Multi-objective Optimisation in Additive Manufacturing (2012)

Additive Manufacturing (AM) has demonstrated great potential to advance product \ndesign and manufacturing, and has showed higher flexibility than conventional \nmanufacturing techniques for the production of small volume, complex and customised \ncomponents. In an economy focused on the need to develop customised and hi-tech \nproducts, there is increasing interest in establishing AM technologies as a more efficient \nproduction approach for high value products such as aerospace and biomedical \nproducts. \nNevertheless, the use of AM processes, for even small to medium volume production \nfaces a number of issues in the current state of the technology. AM production is \nnormally used for making parts with complex geometry which implicates the \nassessment of numerous processing options or choices; the wrong choice of process \nparameters can result in poor surface quality, onerous manufacturing time and energy \nwaste, and thus increased production costs and resources. A few commonly used AM \nprocesses require the presence of cellular support structures for the production of \noverhanging parts. Depending on the object complexity their removal can be impossible \nor very time (and resources) consuming. \n \nCurrently, there is a lack of tools to advise the AM operator on the optimal choice of \nprocess parameters. This prevents the diffusion of AM as an efficient production \nprocess for enterprises, and as affordable access to democratic product development for \nindividual users. \nResearch in literature has focused mainly on the optimisation of single criteria for AM \nproduction. An integrated predictive modelling and optimisation technique has not yet \nbeen well established for identifying an efficient process set up for complicated products which often involve critical building requirements. For instance, there are no \nrobust methods for the optimal design of complex cellular support structures, and most \nof the software commercially available today does not provide adequate guidance on \nhow to optimally orientate the part into the machine bed, or which particular \ncombination of cellular structures need to be used as support. The choice of wrong \nsupport and orientation can degenerate into structure collapse during an AM process \nsuch as Selective Laser Melting (SLM), due to the high thermal stress in the junctions \nbetween fillets of different cells. \nAnother issue of AM production is the limited partsâ surface quality typically generated \nby the discrete deposition and fusion of material. This research has focused on the \nformation of surface morphology of AM parts. Analysis of SLM parts showed that \nroughness measured was different from that predicted through a classic model based on \npure geometrical consideration on the stair step profile. Experiments also revealed the \npresence of partially bonded particles on the surface; an explanation of this phenomenon \nhas been proposed. Results have been integrated into a novel mathematical model for \nthe prediction of surface roughness of SLM parts. The model formulated correctly \ndescribes the observed trend of the experimental data, and thus provides an accurate \nprediction of surface roughness. \nThis thesis aims to deliver an effective computational methodology for the multi-\nobjective optimisation of the main building conditions that affect process efficiency of \nAM production. For this purpose, mathematical models have been formulated for the \ndetermination of partsâ surface quality, manufacturing time and energy consumption, \nand for the design of optimal cellular support structures. \nAll the predictive models have been used to evaluate multiple performance and costs \nobjectives; all the objectives are typically contrasting; and all greatly affected by the \npartâs build orientation. A multi-objective optimisation technique has been developed to visualise and identify\noptimal trade-offs between all the contrastive objectives for the most efficient AM\nproduction. Hence, this thesis has delivered a decision support system to assist the\noperator in the "process planning" stage, in order to achieve optimal efficiency and\nsustainability in AM production through maximum material, time and energy savings.