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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.

Publications

10 25 50
 
Description The project is on track to meet the objectives of the SENSYCUT. These include a sensor integrated system consisting of multiple high end sensors for monitoring of machining systems for producing defect free parts. New methods have been designed, developed and tested for early detection of tool wear to prevent damage to high value precision parts. A new sensory system has been designed and is being developed.
Exploitation Route Further application for research funding to investigate the new findings. Further industry-academia funding to transfer the technologies and learnings from this project to industrial applications. Potential for spin offs or licensing the IP generated after protection.
Sectors Environment

Manufacturing

including Industrial Biotechology

 
Description Twinning IZTECH in Robotics Manufacturing Systems (TWIN-IT-ROMANS)
Amount £107,079 (GBP)
Funding ID 10130829 
Organisation United Kingdom Research and Innovation 
Sector Public
Country United Kingdom
Start 08/2024 
End 09/2027
 
Title Tool wear, cutting forces and vibrations 
Description This is a dataset of various sensor signals (forces, vibrations, surface roughness, acoustic emissions) from machining experiments whilst monitoring the tool condition. 
Type Of Material Database/Collection of data 
Year Produced 2024 
Provided To Others? No  
Impact Generating machining databases where various machining parameters are monitored is very time consuming and costly. These datasets are valuable for training and validating various data-driven models such as deep learning. 
 
Description Integrated Machining Performance for Assessment of Cutting Tools (IMPACT) 
Organisation Chemnitz University of Technology
Country Germany 
Sector Academic/University 
PI Contribution The IMPACT is an initiative by Prof I.S. Jawahir born out of the International Academy of Production Engineers where four of the SENSYCUT investigators are members of and is divided into 5 topics. The SENSYCUT research team took leadership of three of the research topics in this collaboration: i) Design and development of smart tooling/sensors for closed-loop adaptive machining, ii) best-practices in machinability evaluation and industrial knowledge and iii) AI-based multi-objective optimization of total machining performance. This involved performing three state of the art reviews and a round robin test to benchmark and compare the performance of various sensors used for monitoring machining using different setups and machines in various laboratories across the world. The knowledge generated enables us to understand how the data used for training on a machine tool can be transferred to other machines and setups.
Collaborator Contribution Project partners supported the development of the state of the art review papers, brought in different perspectives from across the world and contributed to the round robin tests. Additionally, two of the other topics were led by the partners. Topic E was co-led by SENSYCUT research team and researchers from the University of Chemnitz.
Impact https://doi.org/10.1016/j.cirpj.2024.01.012 https://doi.org/10.1016/j.cirpj.2024.02.008 https://doi.org/10.1016/j.cirpj.2024.05.001 3 technical presentations
Start Year 2021
 
Description Integrated Machining Performance for Assessment of Cutting Tools (IMPACT) 
Organisation Czech Technical University in Prague
Country Czech Republic 
Sector Academic/University 
PI Contribution The IMPACT is an initiative by Prof I.S. Jawahir born out of the International Academy of Production Engineers where four of the SENSYCUT investigators are members of and is divided into 5 topics. The SENSYCUT research team took leadership of three of the research topics in this collaboration: i) Design and development of smart tooling/sensors for closed-loop adaptive machining, ii) best-practices in machinability evaluation and industrial knowledge and iii) AI-based multi-objective optimization of total machining performance. This involved performing three state of the art reviews and a round robin test to benchmark and compare the performance of various sensors used for monitoring machining using different setups and machines in various laboratories across the world. The knowledge generated enables us to understand how the data used for training on a machine tool can be transferred to other machines and setups.
Collaborator Contribution Project partners supported the development of the state of the art review papers, brought in different perspectives from across the world and contributed to the round robin tests. Additionally, two of the other topics were led by the partners. Topic E was co-led by SENSYCUT research team and researchers from the University of Chemnitz.
Impact https://doi.org/10.1016/j.cirpj.2024.01.012 https://doi.org/10.1016/j.cirpj.2024.02.008 https://doi.org/10.1016/j.cirpj.2024.05.001 3 technical presentations
Start Year 2021
 
Description Integrated Machining Performance for Assessment of Cutting Tools (IMPACT) 
Organisation McGill University
Country Canada 
Sector Academic/University 
PI Contribution The IMPACT is an initiative by Prof I.S. Jawahir born out of the International Academy of Production Engineers where four of the SENSYCUT investigators are members of and is divided into 5 topics. The SENSYCUT research team took leadership of three of the research topics in this collaboration: i) Design and development of smart tooling/sensors for closed-loop adaptive machining, ii) best-practices in machinability evaluation and industrial knowledge and iii) AI-based multi-objective optimization of total machining performance. This involved performing three state of the art reviews and a round robin test to benchmark and compare the performance of various sensors used for monitoring machining using different setups and machines in various laboratories across the world. The knowledge generated enables us to understand how the data used for training on a machine tool can be transferred to other machines and setups.
Collaborator Contribution Project partners supported the development of the state of the art review papers, brought in different perspectives from across the world and contributed to the round robin tests. Additionally, two of the other topics were led by the partners. Topic E was co-led by SENSYCUT research team and researchers from the University of Chemnitz.
Impact https://doi.org/10.1016/j.cirpj.2024.01.012 https://doi.org/10.1016/j.cirpj.2024.02.008 https://doi.org/10.1016/j.cirpj.2024.05.001 3 technical presentations
Start Year 2021
 
Description Integrated Machining Performance for Assessment of Cutting Tools (IMPACT) 
Organisation TechSolve
Country United States 
Sector Private 
PI Contribution The IMPACT is an initiative by Prof I.S. Jawahir born out of the International Academy of Production Engineers where four of the SENSYCUT investigators are members of and is divided into 5 topics. The SENSYCUT research team took leadership of three of the research topics in this collaboration: i) Design and development of smart tooling/sensors for closed-loop adaptive machining, ii) best-practices in machinability evaluation and industrial knowledge and iii) AI-based multi-objective optimization of total machining performance. This involved performing three state of the art reviews and a round robin test to benchmark and compare the performance of various sensors used for monitoring machining using different setups and machines in various laboratories across the world. The knowledge generated enables us to understand how the data used for training on a machine tool can be transferred to other machines and setups.
Collaborator Contribution Project partners supported the development of the state of the art review papers, brought in different perspectives from across the world and contributed to the round robin tests. Additionally, two of the other topics were led by the partners. Topic E was co-led by SENSYCUT research team and researchers from the University of Chemnitz.
Impact https://doi.org/10.1016/j.cirpj.2024.01.012 https://doi.org/10.1016/j.cirpj.2024.02.008 https://doi.org/10.1016/j.cirpj.2024.05.001 3 technical presentations
Start Year 2021
 
Description Integrated Machining Performance for Assessment of Cutting Tools (IMPACT) 
Organisation University of Kentucky
Country United States 
Sector Academic/University 
PI Contribution The IMPACT is an initiative by Prof I.S. Jawahir born out of the International Academy of Production Engineers where four of the SENSYCUT investigators are members of and is divided into 5 topics. The SENSYCUT research team took leadership of three of the research topics in this collaboration: i) Design and development of smart tooling/sensors for closed-loop adaptive machining, ii) best-practices in machinability evaluation and industrial knowledge and iii) AI-based multi-objective optimization of total machining performance. This involved performing three state of the art reviews and a round robin test to benchmark and compare the performance of various sensors used for monitoring machining using different setups and machines in various laboratories across the world. The knowledge generated enables us to understand how the data used for training on a machine tool can be transferred to other machines and setups.
Collaborator Contribution Project partners supported the development of the state of the art review papers, brought in different perspectives from across the world and contributed to the round robin tests. Additionally, two of the other topics were led by the partners. Topic E was co-led by SENSYCUT research team and researchers from the University of Chemnitz.
Impact https://doi.org/10.1016/j.cirpj.2024.01.012 https://doi.org/10.1016/j.cirpj.2024.02.008 https://doi.org/10.1016/j.cirpj.2024.05.001 3 technical presentations
Start Year 2021
 
Description Integrated Machining Performance for Assessment of Cutting Tools (IMPACT) 
Organisation University of Tennessee
Country United States 
Sector Academic/University 
PI Contribution The IMPACT is an initiative by Prof I.S. Jawahir born out of the International Academy of Production Engineers where four of the SENSYCUT investigators are members of and is divided into 5 topics. The SENSYCUT research team took leadership of three of the research topics in this collaboration: i) Design and development of smart tooling/sensors for closed-loop adaptive machining, ii) best-practices in machinability evaluation and industrial knowledge and iii) AI-based multi-objective optimization of total machining performance. This involved performing three state of the art reviews and a round robin test to benchmark and compare the performance of various sensors used for monitoring machining using different setups and machines in various laboratories across the world. The knowledge generated enables us to understand how the data used for training on a machine tool can be transferred to other machines and setups.
Collaborator Contribution Project partners supported the development of the state of the art review papers, brought in different perspectives from across the world and contributed to the round robin tests. Additionally, two of the other topics were led by the partners. Topic E was co-led by SENSYCUT research team and researchers from the University of Chemnitz.
Impact https://doi.org/10.1016/j.cirpj.2024.01.012 https://doi.org/10.1016/j.cirpj.2024.02.008 https://doi.org/10.1016/j.cirpj.2024.05.001 3 technical presentations
Start Year 2021
 
Title SENSYCUT tool condition monitoring 
Description SENSYCUT tool wear condition monitoring is a deep learning model that uses SENSYCUT sensor signals to detect and predict cutting tool condition during machining. 
Type Of Technology Software 
Year Produced 2025 
Impact Cutting tools are made of critical raw materials (CRM) such as cobalt and tungsten. Typically, 30-50% of the cutting tools remaining useful life is wasted as a result of changing them early prior to reaching their tool life criterion. Using the SENSYCUT tool condition monitoring system, tool utilisation can be increased to as much as 95% significantly reducing CRM consumption for tooling as well as the associated tooling costs for production. 
 
Description Smallpeice Summer School 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Schools
Results and Impact A group of 50 widening participation (WP) pupils from across the country visited the Sustainable and Intelligent Manufacturing research group at the University of Bath. The visit was part of the lectures and tours given to a summer school organised by colleagues at the University of Bath with a charity called the Smallpeice Trust.
The SENSYCUT research team demonstrated the technologies that we are developing and explained the research that we are doing in SENSYCUT. Additionally, the team explained why the research matters in the industrial context and the wider environmental and economical impact that it can have.
Year(s) Of Engagement Activity 2023,2024
 
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 2022,2024