Authors: Rajan Gopal Sharma; Anupam Das; Swarnambuj Suman
Addresses: Department of Process Engineering (PE), Denso Pricol India Limited, Plot No. 34 and 35, Sector-4 IMT Manesar, Gurgaon-122050 Haryana, India ' Department of Mechanical Engineering, National Institute of Technology Patna, Patna-800005, Bihar, India ' Department of Mechanical Engineering, National Institute of Technology Patna, Patna-800005, Bihar, India
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.
Keywords: six sigma; Hotelling T2 control charts; statistical quality control; SQC; principle component analysis; PCA; response surface methodology; RSM; failure mode and effects analysis; FMEA; DMAIC; process monitoring; die casting; process parameters; statistical process control; SPC; process variation; product quality; brainstorming; histograms; cause and effect diagram; case study.
International Journal of Industrial and Systems Engineering, 2016 Vol.23 No.2, pp.204 - 222
Received: 29 Mar 2014
Accepted: 03 Sep 2014
Published online: 06 May 2016 *