Browsing by Author "El-Mounayri, Hazim"

Now showing 1 - 10 of 81
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    A framework for graph-base neural network using numerical simulation of metal powder bed fusion for correlating process parameters and defect generation
    (Elsevier, 2022) Akter Jahan, Suchana; Al Hasan, Mohammad; El-Mounayri, Hazim; Computer Science, Luddy School of Informatics, Computing, and Engineering
    Powder bed fusion (PBF) is the most common technique used for metal additive manufacturing. This process involves consolidation of metal powder using a heat source such as laser or electron beam. During the formation of three-dimensional(3D) objects by sintering metal powders layer by layer, many different thermal phenomena occur that can create defects or anomalies on the final printed part. Similar to other additive manufacturing techniques, PBF has been in practice for decades, yet it is still going through research and development endeavors which is required to understand the physics behind this process. Defects and deformations highly impact the product quality and reliability of the overall manufacturing process; hence, it is essential that we understand the reason and mechanism of defect generation in PBF process and take appropriate measures to rectify them. In this paper, we have attempted to study the effect of processing parameters (scanning speed, laser power) on the generation of defects in PBF process using a graph-based artificial neural network that uses numerical simulation results as input or training data. Use of graph-based machine learning is novel in the area of manufacturing let alone additive manufacturing or powder bed fusion. The outcome of this study provides an opportunity to design a feedback controlled in-situ online monitoring system in powder bed fusion to reduce printing defects and optimize the manufacturing process.
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    A Novel Framework for Predictive Modeling and Optimization of Powder Bed Fusion Process
    (MDPI, 2021-10) Marrey, Mallikharjun; Malekipour, Ehsan; El-Mounayri, Hazim; Faierson, Eric J.; Agarwal, Mangilal; Mechanical and Energy Engineering, School of Engineering and Technology
    Powder bed fusion (PBF) process is a metal additive manufacturing process which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for optimizing a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for optimizing the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters -- i.e., laser specifications -- and mechanical properties and how to achieve parts with high density (> 98%) as well as better ultimate mechanical properties. In this paper, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage of 10.236% that are used to optimize process parameters in accordance with user or manufacturer requirements. These models use support vector regression, random forest regression, and neural network techniques. It is shown that the intelligent selection of process parameters using these models can achieve an optimized density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.
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    A Novel Framework of Developing a Predictive Model for Powder Bed Fusion Process
    (Mary Ann Liebert, 2024) Marrey, Mallikharjun; Malekipour, Ehsan; El-Mounayri, Hazim; Faierson, Eric J.; Agarwal, Mangilal; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    The powder bed fusion (PBF) process is a metal additive manufacturing process, which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for predictive modeling of a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for studying the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters that is, laser specifications and mechanical properties, and how to obtain an optimum range of volumetric energy density for producing parts with high density (>99%), as well as better ultimate mechanical properties. In this article, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage (i.e., around 10%), which are used for process parameter selection in accordance with user or manufacturer part performance requirements. These models are based on techniques such as support vector regression, random forest regression, and neural network. It is shown that the intelligent selection of process parameters using these models can achieve a high density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.
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    Advanced Virtual Manufacturing Lab for Research, Training, & Education
    (Office of the Vice Chancellor for Research, 2010-04-09) El-Mounayri, Hazim
    The research formed a base for innovative technology that was used to develop a product on its way to commercialization. The new product provides effective and integrated tool for training and education in advanced manufacturing. It is based on sound e-learning pedagogy and highly effective and integrated virtual reality learning environment.
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    AFM-Based Fabrication of Nanofluidic Device for Medical Application
    (Office of the Vice Chancellor for Research, 2014-04-11) Promyoo, Rapeepan; El-Mounayri, Hazim; Karingula, Varun Kumar
    Recent developments in science and engineering have advanced the atomic manufacture of nanoscale structures, allowing for improved high-performance technologies. Among them, AFM-based nanomachining is considered a potential manufacturing tool for operations including machining, patterning, and assembling with in situ metrology and visualization. In this work, atomic force microscope (AFM) is employed in the fabrication of nanofluidic device for DNA stretching application. Nanofluidic channels with various depths and widths are fabricated using AFM indentation and scratching techniques. To introduce the fluid inside the nanochannels, microchannels are made on both sides of the nanochannels. Photolithography technique is used to fabricate microfluidic channels on silicon wafers. A 3D Molecular Dynamics (MD) model is used to guide the design and fabrication of nanodevices through nanoscratching. The correlation between the scratching conditions, including applied force, scratching depth, and distant between any two scratched grooves and the defect mechanism in the substrate/workpiece is investigated. The MD model allows proper process parameter identification resulting in more accurate nanochannel size.
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    AFM-Based Nanofabrication: Modeling, Simulation, and Experimental Verification
    (Office of the Vice Chancellor for Research, 2013-04-05) Promyoo, Rapeepan; El-Mounayri, Hazim; Karingula, Varun Kumar; Varahramyan, Kody
    Recent developments in science and engineering have advanced the fabrication techniques for micro/ nanodevices. Among them, atomic force microscope (AFM) has already been used for nanomachining and fabrication of micro/nanodevices. In this paper, a computational model for AFM-based nanofabrication processes is being developed. Molecular Dynamics (MD) technique is used to model and simulate mechanical indentation and scratching at the nanoscale. The effects of AFM-tip radius and crystal orientation are investigated. The simulation is also used to study the effect of the AFM tip speed on the indentation force at the interface between the tip and the substrate/workpiece. The material deformation and indentation geometry are extracted from the final locations of atoms, which are displaced by the rigid indenter. Material properties including modulus of elasticity and hardness are estimated. It is found that properties vary significantly at the nanoscale. AFM is used to conduct actual nanoindentation and scratching, to validate the MD simulation. Qualitative agreement is found. Finally, AFM-based fabrication of nanochannels/nanofluidic devices is conducted using different applied forces, scratching length, and feed rate.
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    AFM-Based Nanofabrication: Modeling, Simulation, and Experimental Verification
    (Office of the Vice Chancellor for Research, 2012-04-13) Promyoo, Rapeepan; El-Mounayri, Hazim; Varahramyan, Kody
    Recent developments in science and engineering have advanced the fabrication techniques for micro/nanodevices. Among them, atomic force microscope (AFM) has already been used for nanomachining and fabrication of micro/nanodevices. In this research, a multi-scale computational model for AFM-based nanofabrication processes is being developed. Molecular Dynamics (MD) technique was used to model and simulate mechanical indentation and scratching at the nanoscale. MD simulation represents itself as a viable alternative to the expensive traditional experimental approach, which can be used to study the effects of various indentation variables in a much more cost effective way. The effects of workpiece materials, AFM-tip materials, AFM-tip radius, as well as crystal ori entations were investigated. The simulation allows for prediction of the indentation forces at the interface between an indenter and a workpiece. Also, the MD simulation was used to study the effects of speed on the indentation force. The material deformation and indentation geometry are extracted based on the final locations of atoms, which are displaced by the rigid indenter. Material properties including modulus of elasticity and friction coefficient are presented. AFM was used to conduct actual indentation and scratching at the nanoscale, and provide measurements to validate the predictions from the MD simulation. Qualitative agreement was found between the simulation and actual AFM-based nanomachining.
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    AI Based Modelling and Optimization of Turning Process
    (2012-08) Kulkarni, Ruturaj Jayant; El-Mounayri, Hazim; Anwar, Sohel; Wasfy, Tamer
    In this thesis, Artificial Neural Network (ANN) technique is used to model and simulate the Turning Process. Significant machining parameters (i.e. spindle speed, feed rate, and, depths of cut) and process parameters (surface roughness and cutting forces) are considered. It is shown that Multi-Layer Back Propagation Neural Network is capable to perform this particular task. Design of Experiments approach is used for efficient selection of values of parameters used during experiments to reduce cost and time for experiments. The Particle Swarm Optimization methodology is used for constrained optimization of machining parameters to minimize surface roughness as well as cutting forces. ANN and Particle Swarm Optimization, two computational intelligence techniques when combined together, provide efficient computational strategy for finding optimum solutions. The proposed method is capable of handling multiple parameter optimization problems for processes that have non-linear relationship between input and output parameters e.g. milling, drilling etc. In addition, this methodology provides reliable, fast and efficient tool that can provide suitable solution to many problems faced by manufacturing industry today.
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    Analysis of Process Induced Shape Deformations and Residual Stresses in Composite Parts during Cure
    (2019-05) Patil, Ameya S.; Dalir, Hamid; El-Mounayri, Hazim; Zhang, Jing
    Process induced dimensional changes in composite parts has been the topic of interest for many researchers. The residual stresses that are induced in composite laminates during curing process while the laminate is in contact with the process tool often lead to dimensional variations such as spring-in of angles and warpage of flat panels. The traditional trial-and-error approach can work for simple geometries, but composite parts with complex shapes require more sophisticated models. When composite laminates are subjected to thermal stresses, such as the heating and cooling processes during curing, they can become distorted as the in-plane and the throughthickness coeffcients of thermal expansion are di erent, as well as chemical shrinkage of the resin, usually cause spring-in. Deformed components can cause problems during assembly, which significantly increases production costs and affects performance. This thesis focuses on predicting these shape deformations using software simulation of composite manufacturing and curing. Various factors such as resin shrinkage, degrees of cure, difference between through thickness coeffcient of thermal expansion of the composite laminate are taken into the consideration. A cure kinetic model is presented which illustrates the matrix behavior during cure. The results obtained using the software then were compared with the experimental values of spring-in from the available literature. The accuracy of ACCS package was validated in this study. Analyzing the effects of various parameters of it was estimated that 3D part simulation is an effective and cost and time saving method to predict nal shape of the composite part.
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    Application of an innovative MBSE (SysML-1D) co-simulation in healthcare
    (2018-05) Kalvit, Kalpak; El-Mounayri, Hazim