the model only misclassified 2.33% of the instances The classification results in the Confusion Matrix
25. 4.O Evaluation 4.1 Evaluate Results 4.2 Review Process 4.3 Next Steps 25
26. 4.1 Evaluate Results From the J48 Decision Tree we can see the attribute that has the largest impact on the porosity is the “molding team number” This indicates that the process of casting is very dependent on the workers This is also not a surprise and was already recognized by the sponsor The molding team attribute was removed from the dataset to see which attribute was the next most important 26
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29. 4.2 Review Process Process seems to have followed all of the correct steps and has yielded acceptable results Results could be improved or new findings made by using a different software package with different algorithms 30
30. 4.3 Next Steps For continuous monitoring and improvement of the casting porosity while limiting excess data acquisition, it is suggested that data pertaining to casting failures should only be collected on the attributes appearing in the J48 Decision Tree 31
31. 5.0 Deployment Plan The completed model and results met our business objectives Send our final report and model to sponsor for Review Monitoring and maintenance 32
Welcome to our presentation on improving casting porosity with data mining. This presentation was completed for Marquette University’s Engineering Management course on Innovation and Technology by Creed Darling, Marcin Kuta, and Kyle Saginus.
Here is a quick overview of the presentation. We will start by first discussing our business understanding of what we planned to accomplish with this project. Then we will discuss the different aspects of the data and the model we developed to analyze it. We will conclude the presentation by evaluating our model in terms of accomplishing our business goals and the deployment of our model.
For the business understanding section of this presentation we will give a short background along with our objectives and success criteria. We will then present our team’s situation including our requirements and resources followed by the risks and contingency plans for the project. Finally we will talk about the costs and benefits of the project and how we planned to accomplish our business objectives through data mining.
Metal castings are used throughout several industries, varying anywhere from the construction industry to the aerospace industry. Depending upon the final application for the casting, overall quality is essential. Among other factors, castings can be considered defective when a certain level of gas porosity is present in the final product. A defective part is typically returned to the melting pot and recast. This might lead you to believe that defective castings don’t have a significant cost associated with them but a casting’s cost is usually about 10% material and 90% overhead, so a defective casting does have a major cost. Also each casting that is considered defective is a product that went through the entire process but did not make it out the door, so the foundry’s throughput is reduced. Reducing defective parts will increase a foundry’s throughput without spending more capital to increase capacity.
Based on the background our business objectives are simple. Our goal was to reduce the gas porosity in castings to improve the quality of castings, increase the throughput of the foundry, and reduce waste. Our project sponsor has already collected data and analyzed it using data mining techniques. It might seem then that the project is already complete but there are many different approaches in data mining and a new team can always add new insight. Our secondary business objective was to compare our findings to our sponsors and hopefully find similarities to ensure our approach is correct, but also to bring something new to the table.
For our business success criteria, based on our business objectives, we are focusing on the following measurable outcomes. By reducing the number of defects, our project will be successful if there is a lower operating cost, improved throughput in the absence of increased capacity, and reduced waste.
The resources available to the team are three students in the innovation and technology course, the dataset collected by the sponsor, and access to Weka data mining freeware.We were required to complete the project by December 7th (today),and the results found needed to meet the business objectives. Since we are not directly linked to the foundry we needed to make recommendations to our project sponsor as to what attributes in the dataset we were given have the largest impact on high casting porosity. The project sponsor will then employ the recommendations.
There are a couple risks associated with the project that were addressed at the beginning of the project and contingency plans were developed. Since our sponsor is from a foreign country it was assumed that it would be difficult to communicate with our sponsor, however it was thought shouldn’t inhibit our work as the sponsor had already published numerous articles on this data. Our team had a limited knowledge of the casting process, but in one sense it is not necessary to understand the process to analyze the data. Also when confirming if our recommendations were accurate the team had the sponsors findings to see if the analysis was headed in the right direction. The software package that we had available to us may not have the best model for this data, but there are enough algorithms in the software that the team felt it would able to find a model that is sufficient.
The team will not be compensated in any way for completing the project so there is no cost involved for our analysis, but there is also no direct benefit to the team aside from gaining experience in data mining. The foundry will however receive a benefit from our project by possibly improving the quality of their castings, and identifying areas of process improvement that increase the throughput of the foundry and reduce operating cost.
The teams data mining goal was to find the attribute(s) in the dataset that have the largest impact on high casting porosity resulting defective parts. With this knowledge the casting process can be altered to reduce the number of defective castings.
The team’s data mining success criteria depend mostly on the sponsor’s findings. We will know that we have found a good model when our results show findings similar to our sponsor’s. We want to add something to our sponsor’s knowledge of the data, so we will also consider the data mining to be successful when we have found something new.
Following the Business Understanding section, this segment of the presentation will focus on Data Exploration and Description. Here we will explore Data Collection and Description, Data Quality, Data Selection and Data Integration.
The team was provided with over 6500 points of data which were collected by the project sponsor. The data set consisted of 39 attributes derived from 172 samples. The attribute types provided insight into the physical nature of the final product by listing elements in the final composition of the casting. Other attributes provided valuable information into the casting process technology by listing information regarding the casting process. The last set of attributes characterized the casting process itself by listing chronological data regarding the cast. The data points were a mixture of numerical and nominal values.
Since the data points were provided by the project sponsor, data evaluation step was performed by the team in order to ensure adequate data quality. The team’s focus was placed on thorough review of published literature related to the topic of gas porosity in a foundry process. The article review supported the sponsor’s data definition and collection methods. Furthermore, it lead the team to believe that further analysis of the provided data may reveal areas of investigation that could be broadened to provide complete findings.
In parallel to data quality, data selection is a critical step in the data mining analysis. In one article published by the project sponsor it was discovered that 11 of the 39 attributes listed were found to provide no insight or value to the model. In order to ensure successful data selection progression, trials were run using various data mining tools and models with all attributes included and removed when a small difference in error was observed. In conclusion 11 attributes were removed from the data set that showed no influence on gas porosity in the final casting.
Out of the 39 attributes listed in this section, the following 11 attributes showed no influence on the model – those were labels as “Iron and Silicon Amount,” “Final %Al,” “Final %P”, “Nozzle Supplier Code,” “Pouring Order,” “Mould Quality,” “Core Coating Code,” “Molding Sand Code,” “Molding Coating Code,” “Environment Temperature Before Pour” and “Bar Test Casting Porosity”
The data integration and data review lead to the development of two new attributes from the data set. The two attributes were “Total Impurities consisted of summation of FeMnSi, FeSi, FeCaSi, Ca levels” and “Total of Al, Si,and P levels which consisted of summation of Al, Si and P.” No formatting issues were found with the data.
The next step within the project was to develop proper model which enabled the team to analyze the data points and derive appropriate conclusions related to the gas porosity issues found in the casting process. This section of the presentation will focus on Modeling Techniques, Building a Model, Testing a Model and Assessing of the Model.
In an effort to define and build the best model, the team has evaluated various algorithms found in Weka software. A number of simulations were conducted using One Rule Classifier, Naïve Bayes Classifier, J48 Decision Tree Classifier and Multilayer Perception Neural Network algorithm. In order to generate adequate results, the training sets were used with some level of discretization. The data set with the 11 attributes removed and the integrated data set were used for all of the experiments.
The simulation efforts using a number of different algorithms resulted in selection of J48 Decision Tree classifier as appropriate model for this project. The J48 algorithm generated best results and yielded the lowest classification error.
This is the J48 model set up. The model was generated using the software defaults with an exception of “saveinstancedata.” This feature was changed to true. This allowed the team to find out how each sample is classified after the building the J48 classification tree.
This table lists the number of misclassified instances. The original data set with 11 attributes removed showed the same results as the integrated data set. We can see that non discretized sets generated only 4 misclassified attributes, whereas the discretized data sets yielded as many as 13 misclassified instances.
This is the J48 classification tree generated by the model using the non-discretized, original dataset. We can see that the tree has 9 leaves, and that the total size of the tree is 23 elements. The size of the tree is important when testing the model. In general, the goal of the tree learner is to classify the most test samples correctly while reducing the tree size and the number of leaves. Considering the number of attributes in this data set, the model had done a good job in generating only 9 leaves and 23 elements.
The final step within the modeling phase consisted of model assessment. From the Weka output shown, it was observed that the model only misclassified 2.33% of the instances This value was derived from misclassification of 4 out of 172 samples available.
