133 research outputs found

    Automatic steel grades design for Jominy profile achievement through neural networks and genetic algorithms

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    AbstractThe paper proposes an approach to the design of the chemical composition of steel, which is based on neural networks and genetic algorithms and aims at achieving a desired hardenability behavior possibly matching other constraints related to the steel production. Hardenability is a mechanical feature of steel, which is extremely relevant for a wide range of steel applications and refers to the steel capability to improve its hardness following a heat treatment. In the proposed approach, a neural-network-based predictor of the so-called Jominy hardenability profile is exploited, and an optimization problem is formulated, where the optimization function allows taking into account both the desired accuracy in meeting the target Jominy profile and other constraint. The optimization is performed through genetic algorithms. Numerical results are presented and discussed, showing the efficiency of the proposed approach together with its flexibility and easy customization with respect to the user demands and production objectives

    The Science and Technology of 3D Printing

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    Three-dimensional printing, or additive manufacturing, is an emerging manufacturing process. Research and development are being performed worldwide to provide a better understanding of the science and technology of 3D printing to make high-quality parts in a cost-effective and time-efficient manner. This book includes contemporary, unique, and impactful research on 3D printing from leading organizations worldwide

    Novel Approaches for Nondestructive Testing and Evaluation

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    Nondestructive testing and evaluation (NDT&E) is one of the most important techniques for determining the quality and safety of materials, components, devices, and structures. NDT&E technologies include ultrasonic testing (UT), magnetic particle testing (MT), magnetic flux leakage testing (MFLT), eddy current testing (ECT), radiation testing (RT), penetrant testing (PT), and visual testing (VT), and these are widely used throughout the modern industry. However, some NDT processes, such as those for cleaning specimens and removing paint, cause environmental pollution and must only be considered in limited environments (time, space, and sensor selection). Thus, NDT&E is classified as a typical 3D (dirty, dangerous, and difficult) job. In addition, NDT operators judge the presence of damage based on experience and subjective judgment, so in some cases, a flaw may not be detected during the test. Therefore, to obtain clearer test results, a means for the operator to determine flaws more easily should be provided. In addition, the test results should be organized systemically in order to identify the cause of the abnormality in the test specimen and to identify the progress of the damage quantitatively

    Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

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    Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

    Friction Force Microscopy of Deep Drawing Made Surfaces

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    Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

    Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

    Get PDF
    Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

    Advances in Design by Metallic Materials: Synthesis, Characterization, Simulation and Applications

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    Very recently, a great deal of attention has been paid by researchers and technologists to trying to eliminate metal materials in the design of products and processes in favor of plastics and composites. After a few years, it is possible to state that metal materials are even more present in our lives and this is especially thanks to their ability to evolve. This Special Issue is focused on the recent evolution of metals and alloys with the scope of presenting the state of the art of solutions where metallic materials have become established, without a doubt, as a successful design solution thanks to their unique properties

    DIRECT METAL LASER SINTERING OF TI-6AL-4V ALLOY: PROCESS-PROPERTY-GEOMETRY EMPIRICAL MODELING AND OPTIMIZATION

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    DIRECT METAL LASER SINTERING OF TI-6AL-4V ALLOY: PROCESS-PROPERTY-GEOMETRY EMPIRICAL MODELING AND OPTIMIZATIO

    Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

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    talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog

    A systematic review of keys challenges of CO2 transport via pipelines

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    Transport of carbon dioxide (CO2) via pipeline from the point of capture to a geologically suitable location for either sequestration or enhanced hydrocarbon recovery is a vital aspect of the carbon capture and storage (CCS) chain. This means of CO2 transport has a number of advantages over other means of CO2 transport, such as truck, rail, and ship. Pipelines ensure continuous transport of CO2 from the capture point to the storage site, which is essential to transport the amount of CO2 captured from the source facilities, such as fossil fuel power plants, operating in a continuous manner. Furthermore, using pipelines is regarded as more economical than other means of CO2 transport The greatest challenges of CO2 transport via pipelines are related to integrity, flow assurance, capital and operating costs, and health, safety and environmental factors. Deployment of CCS pipeline projects is based either on point-to-point transport, in which case a specific source matches a specific storage point, or through the development of pipeline networks with a backbone CO2 pipeline. In the latter case, the CO2 streams, which are characterised by a varying impurity level and handled by the individual operators, are linked to the backbone CO2 pipeline for further compression and transport. This may pose some additional challenges. This review involves a systematic evaluation of various challenges that delay the deployment of CO2 pipeline transport and is based on an extensive survey of the literature. It is aimed at confidence-building in the technology and improving economics in the long run. Moreover, the knowledge gaps were identified, including lack of analyses on a holistic assessment of component impurities, corrosion consideration at the conceptual stage, the effect of elevation on CO2 dense phase characteristics, permissible water levels in liquefied CO2, and commercial risks associated with project abandonment or cancellation resulting from high project capital and operating costs
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