Investigation and Improvement of Pushing Dislocation in Ceramsite Sand Three-Dimensional Printing.

Three-dimensional printing (3DP) is considered to be one of the important technologies for a new manufacturing mode. When ceramsite sand is used as a 3DP material to produce a mold (core), the printed layer is prone to deviation from the original location. In this study, the continuous stacking of the printed part deviation was termed as pushing dislocation, and a physical model was designed to investigate the pushing dislocation mechanism. When the gravity of the printing layer and the pressure of the sand scraper decreased, or when the supporting force increased, the angle of the sand scraper and the maximum friction of the prelaying layer on the printed part will reduce the pushing dislocation. To optimize the quality of the ceramsite sand mold, experiments on the pushing dislocation were conducted by altering the recoater speed, layer thickness, and bottom support condition (with or without bottom supporting plate). The sample dimensions were obtained by a 3D imaging scanner, and the gas evolution and ignition loss were measured. The results revealed that the dimensional difference of samples continuously decreased and the pushing dislocation was gradually reduced as the recoater speed and layer thickness increased. The pushing dislocation of the X-direction sample was more severe compared with that of the Y-direction sample. Increasing the layer thickness is an effective way of reducing the pushing dislocation. The bottom supporting plate can reduce the pushing dislocation, but the effect was insignificant.

Mechanism of Shrinkage in Compacted Graphite Iron and Prediction of Shrinkage Tendency.

Shrinkage greatly influences the mechanical and fatigue properties of compacted graphite iron and it is necessary in order to study the causes of shrinkage in compacted graphite iron and to predict it effectively. In this paper, a kind of cylindrical necking test sample was designed to evaluate the shrinkage in compacted graphite iron, and a method to calculate the size of shrinkage was proposed. By observing the microstructure around the shrinkage zone, it is concluded that concentrated shrinkage mainly appears in the solidification region where the dendritic gap is closed, and the isolated shrinkage mainly occurs in the final solidification region, and the supersaturated carbon elements are gathered on the surface of the shrinkage. The cause of shrinkage in compacted graphite iron is caused by its solidification method, where the austenite dendrites and the eutectic clusters are generated close to the melt zone during the solidification process, leading to the inability to feed the shrinkage. Based on the thermodynamic analysis, the equations between the volume change of each phase, solid phase rate, and time during solidification of compacted graphite iron were established to theoretically explain the formation mechanism of the shrinkage. Taking nine parameters such as the chemical elements and characteristic values of thermal analysis as the input nods, a four-layer BP neural network model for predicting the size of shrinkage in compacted graphite iron was constructed, and the R-squared of the model reached 97%, which indicates it could be used to predict the shrinkage tendency.

Development of a Gene Risk Signature for Patients of Pancreatic Cancer.

BACKGROUND: Pancreatic cancer is a highly malignant solid tumor with a high lethality rate, but there is a lack of clinical biomarkers that can assess patient prognosis to optimize treatment. METHODS: Gene-expression datasets of pancreatic cancer tissues and normal pancreatic tissues were obtained from the GEO database, and differentially expressed genes analysis and WGCNA analysis were performed after merging and normalizing the datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were used to screen the prognosis-related genes in the modules with the strongest association with pancreatic cancer and construct risk signatures. The performance of the risk signature was subsequently validated by Kaplan-Meier curves, receiver operating characteristic (ROC), and univariate and multivariate Cox analyses. RESULT: A three-gene risk signature containing CDKN2A, BRCA1, and UBL3 was established. Based on KM curves, ROC curves, and univariate and multivariate Cox regression analyses in the TRAIN cohort and TEST cohort, it was suggested that the three-gene risk signature had better performance in predicting overall survival. CONCLUSION: This study identifies a three-gene risk signature, constructs a nomogram that can be used to predict pancreatic cancer prognosis, and identifies pathways that may be associated with pancreatic cancer prognosis.