Additive Manufacturing & 3D Printing Research

Highly specialised requests are first checked for feasibility from this information. Please do not send confidential data through the public form; describe it first and agree secure transfer afterwards.

1. 01Enquiry and requirement assessment
2. 02Technical scoping and proposal
3. 03Execution
4. 04Quality review and delivery, with an agreed round of revisions

Read the full project workflow.

In depth: how the work is done

1. Designed experiments for process parameters

   Screening and response-surface designs identify which parameters influence responses such as density, strength, surface roughness and dimensional accuracy, and how they interact. Build plate position, powder or filament batch and machine state can influence results, so blocking and randomisation are considered. Because builds are costly, we help plan which factors and ranges deliver the most information, including the use of multiple specimens per build and distribution across the plate.

2. Mechanical and defect data analysis

   Specimens produced by additive manufacturing often show anisotropy and higher scatter than wrought material, including in fatigue. Analysis must separate orientation effects, build effects and specimen-level variation, and report scatter, not only means. Defect data from microscopy or tomography, such as porosity size and distribution, are related statistically to mechanical response. We consider appropriate standards for specimens and testing, and comment on how surface condition affects results.

3. Thermal and residual-stress modelling

   Rapid heating and cooling create steep thermal gradients and residual stresses that cause distortion and cracking. Models range from analytical melt-pool estimates to part-scale thermo-mechanical simulations with simplified deposition strategies, such as lumped layers. Validation against temperature or distortion measurements is important, and the simplifications used must be stated. Models are used to compare scanning strategies and support structures, with results treated as indicative.

4. Design for additive manufacturing and topology-aware design

   Design considerations include overhang angles, minimum feature sizes, support structures, orientation and post-processing access. Lattice and topology-optimised structures exploit the freedom of the process but require validation of their effective properties, which may differ from bulk because of process-specific effects at fine scales. Orientation choice affects properties, surface finish and support need, and trade-offs are described.

5. Process-property correlation and reporting

   Linking process parameters to properties is only useful if the process and the specimens are described in enough detail to be reproduced: machine, software settings, material lot, atmosphere, build layout, post-processing and test methods. We help prepare reporting that meets the expectation of journals in the field and describes uncertainty and limitations, including machine-to-machine transferability.

6. Planning builds economically

   Because each build consumes machine time and material, planning should extract as much information as possible from every plate. We suggest including replicate specimens spread across the plate, adding witness coupons for density and microstructure checks, and recording build logs such as chamber atmosphere or print temperature. Pilot builds can screen ranges before committing to a larger design. A written build plan, with specimen identifiers linked to positions and orientations, prevents confusion during testing and makes it possible to explain unexpected scatter by returning to the exact conditions in which each specimen was produced.

Common pitfalls this service helps avoid

• Ignoring build location and orientation. These factors systematically influence properties and can confound parameter effects.
• Reporting means without scatter. High variability is typical and critical for design and for conclusions.
• Assuming bulk properties for lattices. Effective properties of fine structures can differ because of defects and surface effects.
• Under-reporting process details. Without full parameters and post-processing details, results cannot be reproduced or compared.

What good reporting looks like

An additive manufacturing report describes the material and machine, process parameters and build layout, post-processing, the experimental design, specimen and test methods, the data with variability, the statistical analysis of process-property relationships, the defect characterisation, any thermal or residual-stress models and validation, and limitations including machine specificity. It supports research and does not qualify parts or processes for certified applications.

An illustrative scenario

Hypothetical example written for explanation. It is not a client case, a result or a testimonial.

Imagine a researcher exploring laser power and scan speed for a metal alloy, with orientation as a third factor. A designed experiment places specimens at several plate positions. Analysis shows that density depends strongly on an energy-related combination of power and speed, while strength depends on orientation. Tomography images show that large pores at low energy correspond to low ductility. The researcher reports the scatter and the operating window and states that results apply to the specific machine and powder lot.

Questions to settle before you begin

• Which process parameters and ranges will I explore?
• How will I account for build location and orientation?
• How will I characterise defects?
• What specimen standards and test methods will I use?
• How will I document the process for reproducibility?

Key terms

Energy density

A derived quantity combining power, speed and spacing used to compare parameter sets.

Porosity

Void content within a built part.

Anisotropy

Direction-dependent material properties.

Residual stress

Stress remaining in a part after manufacturing without external load.

Build orientation

The direction in which a part is positioned relative to the build layers.

Working together on Mechanical, Manufacturing & Industrial Engineering projects

Mechanical, manufacturing and industrial engineering engagements begin with the process or machine, the performance measures of interest and the data or models already available. We ask for process descriptions, parameter ranges, measurements and constraints, and for the decision the study should inform. Designed experiments, statistical analysis, simulation or optimisation are then selected to suit. Reports include assumptions, uncertainty and practical recommendations, and they distinguish findings that are supported by data from those that are engineering judgement.

Few projects need a single service in isolation. These are the services most often combined with this one.

• Design of Experiments (DOE)

  Planning of efficient experimental programmes using factorial, fractional and optimal designs so that factor effects and interactions are estimated with the fewest runs the question allows.

• Topology & Design Optimisation

  Computational determination of material layout and form within a design space to meet performance targets under constraints, with attention to manufacturability.

• Composite Material Simulation

  Modelling of fibre-reinforced and layered composites, including laminate behaviour, damage initiation and progression, and micromechanical homogenisation.

• Experimental & Materials Research Manuscripts

  Manuscript support for experimental studies, particularly in materials, mechanics and process research: clear experimental procedures, statistical treatment of results, characterisation data and discussion tied to mechanisms.