Prof. David Curtis

MEng (hons), PhD

Advanced Manufacturing Research Centre (AMRC)

Professor of Subtractive Manufacturing

David Curtis
Profile picture of David Curtis
d.t.curtis@sheffield.ac.uk

Full contact details

Prof. David Curtis
Advanced Manufacturing Research Centre (AMRC)
Factory of the Future, Advanced Manufacturing Park
Wallis Way, Catcliffe
Rotherham
S60 5TZ
Profile

In 2005, David completed a MEng in Mechanical Engineering at the University of Birmingham, UK, which included a Year in Industry placement. Upon completion he remained in Birmingham and studied for a PhD funded by EPSRC, Rolls-Royce and other industrial partners. His research concerned the application of point grinding technology for the production of aeroengine disc features.

David is currently the Professor for Subtractive Manufacturing within the AMRC and is based out of the Machining Group. Since joining in 2009, he has progressed from a Project Engineer through to Technical Fellow in key functions. He has led or played a key role in the development of independent research areas including: Abrasive Machining; Emerging Machining Technology; Machining Science; Aerofoil and Gear Manufacturing Technology. He also leads activity within the AMRC on the machining of Ceramic Matrix Composite (CMC) materials, focusing on translational research which takes Machining Science fundamentals into an industrial context.

David has an extensive applied research portfolio centred around Aeroengine, Aerostructure and Transmission commodities. His work applies current technology for immediate exploitation and works towards developing fundamental capability into exploitable outputs across UK manufacturers. He is also the AMRC Co-Director for the EPSRC CDT in Machining Science (IDC in Machining Science).

Qualifications

MEng (hons) in Mechanical Engineering, PhD in Mechanical Engineering

Research interests

David’s research interests include: Subtractive Processes (cutting, abrasive, hybrid, physical), Subtractive Technology (materials, near net shape, sustainability, digital, zero defect, machines) and Subtractive Implementation (alignment to industrial needs, maturing manufacturing capability, supply chain resilience, workforce and skills). In an academic perspective, his specific research areas include abrasive processes, micro machining, ceramic matrix composites and the subtractive manufacturing of advanced materials.

Publications

Journal articles

Conference proceedings papers

Patents

  • Novovic D, Kelsey J & Curtis D (2020) Component manufacturing using a grinding tool following a trochoidal path. GB2579784B Appl. 08 Jul 2020. RIS download Bibtex download
  • Khan S & Curtis D (2020) Grinding cylindrical bores. EP3659746A1 Appl. 03 Jun 2020. RIS download Bibtex download
  • Novovic D, Kelsey J & Curtis D (2020) Manufacturing method. US11267096B2 Appl. 18 Jun 2020. RIS download Bibtex download
  • Sufyan K & Curtis D (2020) Grinding cylindrical bores. US20200156202A1 Appl. 21 May 2020. RIS download Bibtex download
  • Novovic D, Kelsey J & Curtis D (2019) Mold assemblies of the fastening for the method for the workpiece of machining operations and for this workpiece. CN109514747A Appl. 26 Mar 2019. RIS download Bibtex download
  • Novovic D, Kelsey J & Curtis D (2019) Method and an assembly. US20190084104A1 Appl. 21 Mar 2019. RIS download Bibtex download
  • Khan S & Curtis D (2019) Grinding cylindrical bores. GB201818823D0 Appl. 02 Jan 2019. RIS download Bibtex download
  • Novovic D, Kelsey J & Curtis D (2017) A method and an assembly. GB201714976D0 Appl. 01 Nov 2017. RIS download Bibtex download
  • Curtis D, Novovic D & Kelsey J () A method of securing a workpiece for a machining operation and a mould assembly for such a workpiece. EP3456465A3 Appl. 01 Jan 1970. RIS download Bibtex download

Reports

  • Curtis D, Cousins B, Sun C, Farmery M, Staley S & Cook B (2021) Power Skiving – A Step Changing Manufacturing Process Applicable to Multifunctional 5-Axis Machine Tools RIS download Bibtex download

Preprints

  • Sun C, Dominguez-Caballero J, Ward R, Ayvar-Soberanis S & Curtis D (2021) Machining Cycle Time Prediction: Data-driven Modelling of Machine Tool Feedrate Behavior with Neural Networks, arXiv. RIS download Bibtex download
Research group

Machining