Progress in Additive Manufacturing

Salmi, Mika

doi: 10.1007/s40964-022-00329-zpmid: N/A

Spurek, Marvin A.; Haferkamp, Lukas; Weiss, Christian; Spierings, Adriaan B.; Schleifenbaum, Johannes H.; Wegener, Konrad

doi: 10.1007/s40964-021-00240-zpmid: N/A

Powder bed fusion (PBF) is the most commonly adopted additive manufacturing process for fabricating complex metal parts via the layer-wise melting of a powder bed using a laser beam. However, the qualification of PBF-manufactured parts remains challenging and expensive, thereby limiting the broader industrialization of the technology. Powder characteristics significantly influence part properties, and understanding the influencing factors contributes to effective quality standards for PBF. In this study, the influence of the particle size distribution (PSD) median and width on powder flowability and part properties is investigated. Seven gas-atomized SS316L powders with monomodal PSDs, a median particle size ranging from 10 μm to 60 μm, and a distribution width of 15 μm and 30 μm were analyzed and subsequently processed. The PBF-manufactured parts were analyzed in terms of density and melt pool dimensions. Although powder flowability was inversely related to the median particle size, it was unrelated to the distribution width. An inverse relationship between the median particle size and the part density was observed; however, no link was found to the distribution width. Likely, the melt pool depth and width fluctuation significantly influence the part density. The melt pool depth decreases and the width fluctuation increases with an increasing median particle size.

Abdulwahab, Mohamad; Bijanzad, Armin; Khan, Shaheryar A.; Lazoglu, Ismail

doi: 10.1007/s40964-021-00242-xpmid: N/A

The fused Filament fabrication method gained its popularity in the additive manufacturing industry not only because of the low capital and manufacturing cost, but also due to its ease of production approach, availability, and mobility of the method. However, the quality of final parts and the mechanical properties are directly related to layer thickness, resolution, filament raw material, and working temperature. Thermoplastic materials, especially polylactic acid (PLA), are widely used considering their low melting temperature, lower requirement of post-processing, and sustainability. However, poor mechanical properties, layer delamination, and low ductility are the main drawbacks of these materials. This work aims to study the effects of polyurea as an elastomeric coating on PLA printed specimens. Three geometrical configurations were prepared, and tensile properties of coated and uncoated samples are investigated using stress–strain curves. It has been shown that hot polyurea coating results in a reduction in the ultimate tensile strength (UTS) of specimens. However, it increases ductility and elongation performance of the samples remarkably. In addition, the elasticity modulus and elastoplastic behavior of the specimens are modeled mathematically.

Rahman, Mostafizur; Brackett, David; Milne, Katy; Szymanski, Alex; Okioga, Annestacy; Huertas, Lina; Jadhav, Swati

doi: 10.1007/s40964-021-00246-7pmid: N/A

The Additive Manufacturing (AM) process chain has many steps, each of which generates data, potentially, in different formats. This paper aims to show how these data may be used together to mature the process. However, there are many challenges to getting these data and using it to generate knowledge and close the feedback loop. The biggest current challenges which were common to the AM end uses are: identifying key process variables, knowing which data to capture during the process, understanding how to use in-line inspection to detect defects and managing and using the data collected during the whole AM process chain. The digital process itself is not digital, there is still a lot of work done manually, especially data and information handling, and very limited use of data analysis and knowledge management. This paper maps the processes and the data along the AM process chain and proposed an integrated process and data framework for Additive Manufacturing for the purpose of knowledge management and closed-loop feedback.

Farashi, Sajjad; Vafaee, Fariborz

doi: 10.1007/s40964-021-00247-6pmid: N/A

3D printing is an interesting and growing field in many areas. Despite advantages, several issues regarding the quality and strength of products have remained unresolved. Among these issues are the effects of printing process parameters on the mechanical properties of a printed object. In this study, the effects of the layer thickness and print orientation on the tensile strength of FDM-printed samples were considered. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, eligible studies in major databases were found. The pooled effect size, heterogeneity and publication bias were assessed using statistical analysis. Results showed that increasing layer thickness might reduce the tensile strength of FDM-printed samples up to 20%, while increasing the printing angle of the sample on the build platform decreased the tensile strength to about 12%. Furthermore, adjusting extruder temperature to higher values and printing speeds to lower values might reduce the heterogeneity of results between studies. In conclusion, when enhanced tensile strength is required, it is recommended to focus mainly on the layer thickness than printing orientation, adjust lower layer thickness and orient the model parallel to the build platform.

Fiedler, Torben; Dörries, Kai; Rösler, Joachim

doi: 10.1007/s40964-021-00248-5pmid: N/A

Selective laser melted (SLM) aluminum alloys are widely used for many technical applications. However, the application is limited to low temperatures due to their relatively poor creep resistance. The creep resistance and strength could be enhanced by oxide dispersion-strengthening. A hypothesis is that oxygen intake during selective laser melting can lead to formation of fine aluminum oxides and thus strengthen the SLMed part. To elucidate this in more detail, selective laser melted AlSi10Mg was tested in creep experiments at temperatures of 300 °C. Although, in other studies at lower temperatures, a relatively large stress exponent for creep was found, the high temperatures in this work led to a creep exponent of just 7 to 8, indicating no significant dispersion strengthening. Furthermore, for future research, it was necessary to investigate the feasibility of SLM with pure aluminum. For this purpose, a parameter study was carried out and an optimum parameter set for pure aluminum was found. Dense samples with a porosity below 0.2% were produced. Selective laser melting was carried out with a varying oxygen content in the inert-gas atmosphere to elucidate the hypothetic strengthening effects by oxygen intake. However, even at 800 ppm oxygen in the atmosphere, no effect on hardness and microstructure could be observed.

König, Moritz; Diekmann, Jana; Lahres, Michael; Middendorf, Peter

doi: 10.1007/s40964-021-00249-4pmid: N/A

While fiber reinforced polymers in fused filament fabrication (FFF) provide improved tensile properties in bead direction, only low properties can be reached perpendicular to the bead direction. The effects of process parameters on interlayer and bead-to-bead bonding of a short carbon fiber (0.1–0.3 mm) reinforced polyamide were investigated using tensile specimens loaded transverse to the bead direction. The evaluated process parameters were printing temperature, printing speed, bead width, layer height, and air gap. Mesostructures of printed samples were analyzed for parameter effects on interfacial void content using optical microscopy. Fracture surfaces of tested tensile specimens were analyzed using a scanning electron microscope to explain material failure. Parameter settings that were found to achieve high and low interfacial void contents were selected to print further tensile specimens loaded in bead direction to observe the effect of process parameters on the material’s degree of anisotropy. It was found that the short carbon fibers are not able to increase the interfacial bonding strength significantly compared to previously published results of unreinforced polyamides, due to weak fiber–matrix bonding and the process induced fiber orientation. However, the parameters air gap, layer height, and printing temperature were found to compensate the high melt viscosity and the resulting low material flow of the short carbon fiber reinforced polyamide, leading to stronger bead-to-bead bonding. While the bead-to-bead bonding is highly affected by the resulting contact areas of adjacent beads in the mesostructure, interlayer bonding was found to be most positively affected by decreasing printing speed. Nevertheless, while the process parameters affect interlayer and bead-to-bead bonding differently, the material’s degree of anisotropy was found to be decreased with a low interfacial void content. Based on our findings, considering the direction of mechanical stresses is crucial while selecting process parameters to achieve the best part performance of load-bearing parts printed with short fiber reinforced materials in FFF. For further performance improvement, future research should investigate how to establish FFF materials with strong fiber–matrix bonding and how to consider the materials’ anisotropy in selecting a parts infill pattern and part orientation for the parts load case.

Ramazani, Haidar; Kami, Abdolvahed

doi: 10.1007/s40964-021-00250-xpmid: N/A

Recently, various additive manufacturing (AM) methods with a wide range of capabilities have been employed to produce metallic objects. Metals are a popular choice among AM materials due to their superior properties, despite being more challenging to print. Reduced product cost, the possibility for quick production and prototyping, and the capability of a produced component by high accuracy in a broad variety of shapes, geometrical complexity, size, and material are all advantages of metal AM technology. Metal fused deposition modeling (metal FDM) is a relatively new technique based on the widely used FDM process. It is a relatively low-cost competitor to other metal AM techniques such as selective laser melting (SLM). This review paper has explored the most recently issued publications in this extrusion-based metal additive manufacturing (EAM) technique. The main parameters in feedstock preparation, deposition and 3D printing, debinding, and sintering phases of the metal FDM process will be discussed and their influence on the mechanical and microstructural characteristics of the 3D-printed parts. Furthermore, the application of finite element modeling for metal FDM process analysis is explored. Finally, the challenges and gaps in the manufacturing of components and obtaining desired characteristics have been presented.

Dhar, Ananda Rabi; Gupta, Dhrubajyoti; Roy, Shibendu Shekhar; Lohar, Aditya Kumar

doi: 10.1007/s40964-021-00251-wpmid: N/A

To enhance the automation in additive manufacturing technology, establishment of accurate relationship between the process parameters and responses is extremely important. Direct metal deposition (DMD) or laser metal deposition (LMD) is an ever-emerging field in additive manufacturing spectrum because of its higher build-up rate with flexibility at multiple scales and reduced material wastage. In this work, a feed-forward neural network-based model is proposed for predicting the clad height and capture efficiency while building austenitic steel part through DMD process with variations in input parameters, namely laser power, scan speed, and powder flow rate. With an aim to enhance the prediction performance, the model is hybridized with ancient metaheuristic algorithms, namely genetic algorithm and particle swarm optimization as well as some rare and new metaheuristic algorithms, namely firefly algorithm, bio-geography-based optimization, flower pollination algorithm, dragonfly algorithm, and gray wolf optimization. The backward mapping model is also established along similar lines using the same hybridization schema and all the approaches are comparatively studied. The bi-directional models are further investigated by applying extreme gradient boost (XGBoost), a new and emerging paradigm in the field of machine learning. The comparison is further emphasized by employing a statistical test known as ‘technique for order of preference by similarity to ideal solution (TOPSIS).’ The novelty of the present study lies in utilizing the aforementioned rare and new metaheuristic algorithms for training artificial neural networks in order to develop predictive models in the domain of DMD as well as application of XGBoost for the same. The results clearly recommend the application of hybridized computational intelligence-based approaches in case of the forward mapping model and XGBoost in case of the backward mapping model.

Shakor, Pshtiwan; Chu, S. H.; Puzatova, Anastasiia; Dini, Enrico

doi: 10.1007/s40964-021-00252-9pmid: N/A

Binder jetting (inkjet), featured by the dripping of liquid on the powder-based layer for solidification and bonding, is the second most popular 3D printing (3DP) technology for the construction industry. Therefore, an overview of this technology is necessary for both industry and academia. The paper discussed the most suitable materials for the construction industry which can be used in binder jetting 3DP. Attention was given not only to the selection of the materials but also the printing process and challenges that might face the printing process and post-processing stages, with an emphasis on sustainability and suitability. The whole process of printing gypsum, cement and geopolymer mortar, clay, chipped wood and sand materials through the binder jetting technique has been summarized, followed by curing and post-processing to achieve desirable mechanical properties. Finally, an informative approach was introduced for the scale-up of binder jetting 3DP in the construction industry.