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Details of Grant 

EPSRC Reference: EP/X032213/1
Title: Field Computation Based Kernel for Vector 3D Printing
Principal Investigator: Wang, Professor C
Other Investigators:
Researcher Co-Investigators:
Project Partners:
5Axisworks Ltd Airbus Operations Limited
Department: Mechanical Aerospace and Civil Eng
Organisation: University of Manchester, The
Scheme: EPSRC Fellowship
Starts: 01 December 2023 Ends: 30 November 2028 Value (£): 1,648,677
EPSRC Research Topic Classifications:
Design & Testing Technology
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
11 Jul 2023 ELEMENT Fellowship Interview Panel 12 and 13 July 2023 Announced
03 May 2023 Engineering Prioritisation Panel Meeting 3 and 4 May 2023 Announced
Summary on Grant Application Form
Although additive manufacturing is called 3D printing, the fabrication in most cases is still in a 2.5D way - materials are accumulated layer upon layer in planes along a fixed printing direction, restricting the flexibility of 3DP. The commonly identified problems of the current 2.5D printing practice are i) weak mechanical strength between the layers of materials, ii) additional supporting structures that are hard to remove and lead to the waste of material and fabrication time, iii) staircase appearance on the surface of printed models. Moreover, this planar fabrication also forbids printing anisotropically strong materials such as carbon fibres along designed paths like "tendons in muscles" to reinforce the mechanical strength or printing on top of curved surfaces for advanced electrical / biological functions. All restrict the fast growth of 3DP technology.

These limitations can be overcome by the strategy of Vector 3D Printing (Vec3DP) that extrudes materials along dynamically varied directions. Adding more Degrees-of-Freedom (DoFs) onto the 3D printer and controlling its multi-axis motion is less difficult to implement on hardware. Robotic arms for welding or advanced multi-axis milling machines have already realised this sort of motion. However, the state-of-the-art lacks a computational kernel to effectively generate optimised toolpaths / motions of Vec3DP for models with complex geometry and material distribution although there are some pilot works that can produce relatively simple models. This gap of computational kernel further prohibits the upstream investigation of design for Vec3DP and the downstream applications for Vec3DP.

My group is the first in the world that invents the technology for automatically generating manufacturable curved 3D toolpaths to fabricate a general solid model through the multi-axis motion of a robotic system. To secure our leading position at the vanguard of this engineering frontier, my ambition of this fellowship is to investigate and develop a computational kernel to enable the integrated design and manufacturing for vector 3D printing as the next generation of additive manufacturing. Investigating such a kernel for Vec3DP has the following scientific challenges:

1) The search space for optimal solutions has been extended from two-manifold (planar layers for conventional 3DP or given surfaces for multi-axis CNC) into three-manifold (volume). This change from plane / surface to volume tremendously increases both the degrees-of-freedom (DoFs) and the complexity of problems.

2) Decoupled optimization conducted in different phases of design, planning and manufacturing realisation cannot solve the problem systematically. This leads to a consequence that the products optimised in the design phase cannot be successfully realised in the manufacturing phase. This is a challenge for both conventional 3DP and vector 3DP; however, vector 3DP has more complicated manufacturing objectives / constraints to be considered.

3) A whole pipeline optimisation needs to compute the derivatives of objectives, constraints, material models, and other operations with respect to the design variables (i.e., sensitivities), where topological changes (e.g., mesh generation, Boolean operations on B-reps) are not differentiable. This restricts the usage of derivative-based optimisers, including neural network based deep-learning that relies on differentiation in back-propagation.

I envision that all these challenges can be overcome by investigating a field-based computational kernel to tackle the design and manufacturing problems for Vec3DP.

Key Findings
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Organisation Website: http://www.man.ac.uk