Multi-objective Optimisation in Additive Manufacturing

Additive Manufacturing (AM) has demonstrated great potential to advance product design and manufacturing, and has showed higher flexibility than conventional manufacturing techniques for the production of small volume, complex and customised components. In an economy focused on the need to develop customised and hi-tech products, there is increasing interest in establishing AM technologies as a more efficient production approach for high value products such as aerospace and biomedical products. Nevertheless, the use of AM processes, for even small to medium volume production faces a number of issues in the current state of the technology. AM production is normally used for making parts with complex geometry which implicates the assessment of numerous processing options or choices; the wrong choice of process parameters can result in poor surface quality, onerous manufacturing time and energy waste, and thus increased production costs and resources. A few commonly used AM processes require the presence of cellular support structures for the production of overhanging parts. Depending on the object complexity their removal can be impossible or very time (and resources) consuming. Currently, there is a lack of tools to advise the AM operator on the optimal choice of process parameters. This prevents the diffusion of AM as an efficient production process for enterprises, and as affordable access to democratic product development for individual users. Research in literature has focused mainly on the optimisation of single criteria for AM production. An integrated predictive modelling and optimisation technique has not yet been well established for identifying an efficient process set up for complicated products which often involve critical building requirements. For instance, there are no robust methods for the optimal design of complex cellular support structures, and most of the software commercially available today does not provide adequate guidance on how to optimally orientate the part into the machine bed, or which particular combination of cellular structures need to be used as support. The choice of wrong support and orientation can degenerate into structure collapse during an AM process such as Selective Laser Melting (SLM), due to the high thermal stress in the junctions between fillets of different cells. Another issue of AM production is the limited parts’ surface quality typically generated by the discrete deposition and fusion of material. This research has focused on the formation of surface morphology of AM parts. Analysis of SLM parts showed that roughness measured was different from that predicted through a classic model based on pure geometrical consideration on the stair step profile. Experiments also revealed the presence of partially bonded particles on the surface; an explanation of this phenomenon has been proposed. Results have been integrated into a novel mathematical model for the prediction of surface roughness of SLM parts. The model formulated correctly describes the observed trend of the experimental data, and thus provides an accurate prediction of surface roughness. This thesis aims to deliver an effective computational methodology for the multi- objective optimisation of the main building conditions that affect process efficiency of AM production. For this purpose, mathematical models have been formulated for the determination of parts’ surface quality, manufacturing time and energy consumption, and for the design of optimal cellular support structures. All the predictive models have been used to evaluate multiple performance and costs objectives; all the objectives are typically contrasting; and all greatly affected by the part’s build orientation. A multi-objective optimisation technique has been developed to visualise and identify optimal trade-offs between all the contrastive objectives for the most efficient AM production. Hence, this thesis has delivered a decision support system to assist the operator in the "process planning" stage, in order to achieve optimal efficiency and sustainability in AM production through maximum material, time and energy savings.EADS Airbus, Great Western Researc

Failure analysis and mechanical behaviors of metamaterials

In recent years, mechanical metamaterials have been explored for their tunable nature with the continual development of Additive Manufacturing (AM) technologies. As a result, the failure mechanisms of the metamaterials and their mechanical behaviours under different boundary and environmental conditions have been investigated. Firstly, failure mechanisms of AM originated imperfections in the metamaterials have been investigated. In this, three types of imperfection have been considered in the numerical modelling of the metamaterials: distorted struts, missing struts, and strut diameter variation. Then a novel numerical framework was developed to overcome computational difficulties within the existing numerical approaches beyond the elastic region. Three modes of microscopic localisation were observed in metamaterials before failure: crushing band, shear band and void coalescence. The results showed that a clear separation exists between the three modes of localisation depending upon the type and level of defects and loading condition. Under compressive loading, all metamaterials failed due to the crushing band; the distorted lattices are prone to shear band localisation with increased distortion, whereas missing lattices majorly fail due to void coalescence at high missing struts defect. The study on imperfect metamaterials has suggested that it can exhibit either ductile, damage-tolerant behaviours or sudden, catastrophic failure mode, depending on the distribution of the introduced disorderliness. Thus, a data-driven approach has been developed, combining deep-learning and global optimisation algorithms, to tune the distribution of the disorderliness/imperfections to achieve damage-tolerant metamaterial designs. A case study on the metamaterial created from a periodic Face Centred Cubic (FCC) lattice has demonstrated that the optimised metamaterials can generate high-quality designs with improved ductility, enabling them to sustain larger deformations without failure at a lower cost to strength and stiffness. This has been validated by an experimental study on an optimised metamaterial design. The results showed that the optimized designs can achieve up to 100% increase in ductility at the expense of less than 5% stiffness and 8-15% tensile strength. Finally, the creep behaviour of Inconel 718 metamaterial has been investigated at an elevated temperature to understand the effects of the microstructural defects. A Kachanov's damage modelling has been used to predict the creep performance of the metamaterials. The analysis and experimental results indicated that the creep resistance of the metamaterials is dependent on the microstructure and loading conditions. The creep behaviour of the metamaterials is significantly different from that of the bulk material due to their complex microstructure. Overall, this study contributes to the development of mechanical metamaterials with improved mechanical properties using AM technologies. The neural network-based data-driven methodology offers a promising avenue for designing high-quality metamaterials that are cost-effective and have desirable mechanical properties. The results of this study have significant implications for various applications, including structural engineering, biomechanics, and aerospace engineering, including in understanding, and designing for the creep behavior of Inconel 718 metamaterials

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

Failure analysis and mechanical behaviors of metamaterials

In recent years, mechanical metamaterials have been explored for their tunable nature with the continual development of Additive Manufacturing (AM) technologies. As a result, the failure mechanisms of the metamaterials and their mechanical behaviours under different boundary and environmental conditions have been investigated. Firstly, failure mechanisms of AM originated imperfections in the metamaterials have been investigated. In this, three types of imperfection have been considered in the numerical modelling of the metamaterials: distorted struts, missing struts, and strut diameter variation. Then a novel numerical framework was developed to overcome computational difficulties within the existing numerical approaches beyond the elastic region. Three modes of microscopic localisation were observed in metamaterials before failure: crushing band, shear band and void coalescence. The results showed that a clear separation exists between the three modes of localisation depending upon the type and level of defects and loading condition. Under compressive loading, all metamaterials failed due to the crushing band; the distorted lattices are prone to shear band localisation with increased distortion, whereas missing lattices majorly fail due to void coalescence at high missing struts defect. The study on imperfect metamaterials has suggested that it can exhibit either ductile, damage-tolerant behaviours or sudden, catastrophic failure mode, depending on the distribution of the introduced disorderliness. Thus, a data-driven approach has been developed, combining deep-learning and global optimisation algorithms, to tune the distribution of the disorderliness/imperfections to achieve damage-tolerant metamaterial designs. A case study on the metamaterial created from a periodic Face Centred Cubic (FCC) lattice has demonstrated that the optimised metamaterials can generate high-quality designs with improved ductility, enabling them to sustain larger deformations without failure at a lower cost to strength and stiffness. This has been validated by an experimental study on an optimised metamaterial design. The results showed that the optimized designs can achieve up to 100% increase in ductility at the expense of less than 5% stiffness and 8-15% tensile strength. Finally, the creep behaviour of Inconel 718 metamaterial has been investigated at an elevated temperature to understand the effects of the microstructural defects. A Kachanov's damage modelling has been used to predict the creep performance of the metamaterials. The analysis and experimental results indicated that the creep resistance of the metamaterials is dependent on the microstructure and loading conditions. The creep behaviour of the metamaterials is significantly different from that of the bulk material due to their complex microstructure. Overall, this study contributes to the development of mechanical metamaterials with improved mechanical properties using AM technologies. The neural network-based data-driven methodology offers a promising avenue for designing high-quality metamaterials that are cost-effective and have desirable mechanical properties. The results of this study have significant implications for various applications, including structural engineering, biomechanics, and aerospace engineering, including in understanding, and designing for the creep behavior of Inconel 718 metamaterials

Sensitivity Studies on Offshore Submarine Hoses on CALM Buoy with Comparisons for Chinese‑Lantern and Lazy‑S Configuration:OMAE2019‑96755

With more developments into cost-effective offshore designs, the application of offshore hoses has been adapted for water depths that are not too deep, and for short-service life platforms. This has led to the advances on offloading and loading operations in the offshore industry based on the utilization of Catenary Anchor Leg Moorings (CALM) buoys. However variations in the soil stiffness and environmental conditions necessitates the investigation on the behaviour of the submarine hoses based on the structural and hydrodynamic behaviour. The sensitivity study will help hose manufacturers in the problem of submarine hose failures due to high curvatures. In this study, dynamic analysis is carried out based on the design of the submarine hoses attached to a CALM buoy for both cases of the Chinese-lantern configuration and Lazy-S configurations. Six mooring lines are attached to the CALM buoy with a water depth of 26 m and 100 m, respectively. Hydrodynamic simulation using ANSYS AQWA is first conducted and later coupled into the dynamic models in Orcaflex. Sensitivity studies were conducted to study the effect of wave height, flow angles, soil stiffness and hose hydrodynamic loads on the structural behaviour of the submarine hoses