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

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 (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.

Sand Casting Implementation of Two-Dimensional Digital Code Direct-Part-Marking Using Additively Manufactured Tags

“…It, therefore, becomes difficult to identify the most critical parameters influencing the quality of castings without associating process data collected with individual cast part quality inspection data. 10 Some form of tracking of parts is needed to associate process measurements with part quality inspection measurements.…”

Quality Improvement in Investment Castings Using Genetic Algorithm

“…Metal/alloy is melted in furnace, and then molten metal/alloys is poured into heated shell. Shell is broken (once it is cooled) to get castings [1] [2].…”

“…Investment casting mostly faces quality issue related to occurrence of defects including flash (excess metal dropping perpendicularly on the surfaces), misrun (cavities void of liquid metal in the mold), ceramic inclusion (trapping the particles of the ceramic on the surface or subsurface of castings), slag inclusion (dropping off a combination of metal and investment materials on the surface of castings), shrinkage (tiny cavities similar to a sponge in the last solidifying portion), sweating (dropping metal irregularly on the surfaces of castings), distortion (regarding the shape in original for castings), and crack (The local discontinuity is formed from concentrated stress through solidification). The castings also possess higher set of mechanical properties such as ultimate tensile strength, yield strength, and elongation [1] [2].…”

“…It is pressing need to identify such parameters and their specific set of values to achieve desired quality in investment castings. Once these parameters are identified, it becomes relatively easy to control form improving the quality in castings [1] [2].…”