Optimisation of Tool Life though Novel Data Acquisition and Decision Making Techniques
Lead Research Organisation:
CARDIFF UNIVERSITY
Department Name: Sch of Engineering
Abstract
This research will enable the real time assessment of CNC milling cutting processes and the management of process variations.
The primary aim will be to accurately assess tool condition and life in the context of current and imminent machining requirements. The experience of the collaborators indicates that "conservative" approaches to tool life management is costing Renishaw between 5 and 10% of the money spent on cutting tools. The cost of quality arising from process related variations cannot be accurately assessed.
The following work packages CNP) are planned:
Development of algorithms for t he on-line monitoring of tool health.
The engineering of the on-line tool-wear data acquisition system
The off-line application of the prognostic algorithms.
Testing the operation of the cutting tool wear monitoring system.
Final prognostic system implementation and testing
The following deliverables are planned:
Embedded tool wear data acquisition system.
Tool wear process algorithms
Cutting tool wear monitoring system
Off-line prognostic algorithms
Final prognostic system.
The primary aim will be to accurately assess tool condition and life in the context of current and imminent machining requirements. The experience of the collaborators indicates that "conservative" approaches to tool life management is costing Renishaw between 5 and 10% of the money spent on cutting tools. The cost of quality arising from process related variations cannot be accurately assessed.
The following work packages CNP) are planned:
Development of algorithms for t he on-line monitoring of tool health.
The engineering of the on-line tool-wear data acquisition system
The off-line application of the prognostic algorithms.
Testing the operation of the cutting tool wear monitoring system.
Final prognostic system implementation and testing
The following deliverables are planned:
Embedded tool wear data acquisition system.
Tool wear process algorithms
Cutting tool wear monitoring system
Off-line prognostic algorithms
Final prognostic system.
People |
ORCID iD |
Paul Prickett (Primary Supervisor) | |
Jacob Hill (Student) |
Publications
Hill J
(2019)
The practical exploitation of tacit machine tool intelligence
in The International Journal of Advanced Manufacturing Technology
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/P510452/1 | 01/10/2016 | 30/09/2021 | |||
1825828 | Studentship | EP/P510452/1 | 01/10/2016 | 30/09/2020 | Jacob Hill |