Lorena Caires Moreira

Research Fellow, Institute for Future Transport and Cities

Source: Scopus
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17 Citations
2 h-Index
3 Conference proceeding
3 Article
1 Paper

A comparative study of classical static and AI models predicting energy consumption in machining for Learning Factories
Caires Moreira, L., 15 Apr 2020, Proceedings 10th Conference on Learning Factories: (CLF 2020): Learning Factories Across the Value Chain - From Innovation to Service. Procedia Manufacturing
Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review
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 › peer-review
Assessment of Energy Efficiency and Productivity of CNC Machining Processes
Caires Moreira, L., 7 Jun 2019, IEEE Workshop & SENSORICA 2019. IEEE
Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review
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 › peer-review

Open Access
File
6 Citations (Scopus)

35 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 › peer-review

Open Access
File
8 Citations (Scopus)

55 Downloads (Pure)

Industry 4.0: intelligent optimisation and control for efficient and sustainable manufacturing
Author: Caires Moreira, L., Jan 2020
Supervisor: Li, W. (Supervisor) & Fitzpatrick, M. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy

File

A comparative study of classical static and AI models predicting energy consumption in machining for Learning Factories

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

Assessment of Energy Efficiency and Productivity of CNC Machining Processes

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

Industry 4.0: intelligent optimisation and control for efficient and sustainable manufacturing