Introductory description

The module will investigate the way in which quality and reliability techniques can be used to improve the quality of manufacture. Conventional techniques associated with quality, reliability and maintenance will be introduced and used to quantify and diagnose common issues. Digital tools to aid quality, reliability and maintenance will also covered in the module. The role of embedded intelligence to capture, process and share factory events and the role of data analysis in supporting this will be introduced through class-based exploratory exercises.
When this module is delivered overseas, it is delivered in a 2-week block rather than over 4 weeks.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Quality Techniques for assessing process performance;
Six-Sigma;
Failure Mode Effect and Criticality Analysis, Fault Tree Analysis
Lifetime data analysis
Maintenance methods
Asset Management
All above in context of Industry 4.0 , cyber-manufacturing risk and digital technology

Learning outcomes

By the end of the module, students should be able to:

• Examine the digital-specific risks associated with the use of digital-manufacturing to enhance system quality, reliability and availability
• Evaluate techniques and methods that affect system reliability, maintenance and operational performance
• Critically evaluate the application of data-analysis methods to aid manufacturing systems maintenance planning
• Appraise the application of quality tools for process capability and process control.

Indicative reading list

Reading lists can be found in Talis

Subject specific skills

Use quality techniques such as SPC and Six-Sigma to diagnose process performance and capability.
Analyse the potential sources and effect of faults in manufacturing processes.
Perform analysis of fault data using a relevant data-science technique.

Transferable skills

Dealing with complex issues both systematically and creatively, make sound judgements in the absence of complete data, and communicate conclusions clearly to specialist and non-specialist audiences.
Demonstrate self-direction and originality in tackling and solving problems, and act autonomously in planning and implementing tasks at a professional or equivalent level.
Advance knowledge and understanding, by developing new technical skills.
Independent learning ability required for continuing professional development.