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Sökning: hsv:(TEKNIK OCH TEKNOLOGIER) hsv:(Maskinteknik) > Malmö universitet

  • Resultat 1-10 av 128
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1.
  • Fisk, Martin, 1981-, et al. (författare)
  • Coupled electromagnetic-thermal solution strategy for induction heating of ferromagnetic materials
  • 2022
  • Ingår i: Applied Mathematical Modelling. - : Elsevier. - 0307-904X .- 1872-8480. ; 111, s. 818-835
  • Tidskriftsartikel (refereegranskat)abstract
    • Induction heating is used in many industrial applications to heat electrically conductive materials. The coupled electromagnetic-thermal induction heating process is non-linear in general, and for ferromagnetic materials it becomes challenging since both the electromagnetic and the thermal responses are non-linear. As a result of the existing non-linearities, simulating the induction heating process is a challenging task. In the present work, a coupled transient electromagnetic-thermal finite element solution strategy that is appropriate for modeling induction heating of ferromagnetic materials is presented. The solution strategy is based on the isothermal staggered split approach, where the electromagnetic problem is solved for fixed temperature fields and the thermal problem for fixed heat sources obtained from the electromagnetic solution. The modeling strategy and the implementation are validated against induction heating experiments at three heating rates. The computed temperatures, that reach above the Curie temperature, agree very well with the experimental results.
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2.
  • Khoshkangini, Reza, 1984-, et al. (författare)
  • A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction
  • 2023
  • Ingår i: Sensors. - Basel : MDPI. - 1424-8220. ; 23:12
  • Tidskriftsartikel (refereegranskat)abstract
    • Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system's effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.
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3.
  • Dzhusupova, Rimman, et al. (författare)
  • The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects
  • 2022
  • Ingår i: Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022. - New York, NY, USA : ACM. ; , s. 124-135
  • Konferensbidrag (refereegranskat)abstract
    • Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully.
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4.
  • Dzhusupova, Rimman, et al. (författare)
  • Using artificial intelligence to find design errors in the engineering drawings
  • 2023
  • Ingår i: Journal of Software: Evolution and Process. - : Wiley. - 2047-7481 .- 2047-7473. ; 35:12
  • Tidskriftsartikel (refereegranskat)abstract
    • Artificial intelligence is increasingly becoming important to businesses because many companies have realized the benefits of applying machine learning (ML) and deep learning (DL) in their operations. ML and DL have become attractive technologies for organizations looking to automate repetitive tasks to reduce manual work and free up resources for innovation. Unlike rule-based automation, typically used for standardized and predictable processes, machine learning, especially deep learning, can handle more complex tasks and learn over time, leading to greater accuracy and efficiency improvements. One of such promising applications is to use AI to reduce manual engineering work. This paper discusses a particular case within McDermott where the research team developed a DL model to do a quality check of complex blueprints. We describe the development and the final product of this case—AI-based software for the engineering, procurement, and construction (EPC) industry that helps to find the design mistakes buried inside very complex engineering drawings called piping and instrumentation diagrams (P&IDs). We also present a cost-benefit analysis and potential scale-up of the developed software. Our goal is to share the successful experience of AI-based product development that can substantially reduce the engineering hours and, therefore, reduce the project's overall costs. The developed solution can also be potentially applied to other EPC companies doing a similar design for complex installations with high safety standards like oil and gas or petrochemical plants because the design errors it captures are common within this industry. It also could motivate practitioners and researchers to create similar products for the various fields within engineering industry.
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5.
  • Fabijan, Aleksander, et al. (författare)
  • The Online Controlled Experiment Lifecycle
  • 2020
  • Ingår i: IEEE Software. - : IEEE. - 0740-7459 .- 1937-4194. ; 37:2, s. 60-67
  • Tidskriftsartikel (refereegranskat)abstract
    • Online Controlled Experiments (OCEs) enable an accurate understanding of customer value and generate millions of dollars of additional revenue at Microsoft. Unlike other techniques for learning from customers, OCEs establish an accurate and causal relationship between a change and the impact observed. Although previous research describes technical and statistical dimensions, the key phases of online experimentation are not widely known, their impact and importance are obscure, and how to establish OCEs in an organization is underexplored. In this paper, using a longitudinal in-depth case study, we address this gap by (1) presenting the Experiment Lifecycle, and (2) demonstrating with four example experiments their profound impact. We show that OECs help optimize infrastructure needs and aid in project planning and measuring team efforts, in addition to their primary goal of accurately identifying what customers value. We conclude that product development should fully integrate the Experiment Lifecycle to benefit from the OCEs.
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6.
  • Banks-Sills, Leslie, et al. (författare)
  • Strain Driven Transport for Bone Modeling at the Periosteal Surface
  • 2011
  • Ingår i: Mathematical Biosciences. - : Elsevier. - 0025-5564 .- 1879-3134. ; 230:1, s. 37-44
  • Tidskriftsartikel (refereegranskat)abstract
    • Bone modeling and remodeling has been the subject of extensive experimental studies. There have been several mathematical models proposed to explain the observed behavior, as well. A different approach is taken here in which the bone is treated from a macroscopic view point. In this investigation, a one-dimensional analytical model is used to shed light on the factors which play the greatest role in modeling or growth of cortical bone at the periosteal surface. It is presumed that bone growth is promoted when increased amounts of bone nutrients, such as nitric oxide synthase (NOS) or messenger molecules, such as prostaglandin E2 (PGE2), seep out to the periosteal surface of cortical bone and are absorbed by osteoblasts. The transport of the bone nutrients is assumed to be a strain controlled process. Equations for the flux of these nutrients are written for a one-dimensional model of a long bone. The obtained partial differential equation is linearized and solved analytically. Based upon the seepage of nutrients out of the bone, the effect of loading frequency, number of cycles and strain level is examined for several experiments that were found in the literature. It is seen that bone nutrient seepage is greatest on the tensile side of the bone; this location coincides with the greatest amount of bone modeling
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7.
  • Bjerkén, Christina, et al. (författare)
  • Simulation of corrosion fatigue crack growth under mixed-mode loading
  • 2008
  • Ingår i: Engineering Fracture Mechanics. - : Elsevier BV. - 1873-7315 .- 0013-7944. ; 75:3-4, s. 440-451
  • Tidskriftsartikel (refereegranskat)abstract
    • The kinking of a corrosion crack due to mixed-mode fatigue loading is studied using an adaptive finite element procedure. The rate of material dissolution is assumed to be proportional to the stretching of the corroding surface. The dissolution of material is governed by a corrosion law, where no criterion is needed for neither crack growth nor growth direction. The problem is treated as a general moving boundary problem. The kink angles are found to be in very good agreement with results for sharp cracks using criteria reported in the literature.
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8.
  • Ericsson, A., et al. (författare)
  • Transient nucleation in selective laser melting of Zr-based bulk metallic glass
  • 2020
  • Ingår i: Materials & design. - : Elsevier. - 0264-1275 .- 1873-4197. ; 195
  • Tidskriftsartikel (refereegranskat)abstract
    • The crystallization rate during selective laser melting (SLM) of bulk metallic glasses (BMG) is a critical factor in maintaining the material's amorphous structure. To increase the understanding of the interplay between the SLM process and the crystallization behavior of BMGs, a numerical model based on the classical nucleation theory has been developed that accounts for the rapid temperature changes associated with SLM. The model is applied to SLM of a Zr-based BMG and it is shown that the transient effects, accounted for by the model, reduce the nucleation rate by up to 15 orders of magnitude below the steady-state nucleation rate on cooling, resulting in less nuclei during the build process. The capability of the proposed modelling approach is demonstrated by comparing the resulting crystalline volume fraction to experimental findings. The agreement between model predictions and the experimental results clearly suggests that transient nucleation effects must be accounted for when considering the crystallization rate during SLM processing of BMGs.
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9.
  • Nigro, Claudio F., Dr. (författare)
  • Phase-field modeling of stress-induced precipitation and kinetics in engineering metals
  • 2020
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • The formation of brittle compounds in metals operating in corrosive environments can be a tremendous source of embrittlement for industrial structures and such phenomenon is commonly enhanced in presence of stresses. To study this type of microstructural change modeling is preferred to experiment to reduce costs and prevent undesirable environmental impacts. This thesis aims at developing an engineering approach to model stress-induced precipitation, especially near stress concentrators, e.g. crack tips, for multi-phase and polycrystalline metals, with numerical efficiency.In this thesis, four phase-field models are developed and applied on stress-induced hydride precipitation in zirconium and titanium alloys. The energy of the system is minimized through the time-dependent Ginzburg-Landau equation, which provides insights to the kinetics of the phenomenon. In these models, the driving force for precipitation is the coupling between the applied stress and the phase transformation-induced dilatation of the system. Models 1-3 implicitly incorporate near crack-tip stress fields by using linear elastic fracture mechanics so that only the phase-field equation is solved numerically with the finite volume method, reducing the computational costs. Phase transformation is investigated for intragranular, intergranular and interphase cracks in single- and two-phase materials by considering isotropy and some degrees of anisotropy, grain/phase boundary energy, different transition orders and solid solubility limit. Model 4 allows representing anisotropy connected to lattice mismatch and the orientation of the precipitates influenced by the applied stress. The model is employed through the finite element program Abaqus, where the fully coupled thermo-mechanical solving method is applied to the coupled mechanical/phase-field problem. Hydride growth is observed to follow the near-crack tip hydrostatic stress contours and can reach a steady state for specific conditions. The relation between hydride formation kinetics and material properties, and stress relaxation are well-reflected in the results.With the presented approaches, precipitation kinetics including different kinds of defects, multi-phase microstructures, phase/grain boundaries, order transitions and loading modes can be successfully captured with low computational costs. They could therefore contribute to the numerical efficiency of multi-scale environment-assisted embrittlement prediction schemes within commercial software serving engineering projects. 
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10.
  • Yang, Yanjie, et al. (författare)
  • Transport dynamics of droplet impact on the wedge-patterned biphilic surface
  • 2020
  • Ingår i: Experimental Thermal and Fluid Science. - : Elsevier BV. - 0894-1777 .- 1879-2286. ; 113
  • Tidskriftsartikel (refereegranskat)abstract
    • Droplet impact on biphilic surfaces with a wettability contrast has been intensively studied in recent years. In this work the effects of tilting and apex angles on droplet transport dynamics after impacting on a wedge-patterned biphilic surface at low Weber numbers were investigated experimentally. The biphilic surface was fabricated by applying a hydrophobic polymer coating on a bare silicon surface. According to the experimental results, a larger apex angle below 67.4° can accelerate the droplet effectively at first. Then the friction force controls the droplet movement and reduces the speed. The tilting angle along the hydrophilic direction activates the droplet. If the gravity component is opposite to the hydrophilic direction and the tilting angle is over 15°, the droplet can hardly move toward the hydrophilic area. By modeling the hydrodynamics of the droplet movement after impact on a biphilic surface with assumptions of no evaporation, no Marangoni effect, negligible dynamic contact angle variation and negligible rotation effect, the surface tension values versus the position at different apex angles are derived. The predicted position versus time trends agree well with the experimental data. This study aims to provide a better understanding of the mechanisms of droplet hydrodynamics on wedge-patterned biphilic surfaces at low Weber numbers.
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