Abstract A coal rock cutting model was established using ANSYS/LS-DYNA and the influence of different cutting surfaces was studied. By using the control variable method, simulation experiments are ...conducted on single and multiple conical picks cutting under the different ratios of cutting spacing to the cutting thickness. The variation pattern of the cutting correlation is obtained. The conclusion is that when the ratio is 2-2.5, there is a significant cutting correlation between adjacent picks, and the conical picks have the best cutting performance. As the ratio increases, the cutting correlation between adjacent picks weakens, which is not conducive to the cutting of the pick. This conclusion has important guiding significance for the design of cutting parameters.
Abstract
As China has stepped into a new era of development, the popularity of computer applications is becoming more and more profound. Especially in the machinery manufacturing industry, the ...current market has put forward a higher requirement for the accuracy and toughness of machinery manufacturing, and the traditional artificial manufacturing has not been adapted to the development of the industry. Through numerical control technology, numerical control tool manufacturing process can become very standardized, its accuracy and toughness is also guaranteed. This paper will from the numerical control this point of view, to explore the influence of numerical control cutting parameters on the numerical control tool.
Abstract
Taguchi and Response Surface Methodologies (RSM) for Surface Roughness (SR), and Material Removal Rate (MRR) in end processing of AA6082T6 with tungsten carbide Insert. The Experiments have ...been driven using the Taguchi plan. The cutting boundaries are feed, speed, and profundity of cut. The impact of machining boundaries and to assessed the ideal cuttings condition to surface unpleasantness and material expulsion rate. A second-request model has been work between the cutting limits and the machining limits to recognize out the SR and MRR by using reaction surface strategy. The test outcomes have shown the most basic factor in the surface unpleasantness is speed (31.068%) and in the material evacuation rate is profundity of cut (51.9404%). The anticipated qualities are affirmed by utilizing affirmation tests.
Compared to conventional machining (CM), ultrasonic vibration-assisted machining (UVAM) with high-frequency and small-amplitude has exhibited good cutting performances for advanced materials. In ...recent years, advances in ultrasonic generator, ultrasonic transducer, and horn structures have led to the rapid progress in the development of UVAM. Following this trend, numerous new design requirements and theoretical concepts have been proposed and studied successively, however, very few studies have been conducted from a comprehensive perspective. To address this gap in the literature and understanding the development trend of UVAM, a critical overview of UVAM is presented in this study, covering different vibration-assisted machining styles, device architectures, and theoretical analysis. This overview covers the evolution of typical hardware systems used to achieve vibratory motions from the one-dimensional UVAM to three-dimensional UVAM, the discussion of cutting characteristics with periodic separation between the tools and workpiece and the analysis of processing properties. Challenges for UVAM include ultrasonic vibration systems with high power, large amplitude, and high efficiency, as well as theoretical research on the dynamics and cutting characteristics of UVAM. Consequently, based on the current limitations and challenges, device improvement and theoretical breakthrough play a significant role in future research on UVAM.
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•The evolution of UVAM systems are comprehensively discussed in the paper.•Kinematics are analysed in view of cutting type, including Con-UVAM and R-UVAM.•Cutting characteristics (contact rate, force, surface integrity) are summarized.•Based on advantages and interdisciplinary, directions of development are concluded.
Making kirigami-inspired cuts into a sheet has been shown to be an effective way of designing stretchable materials with metamorphic properties where the 2D shape can transform into complex 3D ...shapes. However, finding the optimal solutions is not straightforward as the number of possible cutting patterns grows exponentially with system size. Here, we report on how machine learning (ML) can be used to approximate the target properties, such as yield stress and yield strain, as a function of cutting pattern. Our approach enables the rapid discovery of kirigami designs that yield extreme stretchability as verified by molecular dynamics (MD) simulations. We find that convolutional neural networks, commonly used for classification in vision tasks, can be applied for regression to achieve an accuracy close to the precision of the MD simulations. This approach can then be used to search for optimal designs that maximize elastic stretchability with only 1000 training samples in a large design space of ∼4×10^{6} candidate designs. This example demonstrates the power and potential of ML in finding optimal kirigami designs at a fraction of iterations that would be required of a purely MD or experiment-based approach, where no prior knowledge of the governing physics is known or available.
Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems. We investigate the ability to ...automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences. Motion-sensor “camera traps” enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2 million-image Snapshot Serengeti dataset. Our deep neural networks automatically identify animals with >93.8% accuracy, and we expect that number to improve rapidly in years to come. More importantly, if our system classifies only images it is confident about, our system can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that of crowdsourced teams of human volunteers, saving >8.4 y (i.e., >17,000 h at 40 h/wk) of human labeling effort on this 3.2 million-image dataset. Those efficiency gains highlight the importance of using deep neural networks to automate data extraction from camera-trap images, reducing a roadblock for this widely used technology. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild.
The growing need to store increasing amounts of renewable energy has recently triggered substantial R&D efforts towards efficient and stable water electrolysis technologies. The oxygen evolution ...reaction (OER) occurring at the electrolyser anode is central to the development of a clean, reliable and emission-free hydrogen economy. The development of robust and highly active anode materials for OER is therefore a great challenge and has been the main focus of research. Among potential candidates, perovskites have emerged as promising OER electrocatalysts. In this study, by combining a scalable cutting-edge synthesis method with time-resolved X-ray absorption spectroscopy measurements, we were able to capture the dynamic local electronic and geometric structure during realistic operando conditions for highly active OER perovskite nanocatalysts. Ba
Sr
Co
Fe
O
as nano-powder displays unique features that allow a dynamic self-reconstruction of the material's surface during OER, that is, the growth of a self-assembled metal oxy(hydroxide) active layer. Therefore, besides showing outstanding performance at both the laboratory and industrial scale, we provide a fundamental understanding of the operando OER mechanism for highly active perovskite catalysts. This understanding significantly differs from design principles based on ex situ characterization techniques.
Nickel-based super alloys are gaining more significance, now-a-days, with extensive applications in aerospace, marine, nuclear reactor and chemical industries. Several characteristics including ...superior mechanical and chemical properties at elevated temperature, high toughness and ductility, high melting point, excellent resistance to corrosion, thermal shocks, thermal fatigue and erosion are primarily responsible for wide domain of application. Nevertheless, machined surface integrity of nickel-based super alloys is a critical aspect which influences functional performance including fatigue life of the component. This review paper presents state-of-the-art on various surface integrity characteristics during machining of nickel-based super alloys. Influence of various cutting parameters, cutting environment, coating, wear and edge geometry of cutting tools on different features of surface integrity has been critically explained. These characteristics encompass surface roughness, defects (surface cavities, metal debris, plucking, smeared material, redeposited material, cracked carbide particles, feed marks, grooves and laps), metallurgical aspects in the form of surface and sub-surface microstructure phase transformation, dynamic recrystallisation and grain refinement and mechanical characteristics such as work hardening and residual stress. Microstructural modification of deformed layer, profile of residual stresses and their influence on fatigue durability have been given significant emphasis. Future research endeavour might focus on development of new grades, advanced processing techniques of the same to ensure their superior stability of microstructure and thermo-mechanical properties along with advanced manufacturing processes like additive manufacturing to achieve highest level of fatigue durability of safety critical components while maintaining acceptable surface integrity and productivity.
•State-of-the-art review of machined surface integrity of nickel-based super alloys.•Effects of various cutting condition on surface integrity critically explained.•Surface topography, microstructure, work hardening and residual stress emphasized.•Surface integrity correlated with fatigue life of safety critical components.
Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this ...revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.
Advances in machine learning and data science hold great potential for the rapid computational screening of solid‐state catalyst materials candidates and to accelerate the computation of potential energy landscapes. In this review, we outline important data science and machine learning concepts and show how these are applied in the field of computational heterogeneous catalysis. We cover topics like data storage and sharing, materials featurization and using machine learning models for predictive and mechanistic studies.
Drilling is the most important machining operation applicable to polymeric composite materials since it is essential for mechanical coupling of parts. Drilling process provokes several issues, ...including localized thermal shock, caused by the presence of abrasive and extremely hard fibres and the low thermal conductivity of composite, which restricts the heat dissipation. Such dangerous issue can be evaluated by examining the process temperature, whose value depends on cutting parameters. Therefore, monitoring of process temperature is indispensable to obtain useful information for machining optimization. In this work the influence of cutting parameters was analysed, measuring the temperature during drilling on tool and in the laminate for both CFRP and GFRP. Temperature trends as a function of cutting speed and feed rate were obtained and dangerous values of cutting parameters were identified. Finally, a preliminary numerical model was developed to simulate the temperature rising in the material during drilling.
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