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Details of Grant 

EPSRC Reference: EP/V055011/1
Title: SENSYCUT- Sensor Enabled Systems for Precision Cutting
Principal Investigator: Chaharsooghi, Dr AS
Other Investigators:
Axinte, Professor DA Mohammadi, Dr A Ghadbeigi, Professor H
Newman, Professor ST Liao, Dr Z
Researcher Co-Investigators:
Project Partners:
All British Precision Ltd GKN Aerospace (Melrose) Nikken UK
Renishaw Sandvik (Cormant/Steel) TWI Ltd
Department: Mechanical Engineering
Organisation: University of Bath
Scheme: Standard Research
Starts: 16 December 2021 Ends: 15 August 2025 Value (£): 1,198,919
EPSRC Research Topic Classifications:
Instrumentation Eng. & Dev. Manufact. Enterprise Ops& Mgmt
EPSRC Industrial Sector Classifications:
Aerospace, Defence and Marine Manufacturing
Related Grants:
Panel History:
Panel DatePanel NameOutcome
27 Apr 2021 Precision Manufacturing - Full Proposals Announced
Summary on Grant Application Form
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.

Key Findings
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Organisation Website: http://www.bath.ac.uk