Diploma in Quality Control (QA/QC) Course in Rawalpindi Pakistan
To help students understand the concepts underlying statistical quality control and to develop their ability to apply those concepts to the design and management of quality control processes in industries. Major topics include history and overview of the state of the art of quality control methodologies, tools for descriptive and predictive statistical analysis, design and use of various control charts for quality control, process characterization and capability analysis, R&R gauge capability studies, design of experiments, acceptance sampling and continuous improvement. The emphasis will be on ensuring that the students gain both a broad perspective of quality control as well as the technical skills necessary to implement quality control in any industrial setting.
The course will comprise a balanced blend of the statistical quality control concepts and hands-on training in the methods, standards and guidelines currently being used for industrial quality control. The course will not assume any prior knowledge other than previous exposure to elementary probability theory; the discussion will be self-contained and all of the topics will be developed from the fundamentals. The course will enable a practising engineer to gain a firm grasp of statistical quality control methods and enable him/her to not only analyze and improve existing quality control processes, but also design and implement new quality control processes in industrial settings.
History of quality control, modern quality control philosophy, Design-Measure-Analyze-Improve-Control paradigm, methods for describing variation including histograms, stem-and-leaf plots, box plots, discrete and continuous random variables, probability plots, statistical inference methods, design of control charts for variable and attribute data including X-bar, R, S, CUSUM, MA and EWMA charts, sensitizing rules including Western Electric guidelines, average run length, process characterization and capability analysis, gauge R&R studies, design of experiments with emphasis on factorial design, sampling inspection, attribute and variable acceptance plans, six-sigma and TQM.