Deji Aremu

Accepting PhD Students

PhD projects

1. Development of computational algorithms for Additive manufactured components 2. An investigation into the Computational Fluid Dynamics of Lattices structures 3. A investigation into designs constraints within cheaper additive manufacturing techniques for metals 4. Development of cheap metallic additive manufacturing techniques 5. Development of robust computational algorithm for generating lattices structures 6. Engaging additive manufacturing techniques for solving prevalent engineering issues in third world nations. 7. Development of optimisation strategies and algorithms to topographically derive antibacterial surfaces

Research activity per year

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Education/Academic qualification

Mechanical and Manufacturing, Doctorate, Loughborough University

3 Jan 2009 → 31 Dec 2013

Award Date: 13 Dec 2012

Manufacturing Systems Engineering, MSc, University of Warwick

2 Oct 2006 → 2 Oct 2007

Award Date: 13 Dec 2007

Metallurgical and Material Engineering, Obafemi Awolowo University

1 Sep 1998 → 30 Nov 2005

Award Date: 12 Feb 2005

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1505 Citations
12 h-index
9 Article
4 Conference proceeding
3 Working paper
2 Chapter
More
- 1 Review article

A Machine Learning Approach to Predicting Two Dimensional Structural Performance
Aremu, D., Delli Carri, A., Razmkhah, O. & Ashman, P., 2020.
Research output: Working paper/Preprint › Working paper
A Pythonic Approach to Voxelizing Stereolithographic Models
Aremu, D., Delli Carri, A. & Ashman, P., 2020, Association for Computing Machinery.
Research output: Working paper/Preprint › Working paper
Entrepreneurial Opportunities For Additive Manufacturing Technologies in Sub-Saharan Africa
Aremu, D., Asare, K., Osabuohien, E., Ufua, D., Delli Carri, A. & Ashman, P., 2020, Technovation.
Research output: Working paper/Preprint › Working paper
Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing
Silbernagel, C., Aremu, A. & Ashcroft, I., 2 Dec 2019, In: Rapid Prototyping Journal. 26, 4, p. 625-637 13 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File
22 Citations (Scopus)

93 Downloads (Pure)
Effective design and simulation of surface-based lattice structures featuring volume fraction and cell type grading
Maskery, I., Aremu, A., Parry, L., Wildman, R., Tuck, C. & Ashcroft, I., 5 Oct 2018, In: Materials and Design. 155, p. 220-232 13 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File
149 Citations (Scopus)

110 Downloads (Pure)

Deji Aremu

Personal profile

Education/Academic qualification

Fingerprint

Network

Dive into details

A Machine Learning Approach to Predicting Two Dimensional Structural Performance

A Pythonic Approach to Voxelizing Stereolithographic Models

Entrepreneurial Opportunities For Additive Manufacturing Technologies in Sub-Saharan Africa

Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing

Effective design and simulation of surface-based lattice structures featuring volume fraction and cell type grading

Deji Aremu

Personal profile

Education/Academic qualification

Fingerprint

Network

Research output

A Machine Learning Approach to Predicting Two Dimensional Structural Performance

A Pythonic Approach to Voxelizing Stereolithographic Models

Entrepreneurial Opportunities For Additive Manufacturing Technologies in Sub-Saharan Africa

Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing

Effective design and simulation of surface-based lattice structures featuring volume fraction and cell type grading