1. SylabUZ
2. Wydział Mechaniczny
3. semestr zimowy 2023/2024
4. Management and Production Engineering - pierwszego stopnia z tyt. inżyniera
5. Quality Control

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Quality Control - opis przedmiotu

Cel przedmiotu

The aim of the course is to provide information on: basic concepts in the field of statistics, types and application of statistical methods for quality control and improvement, quality management using statistical methods and other methods, determining the machine and process capability and their impact on the quality of manufacturing processes.

Wymagania wstępne

Zapisz zmiany

passing courses: Statistical Methods in Production Processes and Design for Quality

Zakres tematyczny

Lecture:

L1: Introduction. Basic concepts: quality. Concepts in the development of quality management. Quality Control (QC). Quality control scope. SPC - Statistical Process Control. Basic SPC tools.

L2: Theoretical basis of statistical quality control. Statistical variables: continuous and discrete. Statistical distributions and their parameters. Point estimation: descriptive statistics (measures of location, dispersion and distortion). Graphical presentation and data analysis: histogram, boxplot, quantile-quantile plot, normality plot, scatterplot.

L3: Statistical hypothesis testing. Application of statistical inference in statistical quality control. Basic significance tests.

L4: Statistical hypothesis testing. Basic nonparametric goodness-of-fit tests.

L5: Fitting the distribution to the data. Distribution transformations - Box-Cox transformation.

L6: Basics of statistical process control. The essence of control charts.  Principles and interpretations of control charts. Nelson's rules.

L7: Control charts for continuous variables.

L8: The effectiveness of control charts. Xbar chart and type I and II error. Operating-characteristic (OC) curves. ARL0 and ARL1 indicators.

L9: Sequential control charts: CUSUM, EWMA, MA, MR chart.

L10: Control charts for fraction nonconforming and nonconformities (defects).

L11: Assessment of the stability of short-run production: short-runs control charts.

L12: Monitoring of several process parameters: a set of classic univariate control charts and multivariate chi-square and T2 Hotteling charts.

L13: Process capability analysis, process capability ratios. Examples of capable and incapable processes.

L14: Special charts: modified and acceptance control charts.

L15: Acceptance sampling. Lot-by-lot acceptance sampling for attributes.

 

Laboratory:

L1: Introduction to Statistica.

L2: Process analysis using graphical methods.

L3. Analysis and evaluation of the selected process using descriptive statistics, frequency tables and a probability calculator.

L4. Analysis and evaluation of the selected process using significance tests.

L5: Analysis and evaluation of the selected process using goodness-of-fit tests. Fitting the distribution to the data. Distribution transformations - Box-Cox transformation.

L6. Working with the control charts in the Statistica: configuration, checking non-random patterns (runs rules tests), process monitoring.

L7: Assessment of statistical stability of the selected process using the Xbar - R and Xbar - S charts.

L8: Comparing the effectiveness of classic control charts. Plotting operating-characteristic (OC) curves. Determination of ARL0 and ARL1 indicators.

L9: Comparing the effectiveness of classical and sequential control cards.

L10: Assessment of process stability using control charts for fraction nonconforming and nonconformities.

L11: Assessment of the stability of short-run production: short-runs control charts.

L12: Monitoring of several process parameters: a set of classic univariate control charts and multivariate chi-square and T2 Hotteling charts.

L13: Assessment of the capability of selected normal and non-normal distribution processes.

L14: Determining limits for special charts: modified and acceptance control charts, examining process capability using special charts.

L15: Final test.

Metody kształcenia

Lecture: a conventional lecture

Laboratory: practical classes in the computer laboratory

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

Lecture: written exam preceded by obtaining a credit for laboratory classes

Laboratory: the condition for passing the laboratory is to pass all laboratory tasks and a positive grade in the final test

Final grade: the condition for passing the course is to pass all its forms, the final grade for the course is the arithmetic mean of the grades for individual forms of classes

 

Literatura podstawowa

1. Montgomery D.C.,  Introduction to Statistical Quality Control, John Wiley & Sons, New York,  2009
2. Mitra A., Fundamentals of quality control and improvement, John Wiley & Sons, Hoboken, New Jersey,  2008
3. Oakland J. S.,  Statistical Process Control, Butterworth-Heinemann, 2007

Literatura uzupełniająca

1. Tapiero C. S., The Management of Quality and its Control, Springer, 1996
2. Wheeler D. J., Understanding Statistical Process Control,SPC Press, Knoxville, 2010

Uwagi

Brak

Zmodyfikowane przez dr inż. Iwona Pająk (ostatnia modyfikacja: 01-05-2023 12:10)