Influence of multiple scan fields on the processing of 316L stainless steel using laser powder bed fusion

The sharp growth that additive manufacturing has been showing recently has broadened its application field and resulted in more varied demand of high-volume parts as well as a general increase in part series. The current focus on productivity enhancement of additive manufacturing has imposed the implementation of multiple-laser systems with larger scan fields. Its usage, combined with adequate layer thickness and laser power selection, makes high-volume parts less challenging to obtain. This paper focuses on understanding the influence of using multiple-scan fields for the fabrication of large components, especially on the parts region corresponding to scan field interface. The microstructure as well as mechanical behaviour of the multi-field manufactured samples are compared with parts fabricated using a single-field, for distinct processing parameters. Moreover, given the unreliability of additive manufacturing regarding dimensional and geometrical tolerances with increasing build rates, post-processing metal-cutting operations were studied towards additive manufacturing process hybridization. Despite the typical additive manufacturing process variability, a set of parameters, within testing conditions, could be identified as the most appropriate solution towards mechanical strength enhancement. Nonetheless, porosity levels can significantly impact the ductility of parts, which may be additionally compromised by its occurrence in the scan-field interface region.

Deriving Frequent Itemsets from Lossless Condensed Representation

In data mining, major research topic is frequent itemset mining (FIM). Frequent Itemsets (FIs) usually generating a large amount of Itemsets from database it causing from high memory and long execution time usage. Frequent Closed Itemsets(FCI) and Frequent Maximal Itemsets(FMI) are a reduced lossless representation of frequent itemsets. The FCI allows to decreasing the memory usage and execution time while comparing to FMIs. The whole data of frequent Itemsets(FIs) may be derived from FCIs and FMIs with correct methods. While various study has presented several efficient approach for FCIs and FMIs mining. In sight of this, that we proposed an algorithm called DCFI-Mine for capably derive FIs from Closed FIs and RFMI algorithm derive FMIs to FIs. The advantages of DCFI-Mine algorithm has two features: First, efficiency, different existing algorithm that tends to develop an enormous quantity of Itemsets all through process, DCFI-Mine process the Itemsets straight without candidate generation. But in proposed RFMI multiple scan occurs due to search of item support so efficiency is less than proposed algorithm DCFI-Mine. Second, in terms of losslessness DCFI-Mine and RFMI can discover complete frequent itemset without lapse. Experimental result shows That DCFI-Mine is best deriving FIs in term of memory usage and executions time

Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture

Network neuroscience has relied on a node-centric network model in which cells, populations, and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. Here, we develop an edge-centric network model, which generates the novel constructs of “edge time series” and “edge functional connectivity” (eFC). Using network analysis, we show that at rest eFC is consistent across datasets and reproducible within the same individual over multiple scan sessions. We demonstrate that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap. We go on to show that eFC is systematically and consistently modulated by variation in sensory input. In future work, the edge-centric approach could be used to map the connectional architecture of brain circuits and for the development of brain-based biomarkers of disease and development.