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Binder jetting (BJ) process is an additive manufacturing (AM) process in which powder materials are selectively joined by binder materials. Products can be manufactured layer-by-layer directly from three-dimensional model data. The quality properties of the products fabricated by the BJ AM process are significantly affected by the process parameters. To improve the product quality, the optimal process parameters need to be identified and controlled. This research works with the 420 stainless steel powder material.

This study focuses on four key printing parameters and two end-product quality properties. Sixteen groups of orthogonal experiment designed by the Taguchi method are conducted, and then the results are converted to signal-to-noise ratios and analyzed by analysis of variance.

Five sets of optimal parameters are concluded and verified by four group confirmation tests. Finally, by taking the optimal parameters, the end-product quality properties are significantly improved.

These optimal parameters can be used as a guideline for selecting proper printing parameters in BJ to achieve the desired properties and help to improve the entire BJ process ability.

Additive manufacturing (AM) or solid freeform fabrication (SFF) technique is extensively used to produce intrinsic 3D structures with high accuracy. Its significant contributions in the field of tissue engineering (TE) have significantly increased in the recent years. TE is used to regenerate or repair impaired tissues which are caused by trauma, disease and injury in human body. There are a number of novel materials such as polymers, ceramics and composites, which possess immense potential for production of scaffolds. However, the major challenge is in developing those bioactive and patient-specific scaffolds, which have a required controlled design like pore architecture with good interconnectivity, optimized porosity and microstructure. Such design not only supports cell proliferation but also promotes good adhesion and differentiation. However, the traditional techniques fail to fulfill all the required specific properties in tissue scaffold. The purpose of this study is to report the review on AM techniques for the fabrication of TE scaffolds.

The present review paper provides a detailed analysis of the widely used AM techniques to construct tissue scaffolds using stereolithography (SLA), selective laser sintering (SLS), fused deposition modeling (FDM), binder jetting (BJ) and advanced or hybrid additive manufacturing methods.

Subsequently, this study also focuses on understanding the concepts of TE scaffolds and their characteristics, working principle of scaffolds fabrication process. Besides this, mechanical properties, characteristics of microstructure, in vitro and in vivo analysis of the fabricated scaffolds have also been discussed in detail.

The review paper highlights the way forward in the area of additive manufacturing applications in TE field by following a systematic review methodology.

This paper aims to present a systematic approach in the literature survey related to metal additive manufacturing (AM) processes and its multi-physics continuum modelling approach for its better understanding.

A systematic review of the literature available in the area of continuum modelling practices adopted for the powder bed fusion (PBF) AM processes for the deposition of powder layer over the substrate along with quantification of residual stress and distortion. Discrete element method (DEM) and finite element method (FEM) approaches have been reviewed for the deposition of powder layer and thermo-mechanical modelling, respectively. Further, thermo-mechanical modelling adopted for the PBF AM process have been discussed in detail with its constituents. Finally, on the basis of prediction through thermo-mechanical models and experimental validation, distortion mitigation/minimisation techniques applied in PBF AM processes have been reviewed to provide a future direction in the field.

The findings of this paper are the future directions for the implementation and modification of the continuum modelling approaches applied to PBF AM processes. On the basis of the extensive review in the domain, gaps are recommended for future work for the betterment of modelling approach.

This paper is limited to review only the modelling approach adopted by the PBF AM processes, i.e. modelling techniques (DEM approach) used for the deposition of powder layer and macro-models at process scale for the prediction of residual stress and distortion in the component. Modelling of microstructure and grain growth has not been included in this paper.

This paper presents an extensive review of the FEM approach adopted for the prediction of residual stress and distortion in the PBF AM processes which sets the platform for the development of distortion mitigation techniques. An extensive review of distortion mitigation techniques has been presented in the last section of the paper, which has not been reviewed yet.

Although many researchers have widely studied additive manufacturing (AM) as one of the most important industrial revolutions, few have presented a bibliometric analysis of the published studies in this area. This paper aims to evaluate AM research trends based on 4607 publications most cited from year 2010 to 2020.

The research methodology is bibliometric indicators and network analysis, including analysis based on keywords, citation analysis, productive journal, related published papers and authors indicators. Two free available software were employed VOSviewer and Bibexcel.

Keywords analysis results indicate that among the AM processes, Selective Laser Melting and Fused Deposition Modeling techniques, are the two processes ranked on top of the techniques employed and studied with 35.76% and 20.09% respectively. The citation analysis by VOSviewer software, reveals that the medical applications field and the fabrication of metal parts are the areas that interest researchers greatly. Different new research niches, as pharmaceutical industry, digital construction and food fabrication are growing topics in AM scientific works. This study reveals that journals “Materials & design”, “Advanced materials”, “Acs applied materials & interfaces”, “Additive manufacturing”, “Advanced functional materials” and “Biofabrication” are the most productive and influential in AM scientific research.

The results and conclusions of this work can be used as indicators of trends in AM research and/or as prospects for future studies in this area.

The purpose of this study is to investigate the effect of two unique processing routes (solvent jetting (SJ) and binder jetting (BJ)), on the green density of printed stainless steel 316L (SS316L) and Nickel (Ni) powders.

In the SJ processing route, a solvent is jetted unto the powder/binder mixture to selectively activate the binder, layer by layer. In the BJ processing route, a solution of the binder mixture is jetted onto the powder bed to selectively bind powder particles. The effects of printing parameters such as layer height, roller speed, shaker speed and nozzle temperature on the green density of printed components are investigated and compared for both processing routes.

Results show that layer height and nozzle temperature affect the relative density of the printed compact for both processing routes. Slightly higher relative densities were achieved via the SJ route, with the overall highest relative density being 42.7% at 100 µm layer height and 70% nozzle temperature for the SS316L components and 43.7% at 150 µm layer height and 90% nozzle temperature for the Ni components, respectively. Results also show an increase in the final sintered relative density with an increase in green (printed) relative density of the solvent jetted SS316L components, with the highest relative density being 87.2%.

The paper studies the influence of printing parameters on the green density of printed SS316L and Ni samples in an unprecedented effort to provide a comparative understanding of the process-property relationships in BJ and SJ of SS316L and Ni components to the additive manufacturing research community.

This study aims to experimentally investigate the effect of the powder material characteristics on the qualities of the binder jetting additive manufacturing parts both before and after post processing (sintering).

Three different types of the 316L stainless steel powder feedstock with various mean particle sizes and size distributions were studied. The influence of the powder particle size distributions and pore sizes on the powder bed packing densities and on the dynamics of the binder droplet-powder bed interactions were characterized. In addition, the surface roughness and densities of these parts both in the green state and after sintering were studied.

The results revealed the significant role of the powder feedstock characteristics on the liquid binder/powder bed interaction and consequently on the dimensional accuracies of the green parts. It was observed that the parts printed with the smaller mean particle sizes resulted in better surface finish and higher final densities after sintering. Furthermore, the hardness of the sintered parts produced with smaller powder particles exhibited higher values compared to the parts fabricated with the larger particles. On the other hand, larger particle sizes are advantageous for various green part qualities including the dimensional accuracies, green part densities and surface roughness.

This study establishes more comprehensive correlations between the powder feedstock characteristics and various quality criteria of the printed binder jetting components in both green and sintered states. These correlation are of critical importance in choosing the optimal process parameters for a given material system.

Binder jetting is a promising route to produce complex copper components for electronic/thermal applications. This paper aims to lay a framework for determining the effects of sintering parameters on the final microstructure of copper parts fabricated through binder jetting.

The knowledge gained from well-established powder metallurgy processes was leveraged to study the densification behaviour of a fine high-purity copper powder (D₅₀ of 3.4 µm) processed via binder jetting, by performing dilatometry and microstructural characterization. The effects of sintering parameters on densification of samples obtained with a commercial water-based binder were also explored.

Sintering started at lower temperature in cold-pressed (∼680 °C) than in binder jetted parts (∼900 °C), because the strain energy introduced by powder compression reduces the sintering activation energy. Vacuum sintering promoted pore closure, resulting in greater and more uniform densification than sintering in argon, as argon pressure stabilizes the residual porosity. About 6.9% residual porosity was obtained with air sintering in the presence of graphite, promoting solid-state diffusion by copper oxide reduction.

This paper reports the first systematic characterization of the thermal events occurring during solid-state sintering of high-purity copper under different atmospheres. The results can be used to optimize the sintering parameters for the manufacturing of complex copper components through binder jetting.

This study provides an up-to-date review of additive manufacturing (AM) technologies and guidance for selecting the most appropriate ones for specific applications, taking into account the main features, strengths, and limitations of the existing options.

A literature review on AM technologies was conducted to assess the current state-of-the-art. This was followed by a closer examination of different AM machines to gain a deeper insight into their main features and operational characteristics. The conclusions and data gathered were used to formulate a classification and decision-support framework.

The findings indicate the building blocks of the selection process for AM technologies. Furthermore, this work shows the suitability of the existing AM technologies for specific cases and points to opportunities for technological and decision-support improvements. Lastly, more standardization in AM would be beneficial for future research.

The proposed framework offers valuable support for decision-makers to select the most suitable AM technologies, as demonstrated through practical examples of its utilization. In addition, it can help researchers identify the limitations of AM by pinpointing applications where existing technologies fail to meet the requirements.

The study offers a novel classification and decision-support framework for selecting AM technologies, incorporating machine characteristics, process features, physical properties of printed parts, and costs as key features to evaluate the potential of AM. Additionally, it provides a deeper understanding of these features as well as the potential opportunities for AM and its impact on various industries.

One of the critical reasons for the nonacceptance of additive manufacturing (AM) processes is the lack of understanding and structured knowledge of design for additive manufacturing (DfAM). This paper aims to assist designers to select the appropriate AM technology for product development or redesign. Using the suggestion provided by the design assist tool, the user’s design alterations depend on their ability to interpret the suggestion into the design without affecting the design’s primary objective.

This research reports the development of a tool that evaluates the efficacy values for all seven major standard AM processes by considering design parameters, benchmark standards within the processes and their material efficacies. In this research, the tool provides analytical and visual approaches to suggestion and assistance. Seventeen design parameters and seven benchmarking standards are used to evaluate the proposed product and design quality value. The full factorial design approach has been used to evaluate the DfAM aspects, design quality and design complexity.

The outcome is evaluated by the product and design quality value, material suit and material-product-design (MPD) value proposed in this work for a comparative assessment of the AM processes for a design. The higher the MPD value, the better the process. The visual aspect of the evaluation uses spider diagrams, which are evaluated analytically to confirm the results’ appropriateness with the proposed methodology.

The data used in the database is assumed to make the study comprehensive. The output aims to help opt for the best process out of the seven AM techniques for better and optimized manufacturing. This, as per the authors’ knowledge, is not available yet.

Objects fabricated using additive manufacturing (AM) technologies often suffer from dimensional accuracy issues and other part-specific problems. This study aims to present a framework for estimating the printability of a computer-aided design (CAD) model that expresses the probability that the model is fabricated correctly via an AM technology for a specific application.

This study predicts the dimensional deviations of the manufactured object per vertex and per part using a machine learning approach. The input to the error prediction artificial neural network (ANN) is per vertex information extracted from the mesh of the model to be manufactured. The output of the ANN is the estimated average per vertex error for the fabricated object. This error is then used along with other global and per part information in a framework for estimating the printability of the model, that is, the probability of being fabricated correctly on a certain AM technology, for a specific application domain.

A thorough experimental evaluation was conducted on binder jetting technology for both the error prediction approach and the printability estimation framework.

This study presents a method for predicting dimensional errors with high accuracy and a completely novel approach for estimating the probability of a CAD model to be fabricated without significant failures or errors that make it inappropriate for a specific application.