Process capability improvement of an engine connecting rod machining process

Journal of Industrial Engineering International

دوره 9، شماره 1، اسفند 2013   

نویسندگان

GVSS Sharma^{* 1}؛ P Srinivasa Rao²

¹Department of Mechanical Engineering, GMR Institute of Technology, GMR Nagar, Rajam-532127, Andhra Pradesh State, India

²Department of Industrial Engineering, GITAM Institute of Technology, GITAM University, Visakhapatnam, 530045, Andhra Pradesh State, India

چکیده

Statistical process control is an excellent quality assurance tool to improve the quality of manufacture and ultimately
scores on end-customer satisfaction. SPC uses process monitoring charts to record the key quality characteristics (KQCs)
of the component in manufacture. This paper elaborates on one such KQC of the manufacturing of a connecting rod of
an internal combustion engine. Here the journey to attain the process potential capability index (Cp) and the process
performance capability index (Cpk) values greater than 1.33 is elaborated by identifying the root cause through quality
control tools like the cause-and-effect diagram and examining each cause one after another. In this paper, the
define-measure-analyze-improve-control (DMAIC) approach is employed. The definition phase starts with process
mapping and identifying the KQC. The next phase is the measurement phase comprising the cause-and-effect
diagram and data collection of KQC measurements. Then follows the analysis phase where the process potential and
performance capability indices are calculated, followed by the analysis of variance (ANOVA) of the mean values. Finally,
the process monitoring charts are used to control the process and prevent any deviations. By using this DMAIC
approach, standard deviation is reduced from 0.48 to 0.048, the Cp values from 0.12 to 1.72, and the Cpk values from
0.12 to 1.37, respectively.

کلیدواژه‌ها

Key quality characteristic؛ Cause-and-effect diagram؛ Statistical process control؛ Process monitoring charts؛ Failure modes and effects analysis؛ Analysis of variance