Framework-based on SQC and DMAIC to monitor die casting process parameters
by Rajan Gopal Sharma; Anupam Das; Swarnambuj Suman
International Journal of Industrial and Systems Engineering (IJISE), Vol. 23, No. 2, 2016

Abstract: The article attempts to reduce process variation and improve the product quality by using both off-line and online quality control techniques. This task may be achieved by developing an integrated model based on statistical quality control (SQC) and Six Sigma-based D-M-A-I-C (define-measure-analyse-improve-control). The goal of study was achieved by using various quality control tools viz. brainstorming sessions, histograms, failure mode effect analysis (FMEA), cause and effect diagram, control charts, principle component analysis and response surface methodology. Principle component analysis (PCA) was employed for building of the process representation and Hotelling T² chart based on the principle component score was used for detection of abnormal behaviour. Further response surface methodology was used to optimise the process parameters. There was a reduction in the overall process variability by employment of optimised process parameters. The suggested integrated model seems to be effective for process monitoring of the proposed case study and may be used to monitor other similar processes as well.

Online publication date: Fri, 06-May-2016

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