Application of Predictive Analytics in Estimating Mechanical Properties for Investment Castings

Date
2019-09
Authors
Virdi, Jaspalsingh Karamsingh
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Publisher
Faculty of Graduate Studies and Research, University of Regina
Abstract

The investment cast parts are widely consumed in aviation, automotive, power generation, biomedical, and ship-building industries. The investment casting process is known for manufacturing complex shape parts with near-net finish. This process starts by making wax pattern similar to the shape of finished cast part. These patterns are assembled on a tree structure. A ceramic mold is formed around the tree assembly by repeatedly coating the ceramic mold with ceramic slurry and sand until the desired ceramic mold strength is obtained. The wax is then removed from the ceramic mold by heating it in the furnace. The molten metal is poured into the heated ceramic molds. Once the metal is cooled down, metal parts are extracted from the broken ceramic mold. This high-quality casting should have the required dimension, surface finish, mechanical properties, and should be defect free to provide better serviceability. Industrial quality control system measures mechanical properties through destructive testing after the manufacturing of cast components. The investment casting foundries faces a large amount of rejection or recycling that makes casting process less efficient due to wasted time, money, manpower, and raw material. Controlling the investment casting process is very difficult because it contains several sub-processes. This sub-processes leads to large number of parameters from the processing conditions and compositions. These process parameters are constantly changing from one batch to another during the production process, resulting in highly variable mechanical properties as well as finished castings. So, there is a need to develop an efficient system that can estimate the mechanical properties of investment casting parts before physical production. This thesis proposed a prediction system to ensure better quality control such that casting production meets targeted mechanical properties. Moreover, also shows research efforts in application of feature selection for finding the significant processing parameters, which affect the mechanical properties of investment casting parts. In this prediction system, several machine learning models including Multiple Linear Regression model (MLR), K-Nearest Neighbor (KNN), Random Forest (RF), and Extreme gradient boosting (Xgboost) were employed to predict individual mechanical properties (such as elongation percentage, yield strength, and ultimate tensile strength). Here, aforementioned methods are called as Data Learning Models (DLMs), they were trained with a large amount of foundry data collected for stainless steel automotive investment casting parts. Various feature selection techniques used in this proposed model include Boruta, Variable Selection Using Random Forest (VSURF), and Recursive Feature Elimination (RFE). They were employed to reduce the redundancy in the large dataset and find a best reduced dataset which can improve the prediction capability of DLMs. The performance of prediction models using the reduced datasets have been compared to the original dataset. The results showed that the reduced dataset generated from RFE maximizes the prediction accuracy. Xgboost provides the best accuracy for predicting mechanical properties when using the reduced dataset generated from RFE. The applications showed that the proposed prediction model is robust enough to handle noisy foundry data. Xgboost can easily be implemented and used by an operator for property forecasting. This data-driven machine learning models has demonstrated an effective way to optimize and control the investment casting process. The future work in this direction can lead to smart manufacturing with online monitoring and control of properties.

Description
A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Industrial Systems Engineering, University of Regina. xii, 114 p.
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