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research-article

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

[+] Author and Article Information
Amit Sata

Professor, Mechanical Engineering Department, Marwadi Education Foundation Group of Institutes, Faculty of Engineering, Rajkot, Gujarat, India
ameet_sata2000@yahoo.com

B. Ravi

Institute Chair Professor, Mechanical Engineering Department, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
prof.b.ravi@gmail.com

1Corresponding author.

ASME doi:10.1115/1.4041296 History: Received March 20, 2018; Revised August 22, 2018

Abstract

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, prior odd, conditional probability, joint probability, prior odd, likelihood ratio as well as posterior odd. Computed value of posterior probability helps (parameters is 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 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.

Copyright (c) 2018 by ASME
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