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This paper gives a review of the finite element techniques (FE) applied in the area of material processing. The latest trends in metal forming, non‐metal forming and powder metallurgy are briefly discussed. The range of applications of finite elements on the subjects is extremely wide and cannot be presented in a single paper; therefore the aim of the paper is to give FE users only an encyclopaedic view of the different possibilities that exist today in the various fields mentioned above. An appendix included at the end of the paper presents a bibliography on finite element applications in material processing for the last five years, and more than 1100 references are listed.

As newer high performance polymers in mechanical properties become available for material extrusion-based additive manufacturing, determining infill parameter settings becomes more important to achieve both operational and mechanical performance of printed outputs. For the material extrusion of carbon fiber reinforced poly-ether-ether-ketone (CFR-PEEK), this study aims not only to identify the effects of infill parameters on both operational and mechanical performance but also to derive appropriate infill settings through a multicriteria decision-making process considering the conflicting effects.

A full-factorial experimental design to investigate the effects of two major infill parameters (i.e. infill pattern and density) on each performance measure (i.e. printing time, sample mass, energy consumption and maximum tensile load) is separately performed to derive the best infill settings for each measure. Focusing on energy consumption for operational performance and maximum tensile load for mechanical performance, the technique for order preference by similarity to ideal solution is further used to identify the most appropriate infill settings given relative preferences on the conflicting performance measures.

The results show that the honeycomb pattern type with 25% density is consistently identified as the best for the operational performance measures, while the triangular pattern with 100% density is the best for the mechanical performance measure. Moreover, it is suggested that certain ranges of preference weights on operational and mechanical performance can guide the best parameter settings for the overall material extrusion performance of CFR-PEEK.

The findings from this study can help practitioners selectively decide on infill parameters by considering both operational and mechanical aspects and their possible trade-offs.

This paper aims to understand the effect of extrusion conditions on the degree of foaming of polylactic acid (PLA) during three-dimensional (3D) printing. It was also targeted to optimize the slicing parameters for 3D printing and to study how the properties of printed parts are influenced by the extrusion conditions.

This study used a commercially available PLA filament that undergoes chemical foaming. An extrusion 3D printer was used to produce individual extrudates and print samples that were characterized using an optical microscope, scanning electron microscope and custom in-house apparatuses.

The degree of foaming of the extrudates was found to strongly depend on the extrusion temperature and the material feed speed. Higher temperatures significantly increased the number of nucleation sites for the blowing agent as well as the growth rate of micropores. Also, as the material feed speed increased, the micropores were allowed to grow bigger which resulted in higher degrees of foaming. It was also found that, as the degree of foaming increased, the porous parts printed with optimized slicing parameters were lightweight and thermally less conductive.

This study fills the gap in literature where it examines the foaming behavior of individual extrudates as they are extruded. By doing so, this work distinguishes the effect of extrusion conditions from the effect of slicing parameters on the foaming behavior which enhances the understanding of extrusion of chemically foamed PLA.

To provide a selective bibliography for researchers working with bulk material forming (specifically the forging, rolling, extrusion and drawing processes) with sources which can help them to be up‐to‐date.

A range of published (1996‐2005) works, which aims to provide theoretical as well as practical information on the material processing namely bulk material forming. Bulk deformation processes used in practice change the shape of the workpiece by plastic deformations under forces applied by tools and dies.

Provides information about each source, indicating what can be found there. Listed references contain journal papers, conference proceedings and theses/dissertations on the subject.

It is an exhaustive list of papers (1,693 references are listed) but some papers may be omitted. The emphasis is to present papers written in English language. Sheet material forming processes are not included.

A very useful source of information for theoretical and practical researchers in computational material forming as well as in academia or for those who have recently obtained a position in this field.

There are not many bibliographies published in this field of engineering. This paper offers help to experts and individuals interested in computational analyses and simulations of material forming processes.

Extrusion-based additive manufacturing (AM) has been considered as a promising technique to fabricate scaffolds for tissue engineering due to affordability, versatility and ability to print porous structures. The reliability and controllability of the printing process are necessary to produce 3D-printed scaffolds with desired properties and depend on the geometric characteristics such as porosity and pore diameter. The purpose of this study is to develop an analytical model and explore its effectiveness in the prediction of geometric characteristics of 3D-printed scaffolds.

An analytical model was developed to simulate the geometric characteristics of scaffolds produced by extrusion-based AM using fluid mechanics. Polycaprolactone (PCL) was chosen as a scaffold material and was assumed to be a non-Newtonian fluid for the model. The effectiveness of the model was verified through comparison with the experimental results.

A comparison study between simulation and experimental results shows that strut diameter, pore size and porosity of scaffolds can be predicted by using extrusion pressure, temperature, nozzle diameter, nozzle length and printing speed. Simulation results demonstrate that geometric characteristics have a strong relationship with processing parameters, and the model developed in this study can be used for predicting the scaffold properties for the extrusion-based 3D bioprinting process.

The present study provides a prediction model that can simulate the printing process by a simple input of processing parameters. The geometric characteristics can be predicted prior to the experimental verification, and such prediction will reduce the process time and effort when a new material or method is applied.

This paper aims to develop a novel strategy to manufacture poly-lactic acid (PLA) filaments reinforced with Mg particles for fused filament fabrication of porous scaffolds for biomedical applications.

The mixture of PLA pellets and Mg particles was extruded twice, the second time using a precision extruder that produces a filament with zero porosity, constant diameter and homogeneous dispersion of Mg particles. The physico-chemical properties of the extruded filaments were carefully analysed to determine the influence of Mg particles on the depolymerisation of PLA during high temperature extrusion and the optimum melt flow index to ensure printability.

It was found that the addition of a polyethylene glycol (PEG) plasticizer was necessary to allow printing when the weight fraction of Mg was above 4%. It was possible to print porous face-centre cubic scaffolds with good geometrical accuracy and minimum porosity with composite filaments containing PEG.

The new strategy is easily scalable and seems to be very promising to manufacture biodegradable thermoplastic/metal composite filaments for 3D printing that can take advantage of the different properties of both components from the viewpoint of tissue engineering.

Fused Filament Fabrication (FFF) is an extrusion-based manufacturing process using fused thermoplastics. Despite its low cost, the FFF is not extensively used in high-value industrial sectors mainly due to parts' anisotropy (related to the deposition strategy) and residual stresses (caused by successive heating cycles). Thus, this study aims to investigate the process improvement and the optimization of the printed parts.

In this work, a meshless technique – the Radial Point Interpolation Method (RPIM) – is used to numerically simulate the viscoplastic extrusion process – the initial phase of the FFF. Unlike the FEM, in meshless methods, there is no pre-established relationship between the nodes so the nodal mesh will not face mesh distortions and the discretization can easily be modified by adding or removing nodes from the initial nodal mesh. The accuracy of the obtained results highlights the importance of using meshless techniques in this field.

Meshless methods show particular relevance in this topic since the nodes can be distributed to match the layer-by-layer growing condition of the printing process.

Using the flow formulation combined with the heat transfer formulation presented here for the first time within an in-house RPIM code, an algorithm is proposed, implemented and validated for benchmark examples.

Material extrusion is one of the most commonly used approaches within the additive manufacturing processes available. Despite its popularity and related technical advancements, process reliability and quality assurance remain only partially solved. In particular, the surface roughness caused by this process is a key concern. To solve this constraint, experimental plans have been exploited to optimize surface roughness in recent years. However, the latter empirical trial and error process is extremely time- and resource consuming. Thus, this study aims to avoid using large experimental programs to optimize surface roughness in material extrusion.

This research provides an in-depth analysis of the effect of several printing parameters: layer height, printing temperature, printing speed and wall thickness. The proposed data-driven predictive modeling approach takes advantage of Machine Learning (ML) models to automatically predict surface roughness based on the data gathered from the literature and the experimental data generated for testing.

Using ten-fold cross-validation of data gathered from the literature, the proposed ML solution attains a 0.93 correlation with a mean absolute percentage error of 13%. When testing with our own data, the correlation diminishes to 0.79 and the mean absolute percentage error reduces to 8%. Thus, the solution for predicting surface roughness in extrusion-based printing offers competitive results regarding the variability of the analyzed factors.

There are limitations in obtaining large volumes of reliable data, and the variability of the material extrusion process is relatively high.

Although ML is not a novel methodology in additive manufacturing, the use of published data from multiple sources has barely been exploited to train predictive models. As available manufacturing data continue to increase on a daily basis, the ability to learn from these large volumes of data is critical in future manufacturing and science. Specifically, the power of ML helps model surface roughness with limited experimental tests.

THE paper began with a brief historical survey of the cold extrusion process since its first practical use over sixty years ago. The extrusion of special materials such as molybdenum, beryllium and zirconium was described. The problems relating to the production of suitable tooling for the extrusion process and the preparation, heat treatment and lubrication of extrusion billets and slugs were discussed. Following discussion on temperature and speed effects, prestrain of work material, presses for extrusion and component design, the paper concluded with an outline of the applications and advantages of the cold extrusion process.

The purpose of this study was to fabricate silicone products that had different hardnesses and moduli, thus partially addressing the limitations of homogeneous materials whose deformation depends on altered structure or dimensions, and to provide new dimensions for the design of silicone soft structures.

A soft material three-dimensional printing platform with a dual-channel printing capability was designed and built. Using the material extrusion method, material screening was first performed using single-channel printing, followed by dual-channel-regulated printing experiments on products having different hardness and modulus values.

The proportion of additives has an effect on the accuracy of the printed product. Material screening revealed that Sylgard 527 and SE 1700 could be printed without additives. The hardness and mechanical properties of products are related to the percentage in their composition of hard and soft materials. The hardness of the products could be adjusted from 26A to 42A and the Young’s modulus from 0.875 to 2.378 Mpa.

Existing silicone products molded by casting or printing are mostly composed of a single material, whose uniform hardness and modulus cannot meet the demand for differentiated deformation in the structure. The existing multihardness silicone material printing method has the problems of long material mixing time and slow hardness switching and complicated multi-extrusion head switching. In this study, a simple, low-cost and responsive material extrusion-based hardness programmable preparation method for silicone materials is proposed.