Analysis and Research of the High-Cycle Fatigue Fracture Mode Based on Stress Ratio and Residual Stress of Ti-6Al-4V

The content of titanium is about 0.63% in the earth’s crust, and it ranks 10th among all elements. The content of titanium is next to the metal elements of aluminum, iron and magnesium, iron, and magnesium; titanium alloys have low density, high specific strength (the ratio of tensile strength to density), wide working range (−253°C–600°C), and excellent corrosion resistance melting point; the chemical activity of titanium alloy is very high, and it easily reacts with hydrogen, oxygen, and nitrogen, so it is difficult to be smelted and processed, and the processing cost is high. Titanium alloys also have poor thermal conductivity (only 1/5 of iron and 1/15 of aluminum), small deformation coefficient, large friction coefficient, and other characteristics. They are widely used in aircraft fuselage, gas turbine, petrochemical, automotive industry, medical, and other fields for important parts.

Investigation on Orbital EDM for Double-Hole Thin Wall of Integrated Flexible Joint

The thin neck structure of integrated flexible joint is the key factor to realize high-precision navigation in dynamically tuned gyroscope. The thin neck structure is composed of two adjacent circular holes and the thin wall between the two holes. The thin wall is easy to deform under the external force and vibration exerted by the tool when using traditional machining methods such as drilling and boring, and the cutting tools are easily to be damaged for the machining of small holes in superhard materials, inducing high processing cost. Aiming at these problems, the machining method of double-hole thin wall in the step-by-step orbital electrical discharge machining (EDM) with a high rotation speed electrode is proposed. The procedure for EDM of double-hole flexible thin wall is designed, and the process parameters of each step machining are optimized using orthogonal experiment and signal-to-noise ratio analysis. The machining experiments of double-hole thin wall of 3J33B material are proceed using the optimized parameters. The results show that the hole diameter of the double-hole flexible thin wall is 2 mm, the hole depth is 8mm, and the average thickness of the thin wall is about 46.5 μm. The thickness range between the measured point and the average is 1.55 μm, that compared with average thickness of 46.5 μm, the error is less than 3%, the overall thickness is uniform relatively.

Utilization of Melt Fracture Phenomenon for the Preparation of Shark Skin Structured Hydrophobic Film

With the application of biomimetic shark skin microstructures with hydrophobicity in microfluidics, sensors and self-cleaning materials, microstructure processing methods are increasing. The preparation process has higher requirements for processing cost and efficiency. In this paper, linear low-density polyethylene (LLDPE) hydrophobic films were prepared with the help of melt fracture phenomenon. The equipment is a self-made single screw extruder. By adjusting the process parameters, the biomimetic shark skin structured LLDPE films with good hydrophobic property can be obtained. The surface microstructure shape of the product is related to kinds of additive, die temperature and screw speed. When AC5 was selected as an additive, the optimal processing parameter was found to be 160 °C die temperature and 80 r/min screw speed. A contact angle of 133° was obtained in this situation. In addition, the influences of die temperature and screw speed on the size of shark skin structure were also systematically investigated in this paper. It was found that the microstructure surface with hierarchical roughness had a better hydrophobic property.

EXPRESS: Misretrieval but not misrepresentation: A feature misbinding account of post-interpretive effects in number attraction.

Attraction effects in comprehension have reliably shown a grammaticality asymmetry in which mismatching plural attractors confer facilitatory interference for ungrammatical verbs but no processing cost for grammatical verbs (Tanner et al., 2014; Wagers et al., 2009). While this has favored cue-based retrieval accounts of attraction phenomena in comprehension, Patson and Husband (2016) offered offline evidence suggesting comprehenders systematically misrepresent number information in attraction phrases, leaving open the possibility for faulty NP representations later in processing. The current study employs two self-paced reading discourse experiments to test for number attraction misrepresentations in real-time. Specifically, the attraction phrases occurred as embedded direct object phrases, allowing for a direct test of the role of attractor noun number in head noun number misrepresentation (i.e. no number cue from verb). Although no on-line evidence for misrepresentation was found, a third single-sentence RSVP experiment showed error rates to offline probes corroborating the post-interpretive findings from Patson and Husband (2016), suggesting that a search in memory for associative features may not employ the same processes as the formation of dependencies in discourse comprehension. The findings are discussed in the framework of feature misbinding in memory in line with recent post-interpretive accounts of offline comprehension errors.

A Convolutional Neural Network(CNN) Classification To Identify The Presence of Pores in Powder Bed Fusion Images

This study focuses on the detection of seeded porosity during metal additive manufacturing by employing convolutional neural networks (CNN). The aim of the study is to demonstrate the application of Machine Learning (ML) in in-process monitoring. Laser Powder Bed Fusion (LPBF) is a selective laser melting technique used to build complex 3D parts. The current monitoring system in LPBF is inadequate to produce safety-critical parts due to the lack of automated processing of collected data. To assess the efficacy of applying ML to defect detection in LPBF by in-process images, a range of synthetic defects have been designed into cylindrical artefacts to mimic porosity occurring in different locations, shapes, and sizes. Empirical analysis has revealed insights into the importance of accurate labelling strategies required for data-driven solutions. Two labelling strategies based on the computer aided design (CAD) file and X-ray computed tomography (XCT) scan data was formulated. A novel CNN was trained from scratch and optimised by selecting the best values of an extensive range of hyper-parameters by employing Hyperband tuner. The accuracy of the model was 90% when trained using a CAD-assisted labelling, and 97% when using XCT-assisted labelling. The model successfully spotted pores as small as 0.2mm. Experiments revealed that balancing the data set improved the model's precision from 89% to 97% and recall from 85% to 97% when compared to training on an imbalanced data set. We strongly believed that the proposed model would significantly reduce post-processing cost and provide a better base model network for transfer learning of future ML models aimed at LPBF micro-defects detection.

Valorization of Wastewater from Table Olives: NMR Identification of Antioxidant Phenolic Fraction and Microwave Single-Phase Reaction of Sugary Fraction

The table olive industry is producing a huge amount of wastewater, which is a post-processing cost and an environmental concern. The present study aims to valorize this processing by-product to obtain a value-added product, thereby enhancing resource efficiency and contributing to achieving sustainable development goals (SDGs). In this sense, a chemical reaction-based platform was developed to obtain valuable components, such as levulinic acid (LA) and 5-hydromethylfurfural (HMF). The products were then analyzed using NMR identification of the antioxidant phenolic fraction and microwave single-phase reaction of the sugary fraction. According to the results, the highest concentration of phenolic compounds does not correspond to the sample directly obtained from NaOH treatment (S1), indicating that water washing steps (S2–S5) are fundamental to recover phenolic substances. Moreover, glucose was presented in the sugary fraction that can be transformed into levulinic acid by a single-phase reaction under microwave irradiation. The information provided in this manuscript suggests that the wastewater from the olive processing industry can be valorized to obtain valuable products.

Analysis of Different Load Balancing Algorithms in Cloud Computing

In today's world, the IT industry is emerging day by day; therefore, the need for storage and computing is increasing multifold. Cloud computing has transformed the IT sector to much greater heights by virtualizing the systems, thereby reducing cost of the hardware to greater extent. Cloud computing is based on the pay as per use policy. Due to the exponential growth in cloud computing, users demand supplementary services and improved results which makes load balancing a major challenge. Load balancing distributes the workload across multiple nodes to optimize the performance of the system. Various load balancing algorithms exist to provide better resource utilization. This paper gives a brief analysis of load balancing algorithms and also compared these algorithms on the basis of certain metrics like average response time, processing cost, and data servicing time.

Sharing the load: How a personally coloured calculator for grapheme-colour synaesthetes can reduce processing costs

Synaesthesia refers to a diverse group of perceptions. These unusual perceptions are defined by the experience of concurrents; these are conscious experiences that are catalysed by attention to some normally unrelated stimulus, the inducer. In grapheme-colour synaesthesia numbers, letters, and words can all cause colour concurrents, and these are independent of the actual colour with which the graphemes are displayed. For example, when seeing the numeral ‘3’ a person with synaesthesia might experience green as the concurrent irrespective of whether the numeral is printed in blue, black, or red. As a trait, synaesthesia has the potential to cause both positive and negative effects. However, regardless of the end effect, synaesthesia incurs an initial cost when compared with its equivalent example from normal perception; this is the additional processing cost needed to generate the information on the concurrent. We contend that this cost can be reduced by mirroring the concurrent in the environment. We designed the Digital-Colour Calculator (DCC) app, allowing each user to personalise and select the colours with which it displays its digits; it is the first reported example of a device/approach that leverages the concurrent. In this article we report on the reactions to the DCC for a sample of fifty-three synaesthetes and thirty-five non-synaesthetes. The synaesthetes showed a strong preference for the DCC over its normal counterpart. The non-synaesthetes showed no obvious preference. When using the DCC a subsample of the synaesthete group showed consistent improvement in task speed (around 8%) whereas no synaesthete showed a decrement in their speed.

Algal glycobiotechnology: omics approaches for strain improvement

Microalgae has the capability to replace petroleum-based fuels and is a promising option as an energy feedstock because of its fast growth, high photosynthetic capacity and remarkable ability to store energy reserve molecules in the form of lipids and starch. But the commercialization of microalgae based product is difficult due to its high processing cost and low productivity. Higher accumulation of these molecules may help to cut the processing cost. There are several reports on the use of various omics techniques to improve the strains of microalgae for increasing the productivity of desired products. To effectively use these techniques, it is important that the glycobiology of microalgae is associated to omics approaches to essentially give rise to the field of algal glycobiotechnology. In the past few decades, lot of work has been done to improve the strain of various microalgae such as Chlorella, Chlamydomonas reinhardtii, Botryococcus braunii etc., through genome sequencing and metabolic engineering with major focus on significantly increasing the productivity of biofuels, biopolymers, pigments and other products. The advancements in algae glycobiotechnology have highly significant role to play in innovation and new developments for the production algae-derived products as above. It would be highly desirable to understand the basic biology of the products derived using -omics technology together with biochemistry and biotechnology. This review discusses the potential of different omic techniques (genomics, transcriptomics, proteomics, metabolomics) to improve the yield of desired products through algal strain manipulation.