Effect Of Graphite And Nbc On Mechanical Properties Of Aisi304 Binded Wc

This work studies the role of AISI304 stainless steel as a Co replacement binder for WC-based hardmetals. WC-AISI304 hardmetal powders were produced from raw powders (WC and AISI304) by mechanical alloying technique. The hardmetal powders were then sintered by two sintering methods; vacuum sintering and PHIP sintering. To improve mechanical properties of sintered samples, graphite (Cgr) and NbC were added prior to milling. The results show that -phase (Fe3W3C) formed in the sintered samples during sintering. Cgr addition has enabled to reduce the formation of -phase. As this phase was eliminated, both hardness and fracture toughness of sintered sample were improved. WC grain growth can be inhibited by the addition of NbC. Increasing NbC content led to an increase of hardness but reduce fracture toughness of sintered samples. Besides that, this work also show a higher potential of PHIP in generating higher density of sintered samples compared to vacuum sintering, and hence, improving mechanical properties of WC-AISI304 hardmetals. The Vickers hardness of WC-10AISI304-2Cgr-xNbC (x = 1 - 5) hardmetals produced is in range of 1600 to1660 kg/mm2 and fracture toughness, KIC, from 8.7 to 8.3 MPa.m1/2 by vacuum sintering. However, the same samples produced via PHIP sintering gave the Vickers hardness from 1640 to 1820 kg/mm2 and fracture toughness, KIC, from 10 to 7.3 MPa.m1/2. These values of hardness and fracture toughness are in the intermediate range compared to other systems provided by literatures. The results indicate that AISI304 could be proposed to replace Co binder in order to fabricate cutting inserts

Integration of objective weighting methods for criteria and MCDM methods: application in material selection

Determining weights for criteria is an extremely crucial step in the process of selecting an option based on multiple criteria, also known as Multi-Criteria Decision Making (MCDM). This article presents the combination of five objective weighting methods for criteria with three MCDM methods in the context of material selection. The five objective weighting methods considered are Entropy, MEREC (Method based on the Removal Effects of Criteria), LOPCOW (Logarithmic Percentage Change-driven Objective Weighting), CRITIC (Criteria Importance Through Intercriteria Correlation), and MEAN. The three MCDM methods employed are MARA (Magnitude of the Area for the Ranking of Alternatives), RAM (Root Assessment Method), and PIV (Proximity Indexed Value). Material selection investigations were conducted in three different cases, including lubricant selection for two-stroke engines, material selection for manufacturing screw shafts, and material selection for manufacturing gears. The Spearman's rank correlation coefficient was calculated to assess the stability of ranking the alternatives using different MCDM methods. The combinations of objective weighting methods and MCDM methods were evaluated based on factors such as consistency in identifying the best material type, range, average value, and median of each set of Spearman's rank correlation coefficients. Two significant findings were identified. First, the weights of criteria calculated using LOPCOW method appear to be inversely related to those calculated using the Entropy method. Second, among the three MCDM methods used, MARA was identified as the most suiTable for lubricant selection for two-stroke engines, RAM was found to be the most suiTable for material selection for screw shafts and gears. The best material type in each case was also determine

NearbyPatchCL: Leveraging Nearby Patches for Self-Supervised Patch-Level Multi-Class Classification in Whole-Slide Images

Whole-slide image (WSI) analysis plays a crucial role in cancer diagnosis and treatment. In addressing the demands of this critical task, self-supervised learning (SSL) methods have emerged as a valuable resource, leveraging their efficiency in circumventing the need for a large number of annotations, which can be both costly and time-consuming to deploy supervised methods. Nevertheless, patch-wise representation may exhibit instability in performance, primarily due to class imbalances stemming from patch selection within WSIs. In this paper, we introduce Nearby Patch Contrastive Learning (NearbyPatchCL), a novel self-supervised learning method that leverages nearby patches as positive samples and a decoupled contrastive loss for robust representation learning. Our method demonstrates a tangible enhancement in performance for downstream tasks involving patch-level multi-class classification. Additionally, we curate a new dataset derived from WSIs sourced from the Canine Cutaneous Cancer Histology, thus establishing a benchmark for the rigorous evaluation of patch-level multi-class classification methodologies. Intensive experiments show that our method significantly outperforms the supervised baseline and state-of-the-art SSL methods with top-1 classification accuracy of 87.56%. Our method also achieves comparable results while utilizing a mere 1% of labeled data, a stark contrast to the 100% labeled data requirement of other approaches. Source code: https://github.com/nvtien457/NearbyPatchCLComment: MMM 202

Hydrodynamic Simulations of Circumstellar Envelopes under the Gravitational Influence of a Wide Binary Companion: Comparison Between Circular and Elliptical Orbits

Shapes of circumstellar envelopes around mass losing stars contain information of the very inner region of the envelope where mass loss process takes place. It’s well known that the presence of a binary companion leads to strong influence on the structure of the envelope through orbital motion of the mass losing star and the gravitational interaction of the companion with the stellar wind. To investigate this effect and structures of envelopes, we have performed high resolution hydrodynamic simulations of a wide binary system in a number of orbital configurations. Our simulations clearly show the importance of the equation of state of the gas because in isothermal case the width of the spiral arm is significantly broadened with respect to the ideal gas case, therefore resulting in unrealistic spiral patterns. As the orbital geometry changes from circular to elliptical, our simulation results show that the spiral becomes bifurcated and increasingly asymmetric as indicated in previously published results. In the polar direction, the prominent alternating arcs associated with circular orbital configuration morph into almost continuous circular rings. The physical condition of the gas in the envelope is shown to vary strongly between the spiral arm and inter-arm regions. Our hydrodynamic simulations will be useful to interpret high angular resolution observations of circumstellar envelopes

Large-scale Vietnamese point-of-interest classification using weak labeling

Point-of-Interests (POIs) represent geographic location by different categories (e.g., touristic places, amenities, or shops) and play a prominent role in several location-based applications. However, the majority of POIs category labels are crowd-sourced by the community, thus often of low quality. In this paper, we introduce the first annotated dataset for the POIs categorical classification task in Vietnamese. A total of 750,000 POIs are collected from WeMap, a Vietnamese digital map. Large-scale hand-labeling is inherently time-consuming and labor-intensive, thus we have proposed a new approach using weak labeling. As a result, our dataset covers 15 categories with 275,000 weak-labeled POIs for training, and 30,000 gold-standard POIs for testing, making it the largest compared to the existing Vietnamese POIs dataset. We empirically conduct POI categorical classification experiments using a strong baseline (BERT-based fine-tuning) on our dataset and find that our approach shows high efficiency and is applicable on a large scale. The proposed baseline gives an F1 score of 90% on the test dataset, and significantly improves the accuracy of WeMap POI data by a margin of 37% (from 56 to 93%)

Choosing the best machine tool in mechanical manufacturing

Machine tools are indispensable components and play an important role in mechanical manufacturing. The equipment of machine tools has a huge effect on the operational efficiency of businesses. Each machine tool type is described by many different criteria, such as cost, technological capabilities, accuracy, energy consumption, convenience in operation, safety for workers, working noise, etc. If the selection of machine is only based on one or several criteria, it will be really easy to make mistakes, which means it is not possible to choose the real best machine. A machine is considered to be the best only when it is chosen based on all of its criteria. This work is called multi-criteria decision-making (MCDM). In this study, the selection of machine tools has been done using two different multi-criteria decision-making methods, including the FUCA method (Faire Un Choix Adéquat) and the CURLI method (Collaborative Unbiased Rank List Intergration). These are two methods with very different characteristics. When using the FUCA method, it is necessary to normalize the data and determine the weights for the criteria. Meanwhile, if using the CURLI method, these two things are not necessary. The selection of these two distinct methods is intended to produce the most generalizable conclusions. Three types of machine tool, which are considered in this study, include grinding machine, drilling machine and milling machine. The number of grinders that were offered for selection was twelve, the number of drills that were surveyed in this study was thirteen, while nine were the number of milling machines that were given for selection. The objective of this study is to determine the best solution in each type of machine. The results of ranking the machines are very similar when using the two mentioned methods. Specially, in all the surveyed cases, the two methods FUCA and CURLI always find the same best alternative. Accordingly, it is possible to firmly come to a conclusion that the FUCA method and the CURLI method are equally effective in machine tool selection. In addition, this study has determined the best three machines corresponding to the three different machine type

On the Performance of Cognitive Underlay SIMO Networks over Equally Correlated Rayleigh Fading Channels

The performance of single-input multiple-output (SIMO) cognitive spectrum sharing networks with the presence of equally correlated Rayleigh fading channels is investigated. In particular, based on the truncated infinitive series of cumulative distribution function (CDF) and probability density function (PDF) of the end-to-end signal-to-noise ratios (SNRs), close-form expressions are provided for the system outage performance, bit error rate and ergodic capacity. It is shown that the system performance merely depends on the correlation coefficient between antennas. Monte-Carlo simulations are also contributed to confirm the accuracy of our analysis