Optimization of selective laser sintering process conditions using stable sintering region approach

Mechanism and application of 3D-printed degradable bioceramic scaffolds for bone repair

Bioceramics have attracted considerable attention in the field of bone repair because of their excellent osteogenic properties, degradability, and biocompatibility. To resolve issues regarding limited formability, recent studies have introduced 3D printing technology for the fabrication of bioceramic bone repair scaffolds. Nevertheless, the mechanisms by which bioceramics promote bone repair and clinical applications of 3D-printed bioceramic scaffolds remain elusive. This review provides an account of the fabrication methods of 3D-printed degradable bioceramic scaffolds. In addition, the types and characteristics of degradable bioceramics used in clinical and preclinical applications are summarized. We have also highlighted the osteogenic molecular mechanisms in biomaterials with the aim of providing a basis and support for future research on the clinical applications of degradable bioceramic scaffolds. Finally, new developments and potential applications of 3D-printed degradable bioceramic scaffolds are discussed with reference to experimental and theoretical studies.

A novel hybrid improved hunger games search optimizer with extreme learning machine for predicting shrinkage of SLS parts

The material constriction is one of the important factors that influence the forming accuracy of selective laser sintering (SLS). Currently, in order to reduce the shrinkage and improve the quality of products, the optimal combination of machining process parameters is mainly determined by numerous experiments. This often takes valuable time and costs a lot, but the results are mediocre. With the development of intelligent optimization algorithms, they are applied in various disciplines for solving complex problems. Hence, for reducing the shrinkage of parts and overcoming the limitation in the optimization of the process parameters, this paper proposes a novel hybrid improved Hunger Game Search algorithm (HGS) with extreme learning machine (ELM) model for predicting the shrinkage of parts. Firstly, the orthogonal experiments were conducted based on the five key process parameters, the obtained parts datasets were divided into the training set and test set. Secondly, the Cube mapping and refracted opposition-based learning strategies are adopted to increase the convergence speed and solution accuracy of HGS. In addition, the regression prediction model was constructed with the improved HGS(IHGS) and ELM, and this model is trained using the training set. Finally, the test set is used to evaluate the trained model and find the optimal combination of process parameters with the lowest shrinkage of parts. The experimental results suggest that the proposed in this paper IHGS-ELM model proposed in this study has high forecasting precision, with the R2 and RMSE are only 0.9124 and 0.2433, respectively. This model can guide the laser sintering process of polyether sulfone (PES) powder.

Setting the Optimal Laser Power for Sustainable Powder Bed Fusion Processing of Elastomeric Polyesters: A Combined Experimental and Theoretical Study

Additive manufacturing (AM) of polymeric materials offers many benefits, from rapid prototyping to the production of end-use material parts. Powder bed fusion (PBF), more specifically selective laser sintering (SLS), is a very promising AM technology. However, up until now, most SLS research has been directed toward polyamide powders. In addition, only basic models have been put forward that are less directed to the identification of the most suited operating conditions in a sustainable production context. In the present combined experimental and theoretical study, the impacts of several SLS processing parameters (e.g., laser power, part bed temperature, and layer thickness) are investigated for a thermoplastic elastomer polyester by means of colorimetric, morphological, physical, and mechanical analysis of the printed parts. It is shown that an optimal SLS processing window exists in which the printed polyester material presents a higher density and better mechanical properties as well as a low yellowing index, specifically upon using a laser power of 17–20 W. It is further highlighted that the current models are not accurate enough at predicting the laser power at which thermal degradation occurs. Updated and more fundamental equations are therefore proposed, and guidelines are formulated to better assess the laser power for degradation and the maximal temperature achieved during sintering. This is performed by employing the reflection and absorbance of the laser light and taking into account the particle size distribution of the powder material.

Process Phenomena and Material Properties in Selective Laser Sintering of Polymers: A Review

Selective laser sintering (SLS) is a powder bed fusion technology that uses a laser source to melt selected regions of a polymer powder bed based on 3D model data. Components with complex geometry are then obtained using a layer-by-layer strategy. This additive manufacturing technology is a very complex process in which various multiphysical phenomena and different mechanisms occur and greatly influence both the quality and performance of printed parts. This review describes the physical phenomena involved in the SLS process such as powder spreading, the interaction between laser beam and powder bed, polymer melting, coalescence of fused powder and its densification, and polymer crystallization. Moreover, the main characterization approaches that can be useful to investigate the starting material properties are reported and discussed.