Economic aspects of additive manufacturing: benefits, costs and energy consumption

Additive Manufacturing (AM) refers to the use of a group of technologies capable of combining material layer-by-layer to manufacture geometrically complex products in a single digitally controlled process step, entirely without moulds, dies or other tooling. AM is a parallel manufacturing approach, allowing the contemporaneous production of multiple, potentially unrelated, components or products. This thesis contributes to the understanding of the economic aspects of additive technology usage through an analysis of the effect of AM s parallel nature on economic and environmental performance measurement. Further, this work assesses AM s ability to efficiently create complex components or products. To do so, this thesis applies a methodology for the quantitative analysis of the shape complexity of AM output. Moreover, this thesis develops and applies a methodology for the combined estimation of build time, process energy flows and financial costs. A key challenge met by this estimation technique is that results are derived on the basis of technically efficient AM operation. Results indicate that, at least for the technology variant Electron Beam Melting, shape complexity may be realised at zero marginal energy consumption and cost. Further, the combined estimator of build time, energy consumption and cost suggests that AM process efficiency is independent of production volume. Rather, this thesis argues that the key to efficient AM operation lies in the user s ability to exhaust the available build space

Real-time stress analysis of three-dimensional boundary element problems with continuously updating geometry

Computational design of mechanical components is an iterative process that involves multiple stress analysis runs; this can be time consuming and expensive. Significant improvements in the efficiency of this process can be made by increasing the level of interactivity. One approach is through real-time re-analysis of models with continuously updating geometry. In this work the boundary element method is used to realise this vision. Three primary areas need to be considered to accelerate the re-solution of boundary element problems. These are re-meshing the model, updating the boundary element system of equations and re-solution of the system. Once the initial model has been constructed and solved, the user may apply geometric perturbations to parts of the model. A new re-meshing algorithm accommodates these changes in geometry whilst retaining as much of the existing mesh as possible. This allows the majority of the previous boundary element system of equations to be re-used for the new analysis. Efficiency is achieved during re-integration by applying a reusable intrinsic sample point (RISP) integration scheme with a 64-bit single precision code. Parts of the boundary element system that have not been updated are retained by the re-analysis and integrals that multiply zero boundary conditions are suppressed. For models with fewer than 10,000 degrees of freedom, the re-integration algorithm performs up to five times faster than a standard integration scheme with less than 0.15% reduction in the L_2-norm accuracy of the solution vector. The method parallelises easily and an additional six times speed-up can be achieved on eight processors over the serial implementation. The performance of a range of direct, iterative and reduction based linear solvers have been compared for solving the boundary element system with the iterative generalised minimal residual (GMRES) solver providing the fastest convergence rate and the most accurate result. Further time savings are made by preconditioning the updated system with the LU decomposition of the original system. Using these techniques, near real-time analysis can be achieved for three-dimensional simulations; for two-dimensional models such real-time performance has already been demonstrated

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms