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Research Papers

Foundry Data Analytics to Identify Critical Parameters Affecting Quality of Investment Castings

[+] Author and Article Information
Amit Sata

Professor
Mechanical Engineering Department,
Faculty of Engineering,
Marwadi Education Foundation
Group of Institutes,
Rajkot 360003, Gujarat, India

B. Ravi

Institute Chair Professor
Mechanical Engineering Department,
Indian Institute of Technology Bombay,
Mumbai 400076, Maharashtra, India

Manuscript received March 20, 2018; final manuscript received August 22, 2018; published online November 19, 2018. Assoc. Editor: Siu-Kui Au.

ASME J. Risk Uncertainty Part B 5(1), 011010 (Nov 19, 2018) (7 pages) Paper No: RISK-18-1014; doi: 10.1115/1.4041296 History: Received March 20, 2018; Revised August 22, 2018

Investment castings are used in industrial sectors including automobile, aerospace, chemical, biomedical, and other critical applications; they are required to be of significant quality (free of defects and possess the desired range of mechanical properties). In practice, this is a big challenge, since there are large number of parameters related to process and alloy composition are involved in process. Also, their values change for every casting, and their effect on quality is not very well understood. It is, however, difficult to identify the most critical parameters and their specific values influencing the quality of investment castings. This is achieved in the present work by employing foundry data analytics based on Bayesian inference to compute the values of posterior probability for each input parameter. Computation of posterior probability for each parameter in turn involves computation of local probability (LP), prior odd, conditional probability (CP), joint probability (JP), prior odd, likelihood ratio (LR) as well as posterior odd. Computed value of posterior probability helps (parameters are considered to be critical if the value of posterior probability is high) in identifying critical parameter and their specific range of values affecting quality of investment castings. This is demonstrated on real-life data collected from an industrial foundry. Controlling the identified parameters within the specific range of values resulted in improved quality. Unlike computer simulation, artificial neural networks (ANNs), and statistical methods explored by earlier researchers, the proposed approach is easy to implement in industry for controlling and optimizing the parameters to achieve castings that are defect free as well as in desired range of mechanical properties. The current work also shows the way forward for building similar systems for other casting and manufacturing processes.

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Figures

Grahic Jump Location
Fig. 2

Industrial casting and sample test bar as per ASTM A370 guidelines

Grahic Jump Location
Fig. 3

Computation of posterior probability

Tables

Errata

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