Project Details
Description
Although British healthcare/biomedical manufacturing generates £70 billion/year and 240,000 jobs; its most important yield is a healthy, functional, thriving society. Unexpected externalities such as supply chain disruptions, sustainability requirements and socioeconomic circumstances (e.g. Brexit, COVID-19) pose a threat to this sector and more importantly to the wellbeing of Britain's population. To cope with these threats, it is imperative to develop new and strengthen existing technologies capable of manufacturing precise high-value, patient-personalised products in decentralised settings.
Additive manufacturing technologies, such as 3D printing, have shown these characteristics as they enable prototyping and manufacturing customized products on-site in a rapid, and economic manner. Certainly, 3D printing has revolutionized manufacturing practices and generated tremendous economic benefits to economies worldwide; for instance, in the UK, 3D printing has a revenue of £2.4bn annually. Even so, this technology has major technical issues including, feedstock-performance dependency (printing needs to be calibrated depending of the plastic used), excessive plastic waste production (a major environmental concern), poor printing resolution (nanometer-size structures cannot be printed) and low flexibility in its operation mode (cannot produce long fibres, particles). These technical drawbacks significantly hinder the deployment of 3D printing in many healthcare/biomedical settings.
Inspired by the response of organisms to environmental conditions, this project will develop a novel responsive additive technology (named eHD-3D printing) capable of responding autonomously to feedstock and product requirements, while addressing each of the challenges present in modern 3D printing technologies. To achieve these transformative characteristics, we will integrate bio-inspired modalities (e.g. sensing, thinking and moving). We will employ novel analytical tools that enable sensing the type of material/plastic fed into the unit. This information coupled with the characteristics of the product will allow an AI-algorithm to determine the best operating conditions and operation mode. Beyond conventional 3D printing, the eHD-3D unit will be able to generate particles (0D) and fibres (1D) with a nano-metric resolution, enabling the manufacture of complex multi-scaled structures. Moreover, to demonstrate the transformative features of the eHD-3D unit, a range of geometrically and structurally diverse tissue scaffolds will be manufactured.
Additive manufacturing technologies, such as 3D printing, have shown these characteristics as they enable prototyping and manufacturing customized products on-site in a rapid, and economic manner. Certainly, 3D printing has revolutionized manufacturing practices and generated tremendous economic benefits to economies worldwide; for instance, in the UK, 3D printing has a revenue of £2.4bn annually. Even so, this technology has major technical issues including, feedstock-performance dependency (printing needs to be calibrated depending of the plastic used), excessive plastic waste production (a major environmental concern), poor printing resolution (nanometer-size structures cannot be printed) and low flexibility in its operation mode (cannot produce long fibres, particles). These technical drawbacks significantly hinder the deployment of 3D printing in many healthcare/biomedical settings.
Inspired by the response of organisms to environmental conditions, this project will develop a novel responsive additive technology (named eHD-3D printing) capable of responding autonomously to feedstock and product requirements, while addressing each of the challenges present in modern 3D printing technologies. To achieve these transformative characteristics, we will integrate bio-inspired modalities (e.g. sensing, thinking and moving). We will employ novel analytical tools that enable sensing the type of material/plastic fed into the unit. This information coupled with the characteristics of the product will allow an AI-algorithm to determine the best operating conditions and operation mode. Beyond conventional 3D printing, the eHD-3D unit will be able to generate particles (0D) and fibres (1D) with a nano-metric resolution, enabling the manufacture of complex multi-scaled structures. Moreover, to demonstrate the transformative features of the eHD-3D unit, a range of geometrically and structurally diverse tissue scaffolds will be manufactured.
| Status | Finished |
|---|---|
| Effective start/end date | 2/11/21 → 29/08/25 |
Collaborative partners
- University of Bath (lead)
- 3D Metal Printing Ltd
- Royal United Hospitals Bath NHS Foundation Trust
Funding
- Engineering and Physical Sciences Research Council

Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Safe Multi-channel Communication for Human-Robot Collaboration
Al, G. A. & Martinez Hernandez, U., 28 Feb 2026, In: Robotics and Computer-Integrated Manufacturing. 97, 23 p., 103109.Research output: Contribution to journal › Article › peer-review
Open Access3 Link opens in a new tab Citations (SciVal) -
Designing porous molecularly imprinted polymers via simulation of pre-polymerisation mixtures: a case study with trinitrotoluene
Lightfoot, J. C., Battell, W., Castro Dominguez, B. & Herdes, C., 25 Aug 2025, In: Molecular Systems Design & Engineering. 10, 12, p. 1051-1059 9 p.Research output: Contribution to journal › Article › peer-review
Open Access1 Link opens in a new tab Citation (SciVal) -
Electrospinning Technology, Machine Learning and Control Approaches: A Review
Shabani, A., Al, G. A., Berri, N., Castro Dominguez, B., Leese, H. & Martinez Hernandez, U., 30 Apr 2025, In: Advanced Engineering Materials. 27, 7, 2401353.Research output: Contribution to journal › Review article › peer-review
Open Access
Datasets
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Dataset for "Low-Cost, Multi-Sensor Non-Destructive Banana Ripeness Estimation Using Machine Learning"
Callaghan, K. (Creator) & Martinez Hernandez, U. (Creator), University of Bath, 16 Jan 2025
DOI: 10.15125/BATH-01459
Dataset
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Dataset for "Filament Type Recognition for Additive Manufacturing Using a Spectroscopy Sensor and Machine Learning"
Al, G. A. (Creator), University of Bath, 3 Mar 2025
DOI: 10.15125/BATH-01501
Dataset
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Dataset for "Soft Tactile Sensor with Multimodal Data Processing for Texture Recognition"
Martinez-Hernandez, U. (Creator), University of Bath, 1 Aug 2023
DOI: 10.15125/BATH-01303
Dataset