Computational process modelling of metal additive manufacturing has gained significant research attention in recent past. The cornerstone of many process models is the transient thermal response during the AM process. Since deposition-scale modelling of the thermal conditions in AM is computationally expensive, spatial and temporal simplifications, such as simulating deposition of an entire layer or multiple layers, and extending the laser exposure times, are commonly employed in the literature. Although beneficial in reducing computational costs, the influence of these simplifications on the accuracy of temperature history is reported on a case-by-case basis. In this paper, the simplifications from the existing literature are first classified in a normalised simplification space based on assumptions made in spatial and temporal domains. Subsequently, all types of simplifications are investigated with numerical examples and compared with a high-fidelity reference model. The required numerical discretisation for each simplification is established, leading to a fair comparison of computational times. The holistic approach to the suitability of different modelling simplifications for capturing thermal history provides guidelines for the suitability of simplifications while setting up a thermal AM model.

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Overheating is a major issue especially in metal Additive Manufacturing (AM) processes, leading to poor surface quality, lack of dimensional precision, inferior performance and/or build failures. A 3D density-based topology optimization (TO) method is presented which addresses the issue of local overheating during metal AM. This is achieved by integrating a simplified AM thermal model and a thermal constraint within the optimization loop. The simplified model, recently presented in literature, offers significant computational gains while preserving the ability of overheating detection. The novel thermal constraint ensures that the overheating risk of optimized designs is reduced. This is fundamentally different from commonly used geometry-based TO methods which impose a geometric constraint on overhangs. Instead, the proposed approach takes the process physics into account. The proposed method is validated via an experimental comparative study. Optical tomography (OT) is used for in-situ monitoring of process conditions during fabrication and obtained data is used for evaluation of overheating tendencies. The novel TO method is compared with two other methods: standard TO and TO with geometric overhang control. The experimental data reveals that the novel physics-based TO design experienced less overheating during the build as compared to the two classical designs. A study further investigated the correlation between overheating observed by high OT values and the defect of porosity. It shows that overheated regions indeed show higher defect of porosity. This suggests that geometry-based guidelines, although enhance printability, may not be sufficient for eliminating overheating issues and related defects. Instead, the proposed physics-based method is able to deliver efficient designs with reduced risk of overheating.

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With the advent of Additive Manufacturing (AM) techniques, the design principle of `form follows function' no longer remains a utopian proposition. The unprecedented design freedom offered by AM is making it possible to conceptualize highly performant designs by efficiently leveraging geometrical complexity. The increase in design freedom requires novel design tools which are tailored to capitalize on the form freedom offered by AM. Topology optimization (TO) is such a computational design tool which can find the optimal geometric layout of a part to achieve a pre-defined objective, while satisfying certain constraints. However, AM processes have inherent manufacturing constraints which should be considered at the design stage to ensure manufacturability. The suitability of TO as an ideal design tool is already widely recognized and there have been significant research efforts to integrate AM constraints within TO. In this regard, most AM-oriented TO methods utilize geometry-based constraint where a geometric AM design guideline is integrated within TO. The maturity of research in this direction is evident by the fact that most commercial CAD packages are already equipped with TO plugins including these geometry-based AM constraints. Although beneficial, such geometry-based TO constraint do not guarantee defect-free fabrication since manufacturability is not only a function of geometry, but depends on a range of complex physical interactions during the process. Therefore, a TO method that accounts for more of the physics of the AM process would enhance the likelihood of achieving better quality parts with reduced defects. This thesis is focused on laser based powder bed fusion (L-PBF) since it is the most widely utilized AM technique for metal parts. However, L-PBF suffers from certain constraints which critically compromise the part quality and inhibit its adoption as a mainstream manufacturing method. Among the constraints, the issue of local overheating remains a critical barrier as it leads to poor surface quality, inferior mechanical properties and/or build failures. Moreover, uneven heating/cooling thermal cycles due to overheating could lead to development of undesirable residual stresses and distortions. Typically, overheating is associated with downfacing surfaces called overhangs which led to development of geometry-based design guidelines, for example, avoidance of geometric features with overhangs more acute than a certain threshold. This guideline has been the most common AM constraint to integrate within TO. However, it is evident by a number of numerical and experimental studies in the literature, that the avoidance of overhangs does not guarantee overheating free designs. Therefore, the two aims of this thesis are (1) to thoroughly investigate local overheating during L-PBF process using computational models and (2) to develop a novel TO for generating overheating free AM ready designs. In this regard, the extremely high computational cost of L-PBF models was identified as the biggest challenge for both the objectives i.e. quick assessment of overheating-prone features in AM parts and integration of a L-PBF thermal model with TO. The first half of this thesis deals with a systematic investigation of the simplifications commonly used in the thermal modelling of the heat transfer phenomena during the L-PBF process. The simplifications have been classified based on the spatio-temporal resolution they assume for modelling the process. With help of numerical experiments, the findings reveal the relationship between spatio-temporal simplifications and their ability to capture certain process attributes. For example, it is found that if peak process temperatures are to be predicted, then short laser exposure times should be specified in the computational domain. On the contrary, if temperatures far away from the topmost layer are analyzed, a simplified model assuming a longer exposure time can capture it. These findings serve as guidelines in making informed choices while setting up an L-PBF thermal model. In addition to this, numerical discretization requirements associated with different simplifications are also provided. Next, a deeper investigation of relevant simplifications for detecting local overheating is presented. Three novel simplifications based on the analytical solution of the heat equation are presented which drastically reduce the computational expense while retaining the ability to identify overheating prone features. The most simplified model in this regard utilizes a localized steady-state analysis which provides maximum computational gain of approximately 600 fold as compared to a high fidelity transient simulation. The second half of the thesis presents the integration of the aforementioned steady-state L-PBF thermal model with the density- based TO method. This is achieved by formulating a novel constraint which limits the peak temperature predicted by the simplified L-PBF model. This novel physics-based TO method is validated using in-situ optical tomography (OT) measurements. Comparing OT based overheating data across geometry-based and physics-based TO designs, it is revealed that the latter have a lower tendency of overheating. Finally, the usability of the new TO method is demonstrated on an industrial injection mould. Another application of the novel TO is demonstrated by designing support structures for optimal heat evacuation. Based on the findings presented in thesis, it can be concluded that a physics-based TO method offers significant advantages over a purely geometry-based approach. In particular, it is shown that overheating avoidance cannot be assured just by avoiding acute overhangs. While for overheating detection even a simplification to steady-state analysis was possible, it is expected that for other aspects the full thermal history must be evaluated, which presents a challenge for future work. Apart from development of the novel TO approach, the second major contribution of this thesis are the insights developed regarding modelling simplifications which assist in drastically reducing the computational expenses associated with L-PBF modelling. It is expected that outcomes from this thesis will positively contribute towards development of efficient modelling techniques which will also inherently benefit further advancement of physics-based TO methods. @en

A novel constraint to prevent local overheating is presented for use in topology optimization (TO). The very basis for the constraint is the Additive Manufacturing (AM) process physics. AM enables fabrication of highly complex topologically optimized designs. However, local overheating is a major concern especially in metal AM processes leading to part failure, poor surface finish, lack of dimensional precision, and inferior mechanical properties. It should therefore be taken into account at the design optimization stage. However, including a detailed process simulation in the optimization would make the optimization intractable. Hence, a computationally inexpensive thermal process model, recently presented in the literature, is used to detect zones prone to local overheating in a given part geometry. The process model is integrated into density-based TO in combination with a robust formulation, and applied in various numerical test examples. It is found that existing AM-oriented TO methods which rely purely on overhang control do not ensure overheating avoidance. Instead, the proposed physics-based constraint is able to suppress geometric features causing local overheating and delivers optimized results in a computationally efficient manner.

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The powder bed fusion (PBF) process is a type of Additive Manufacturing (AM) technique which enables fabrication of highly complex geometries with unprecedented design freedom. However, PBF still suffers from manufacturing constraints which, if overlooked, can cause various types of defects in the final part. One such constraint is the local accumulation of heat which leads to surface defects such as melt ball and dross formation. Moreover, slow cooling rates due to local heat accumulation can adversely affect resulting microstructures. In this paper, first a layer-by-layer PBF thermal process model, well established in the literature, is used to predict zones of local heat accumulation in a given part geometry. However, due to the transient nature of the analysis and the continuously growing domain size, the associated computational cost is high which prohibits part-scale applications. Therefore, to reduce the overall computational burden, various simplifications and their associated effects on the accuracy of detecting overheating are analyzed. In this context, three novel physics-based simplifications are introduced motivated by the analytical solution of the one-dimensional heat equation. It is shown that these novel simplifications provide unprecedented computational benefits while still allowing correct prediction of the zones of heat accumulation. The most far-reaching simplification uses the steady-state thermal response of the part for predicting its heat accumulation behavior with a speedup of 600 times as compared to a conventional analysis. The proposed simplified thermal models are capable of fast detection of problematic part features. This allows for quick design evaluations and opens up the possibility of integrating simplified models with design optimization algorithms. @en

This work presents the redesign of an injection molding metal insert to be prototyped by the selective laser melting (SLM) process. The case study has been topology optimized to minimize its total mass while keeping the maximum von Mises stress and maximum displacement under load condition below chosen thresholds. Particular attention has been given to properly select the design space for the topology optimization (TO), taking care both of the industrial requirements and the simplifications needed to run the TO code. While the main TO has been performed with a commercially available software, a secondary optimization has been tried with in-house code to address the problem of local heat accumulation during the SLM manufacturing. Heat accumulation is a well-known issue for design features like overhangs and thin sections, and can cause issues such as poor surface finish and dross formation. A novel TO formulation is therefore employed to control AM associated local overheating by imposing a thermal constraint. The “hotspot indicator” is integrated with the standard compliance minimization TO problem, and a new design of the mold insert without local overheating is produced. Finally, a comparison between the two TO redesigns is briefly discussed.@en

Numerous challenges of additive manufacturing (AM) are tackled in the European Horizon 2020 project PAM^2 by studying and linking every step of the AM process cycle. For example, PAM^2 researchers from the design, processing and application side have collaborated in this work to optimise the manufacturability of metal AM parts using an improved Topology Optimisation (TO) approach, including a thermal constraint. Additionally, the project is focusing on modelling, post-processing, in- and post-process quality control and industrial assessment of AM parts, with the aim of moving beyond the state-of-the-art of precision metal AM. @en

In Laser Powder Bed Fusion (LPBF), the downfacing surfaces usually have increased surface roughness and reduced dimensional accuracy due to local overheating and warpage. To partially overcome this a new supporting structure is developed in this study, namely the contactless support. This is a thin blade parallel to the critical area which transfer the heat away from the melt pool via conduction through the powder bed instead of direct contact. The support is tested in different geometries and printing conditions to define the optimal distance from the part and its effectiveness is evaluated by measuring the surface roughness of the samples. Numerical modelling of heat transfer phenomenon is also employed to determine the thermal history of the printing process and understand which parameters define the optimal distance for the thermal supports. Finally topology optimization is used to create a support structure which minimize the wasted material while keeping the heat flow optimal.@en

The Additive Manufacturing (AM) of injection molding inserts has gained popularity during recent years primarily due to the reduced design-to-production time and form freedom offered by AM. In this paper, Topology Optimization (TO) is performed on a metallic mold insert which is to be produced by the Laser Powder Bed Fusion (LPBF) technique. First, a commercially available TO software is used, to minimize the mass of the component while ensuring adequate mechanical response under a prescribed loading condition. The commercial TO tool adopts geometry-based AM constraints and achieves a mass reduction of ~50 %. Furthermore, an in-house TO method has been developed which integrates a simplified AM process model within the standard TO algorithm for addressing the issue of local overheating during manufacturing. The two topology optimized designs are briefly compared, and the advantages of implementing manufacturing constraints into the TO algorithm are discussed.
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Additive Manufacturing (AM) enables fabrication of geometrically complex designs and hence offers increased freedom for designers. It has been recognized that topology optimization can serve as an ideal design tool in order to fully exploit the advantages offered by AM. However, AM processes have specific limitations which should be taken into account at the design optimization stage in order to minimize manual design adaptations and post processing cost. One such major constraint is local overheating during processing. It is evident that excessive local heating can cause defects such as melt ball formation which subsequently leads to poor surface finish and undesired mechanical properties. This paper presents a simplified thermal model inspired by the physics of additive processes and detects zones of local heat concentration i.e. `hotspots' in a geometry. Although the model emulates the boundary conditions of an AM layer and predicts the temperature field, it is not a detailed process simulation. Instead, a dedicated computationally inexpensive thermal analysis has been preferred here as it proves to be able to identify regions which are prone to overheating. The model is thus referred to as ‘hotspot detector’. A mathematical formulation is developed in order to integrate the ‘hotspot detector’ model with the density based topology optimization using adjoint sensitivity calculation method. The new method is tested and demonstrated on several numerical examples.@en