Subject description
Descriptive statistics and probability: data management and visualization, numerical descriptions, correlation and regression, basic probability, random variables and probability distributions.
Statistical inherence: statistics and sampling distributions, estimations of parameters, hypothesis testing.
Statistical process control: basic tools and philosophy of statistical process control, fundamentals of control diagrams, control charts for variables, control charts for attributes, process capability ratios.
Visual quality control: cameras, illumination and optical systems, basic tools for image processing and analysis, examples of machine vision systems for visual quality control.
The subject is taught in programs
Objectives and competences
To introduce basic theory and the most common tools for statistical process control, which enable efficient monitoring and minimization of variability in the production processes and thereby improving the quality of products.
Teaching and learning methods
Basic theory, procedures and practical examples are considered at lectures, while practical knowledge is gained through problem-solving tasks at lab works.
Expected study results
After successful completion of the course, students should be able to:
- apply tools of descriptive statistics for monitoring and numerical estimation of data
- explain basic methods of statistical inherence
- explain the main causes of stochastic variations in production processes
- build basic tolls of statistical process control for monitoring production processes
- build control diagrams for monitoring mean values and variabilities of production processes
- describe the applicability of computer vision for quality control in industry
Basic sources and literature
B. Likar, Osnove statistične procesne kontrole, Založba FE in FRI, 1. izdaja, 2005.
D. C. Montgomery, Introduction to statistical quality control, Wiley; 6th edition, 2008.
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University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25, 1000 Ljubljana