Statistical Process Control

Higher education teachers: Likar Boštjan

Subject description

Prerequisits:

• Enrolment in the 2nd year and passed Mathematics I and II courses.

Content (Syllabus outline):

• 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.

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.

Intended learning outcomes:

• Descriptive statistics and probability,
• statistical inherence,
• statistical process control and
• visual quality control.

Learning and teaching methods:

• Basic theory,
• procedures and practical examples are considered at lectures,
• while practical knowledge is gained through problem-solving tasks at lab works.

Study materials

Readings:

1. D. C. Montgomery, Introduction to statistical quality control, Wiley; 6th edition, 2008.