Lorena Caires Moreira

Research Fellow, Institute for Future Transport and Cities

Emailad3722@coventry.ac.uk

Source: Scopus
Calculated based on no. of publications stored in Pure and citations from Scopus

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14 Citations
2 h-Index
3 Article
1 Conference proceeding
1 Paper

Barriers and Enablers of Workforce Adoption of Digital Transformation in a Modern Manufacturing Environment
Maher, K., Soares, A. & Caires Moreira, L., 2020, (Accepted/In press).
Research output: Contribution to conference › Paper
Energy-Efficient Machining Process Analysis and Optimisation Based on BS EN24T Alloy Steel as Case Studies
Caires Moreira, L., Li, W., Lu, X. & Fitzpatrick, M., Aug 2019, In : Robotics and Computer-Integrated Manufacturing. 58, p. 1-12 12 p.
Research output: Contribution to journal › Article

Open Access
File
5 Citations (Scopus)

21 Downloads (Pure)
Supervision Controller for Real-time Surface Quality Assurance in CNC Machining using Artificial Intelligence
Caires Moreira, L., Li, W., Lu, X. & Fitzpatrick, M., Jan 2019, In : Computers & Industrial Engineering. 127, p. 158-168 11 p.
Research output: Contribution to journal › Article

Open Access
File
6 Citations (Scopus)

23 Downloads (Pure)
Research on energy consumption and energy efficiency of machine tools: a comprehensive survey
Caires Moreira, L., Li, W., Fitzpatrick, M., Lu, X. & Li, X., 2018, In : International Journal of Nanomanufacturing. 14, 2, p. 140-164 25 p.
Research output: Contribution to journal › Article
2 Citations (Scopus)
Supervision Control for Quality Assurance in Milling Processes
Caires Moreira, L., Lu, X., Li, W. & Fitzpatrick, M., 2017, 47th International Conference on Computers & Industrial Engineering 2017 (CIE47): How Digital Platforms and Industrial Engineering are Transforming Industry and Services. Computers and Industrial Engineering, p. 398-405 8 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding

Open Access
1 Citation (Scopus)

Barriers and Enablers of Workforce Adoption of Digital Transformation in a Modern Manufacturing Environment

Energy-Efficient Machining Process Analysis and Optimisation Based on BS EN24T Alloy Steel as Case Studies

Supervision Controller for Real-time Surface Quality Assurance in CNC Machining using Artificial Intelligence

Research on energy consumption and energy efficiency of machine tools: a comprehensive survey

Supervision Control for Quality Assurance in Milling Processes