Thesis

Monitoring of incremental rotary forming

Creator
Rights statement
Awarding institution
  • University of Strathclyde
Date of award
  • 2019
Thesis identifier
  • T15370
Person Identifier (Local)
  • 201574564
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • As manufacturing processes move toward full automation, reliable instrumentation is vital for improved process control. This work examines new applications of nondisruptive monitoring techniques for incremental rotary forming (IRF) processes. IRF processes such as flow forming (FF) and spinning make rotationally symmetric components. These cold, incremental processes produce parts to near net shape with improved mechanical properties and high material utilisation. The design and operation of these processes is limited by an incomplete understanding of forming mechanics and process design.Monitoring is used in many industries to improve process understanding, control and operation. The relevance of existing monitoring technologies to IRF was assessed and three were selected. Acoustic and vibration monitoring were both investigated fordetecting fracture during FF. An ultrasonic (US) monitoring system was developed toscan the contact area between the tool and the material during FF and spinning.;The results showed that acoustic monitoring can detect major fracture events in FF. Vibration monitoring did not show useful results. Testing of the US system showed that it is possible to record changes in the tool-workpiece contact area, detect internal fractures in the part, and measure the thickness of parts spun in free air. Fracture,contact area and thickness have never before been measured in-process.This work demonstrates for the first time the ability to monitor IRF contact properties, thickness and fracture in real time. Monitoring of these could be used in industry to refine the design of tooling and processes, validate modelling, and avoid unexpected failures. In the medium term, improved monitoring will allow improved process control and automation with reduced risk.
Advisor / supervisor
  • Ion, William
  • Gray, Alistair
Resource Type
DOI
Date Created
  • 2019
Former identifier
  • 9912769989102996

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