SENSYCUT- Sensor Enabled Systems for Precision Cutting
Lead Research Organisation:
University of Bath
Department Name: Mechanical Engineering
Abstract
UK is the world's 9th largest manufacturing country [1]. Machining is one of the most used processes for producing precision parts used in aerospace and automotive industries. The demand for high performance and quality assured parts requires high precision, often over a large scale resulting in increased manufacturing costs. It has become a rule of thumb that precise machines with stiff structures and large foot prints are required for machining precision parts. As a consequence, machining costs grow exponentially as the precision increases. This has resulted in the development of expensive and non-value adding off-line verification and error compensation methods. However, these methods do not take the impact of cutting tool/workpiece geometry, cutting forces and time variable errors into account. The uptake of additive manufacturing has also resulted in generation of optimised parts often with complex geometries and thin and high walls which require finish machining with long slender tools. In these scenarios, cutting forces can bend the tool and the workpiece resulting in geometrical inaccuracies. Fluctuating cutting forces result in chatter leading to damaged surface integrity and short tool life.
Using new sensors, advanced signal processing and intelligent control systems can provide the ability to detect geometrical and surface anomalies when machining, and provide data to generate strategies to prevent costly mistakes and poor quality. However, off-the-shelf sensors and data transmission devices are not necessarily suitable for monitoring and controlling machining processes. Existing high precision sensors are either too large or too expensive making them only useful for laboratory applications. Conventional statistical and process control methods cannot cope with high data sampling rates required in machining.
The proposed research will realise low-cost sensors with nano scale resolution specific to machining, tools and intelligent control methods for precision machining of large parts by detecting and preventing anomalies during machining to ensure high precision part manufacture and prevent scrap production.
[1] Rhodes, C., 2018, Briefing Paper No. 05809, Manufacturing: International comparisons, House of Commons Library.
Using new sensors, advanced signal processing and intelligent control systems can provide the ability to detect geometrical and surface anomalies when machining, and provide data to generate strategies to prevent costly mistakes and poor quality. However, off-the-shelf sensors and data transmission devices are not necessarily suitable for monitoring and controlling machining processes. Existing high precision sensors are either too large or too expensive making them only useful for laboratory applications. Conventional statistical and process control methods cannot cope with high data sampling rates required in machining.
The proposed research will realise low-cost sensors with nano scale resolution specific to machining, tools and intelligent control methods for precision machining of large parts by detecting and preventing anomalies during machining to ensure high precision part manufacture and prevent scrap production.
[1] Rhodes, C., 2018, Briefing Paper No. 05809, Manufacturing: International comparisons, House of Commons Library.
Publications
Liao Z
(2024)
Review of current best-practices in machinability evaluation and understanding for improving machining performance
in CIRP Journal of Manufacturing Science and Technology
Ullrich K
(2024)
AI-based optimisation of total machining performance: A review
in CIRP Journal of Manufacturing Science and Technology
Omole S
(2023)
Using machine learning for cutting tool condition monitoring and prediction during machining of tungsten
in International Journal of Computer Integrated Manufacturing
Axinte D
(2022)
What micro-mechanical testing can reveal about machining processes
in International Journal of Machine Tools and Manufacture
Dogan H
(2023)
Towards Sustainable and Intelligent Machining: Energy Footprint and Tool Condition Monitoring for Media-Assisted Processes
in Journal of Machine Engineering
Dogan H
(2023)
Investigation of chatter detection with sensor-integrated tool holders based on strain measurement
in Procedia CIRP
Malakizadi A
(2022)
Recent advances in modelling and simulation of surface integrity in machining - a review
in Procedia CIRP
Jamshidi H
(2023)
A Finite Element Assessment of The Workpiece Plastic Deformation in Machining of Ti-6Al-4V
in Procedia CIRP
Description | Tour of advanced manufacturing research group for year 9 pupils |
Form Of Engagement Activity | Participation in an open day or visit at my research institution |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Schools |
Results and Impact | A group of year 9 pupils from a local secondary school Alderman White visited University of Nottingham Advanced manufacturing research group. It involved discussion about different manufacturing processes and how things are made with the pupils and it raised their interest in knowing more about manufacturing of aerospace parts. |
Year(s) Of Engagement Activity | 2022 |