Hazim El-Mounayri

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https://hdl.handle.net/1805/6690

Hazim El-Mounayri has translated his research into practical technology that brings virtual training to tomorrow's manufacturing workforce. The Advanced Virtual Manufacturing Laboratory (AVML), developed with industrial partner Advanced Science and Automation Corp., provides virtual training and education on high-tech Computer Numerically Controlled (CNC) machines. It enables colleges to easily and inexpensively provide students with effective, safe, and highly accessible web-based training on advanced machining tools, equipment and processes.

AVML is a valuable tool for training the local workforce in advanced manufacturing. The system can be used by machine tool manufacturers to provide online training, reducing or eliminating the need for on-site, live training classes for their customers. The system can also be used for machining process verification and optimization. The AVML is so versatile it can run on desktop or laptop personal computers as well as on more sophisticated 3D and fully immersive systems.

The new technology opens the door for effective distance education in disciplines that were traditionally confined to live teaching, including engineering, physics, and science. It is expected to be a major tool for training of Indiana's workforce in advanced manufacturing and attracting talented students to engineering and technology directly from high schools.

Professor El-Mounaryi's use of technology to expand the reach of teaching and training in advanced manufacturing is another practical example of how IUPUI's faculty members are TRANSLATING their RESEARCH INTO PRACTICE.

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Browsing Hazim El-Mounayri by Subject "additive manufacturing"

Now showing 1 - 7 of 7
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    Correlation Between Process Parameters and Mechanical Properties in Parts Printed by the Fused Deposition Modeling Process
    (Springer, 2019) Attoye, Samuel; Malekipour, Ehsan; El-Mounayri, Hazim; Mechanical and Energy Engineering, School of Engineering and Technology
    Fused deposition modeling (FDM) represents one of the most common techniques for rapid prototyping and industrial additive manufacturing (AM). Optimizing the process parameters which significantly impact the mechanical properties is critical to achieving the ultimate final part quality sought by industry today. This work investigates the effect of different process parameters including nozzle temperature, printing speed, and print orientation on Young’s modulus, yield strength, and ultimate strength of the final part for two types of filament, namely, Poly Lactic Acid (PLA) and Acrylonitrile Butadiene Styrene (ABS). Design of Experiments (DOE) is used to determine optimized values of the process parameters for each type of filaments; also, a comparison is made between the mechanical properties of the parts fabricated with the two materials. The results show that Y-axis orientation presents the best mechanical properties in PLA while X-axis orientation is the best orientation to print parts with ABS.
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    Defects, Process Parameters and Signatures for Online Monitoring and Control in Powder-Based Additive Manufacturing
    (Springer, 2018) Malekipour, Ehsan; El-Mounayri, Hazim; Mechanical and Energy Engineering, School of Engineering and Technology
    Additive Manufacturing (AM) is a process that is based on manufacturing parts layer by layer in order to avoid any geometric limitation in terms of creating the desired design. In the early stages of AM development, the goal was just creating some prototypes to decrease the time of manufacturing assessment. But with metal-based AM, it is now possible to produce end-use parts. In powder-based AM, a designed part can be produced directly from the STL file (Standard Tessellation Language/ stereolithography) layer by layer by exerting a laser beam on a layer of powder with thickness between 20 μm and 100 μm to create a section of the part. The Achilles’ heel of this process is generation of some defects, which weaken the mechanical properties and in some cases, these defects may even lead to part failure under manufacturing. This prevents metal-based AM technology from spreading widely while limiting the repeatability and precision of the process. Online monitoring (OM) and intelligent control, which has been investigated prevalently in contemporary research, presents a feasible solution to the aformentioned issues, insofar as it monitors the generated defects during the process and eliminates them in real-time. In this regard, this paper reveals the most frequent and traceable defects which significantly affect quality matrices of the produced part in powder-based AM, predominately focusing on the Selective Laser Sintering (SLS) process. These defects are classified into “Geometry and Dimensions,” “Surface Quality (Finishing),” “Microstructure” and the defects leading to “Weak Mechanical Properties.” In addition, we introduce and classify the most important parameters, which can be monitored and controlled to avoid those defects. Furthermore, these parameters may be employed in some error handling strategies to remove the generated defects. We also introduce some signatures that can be monitored for adjusting the parameters into their optimum value instead of monitoring the defects directly.
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    Design Optimization of Injection Molds with Conformal Cooling for Additive Manufacturing
    (Office of the Vice Chancellor for Research, 2015-04-17) Wu, Tong; Jahan, Suchana A.; Kumaar, Praveen; Tovar, Andres; El-Mounayri, Hazim; Zhang, Yi; Zhang, Jing; Acheson, Doug; Nalim, M. Razi
    Abstract This is a framework for optimizing additive manufacturing of plastic injection molds. The proposed system consists of three modules, namely process and material modeling, multi-scale topology optimization, and experimental testing, calibration and validation. Advanced numerical simulation is implemented for a typical die with conformal cooling channels to predict cycle time, part quality and tooling life. A thermo-mechanical topology optimization algorithm is being developed to minimize the die weight and enhance its thermal performance. The technique is implemented for simple shapes for validation before it is applied to dies with conformal cooling in future work. A method for designing a die with porous material which can be produced in additive manufacturing is developed. Also a comprehensive set of systemic design rules are developed and to be integrated with CAD modeling to automate the process of obtaining viable plastic injection dies with conformal cooling channels. Finally, material modeling using simulation as well as design of experiments is underway for obtaining the material properties and their variations.
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    A Framework for Optimizing Process Parameters in Powder Bed Fusion (PBF) Process Using Artificial Neural Network (ANN)
    (2019) Marrey, Mallikharjun; Malekipour, Ehsan; El-Mounayri, Hazim; Faierson, Eric J.; 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. Research in the PBF process predominantly focuses on the impact of few parameters on the ultimate properties of the printed part. The lack of a systematic approach to optimizing the process parameters for a better performance of given material results in a sub-optimal process. This process needs a comprehensive study of all the influential parameters and their impact on the mechanical and microstructural properties of a fabricated part. Furthermore, there is a need to develop a quantitative system for mapping the material properties and process parameters with the ultimate quality of the fabricated part to achieve improvement in the manufacturing cycle as well as the quality of the final part produced by the PBF process. To address the aforementioned challenges, this research proposes a framework to optimize the process for 316L stainless steel material. This framework characterizes the influence of process parameters on the microstructure and mechanical properties of the fabricated part using a series of experiments. These experiments study the significance of process parameters and their variance as well as study the microstructure and mechanical properties of fabricated parts by conducting tensile, impact, hardness, surface roughness, and densification tests, and ultimately obtain the optimum range of parameters. This would result in a more complete understanding of the correlation between process parameters and part quality. Furthermore, these experiments provide the required data needed to develop an Artificial Neural Network (ANN) model to optimize process parameters (for achieving the desired properties) and estimate fabrication time.
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    Heat Conduction and Geometry Topology Optimization of Support Structure in Laser-based Additive Manufacturing
    (Springer, 2018) Malekipour, Ehsan; Tovar, Andres; El-Mounayri, Hazim; Mechanical Engineering, School of Engineering and Technology
    Laser-based metal additive manufacturing technologies such as Selective Laser Sintering (SLS) and Selective Laser Melting (SLM) allow the fabrication of complex parts by selectively sintering or melting metallic powders layer by layer. Although elaborate features can be produced by these technologies, heat accumulation in overhangs leads to heat stress and warping, affecting the dimensional and geometrical accuracy of the part. This work introduces an approach to mitigate heat stress by minimizing the temperature gradient between the heat-accumulated zone in overhangs and the layers beneath. This is achieved by generating complex support structures that maintain the mechanical stability of the overhang and increase the heat conduction between these areas. The architecture of the complex support structures is obtained by maximizing heat conduction as an objective function to optimize the topology of support structure. This work examines the effect of various geometries on the objective function in order to select a suitable one to consume less material with almost same conduction. Ongoing work is the development of an experimental testbed for verification.
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    Investigation of Layer Based Thermal Behavior in Fused Deposition Modeling Process by Infrared Thermography
    (Elsevier, 2018) Malekipour, Ehsan; Attoye, Samuel; El-Mounayri, Hazim; Mechanical Engineering, School of Engineering and Technology
    There are numerous research efforts that address the monitoring and control of additive manufacturing (AM) processes to improve part quality. Much less research exists on process monitoring and control of Fused Deposition Modeling (FDM). FDM is inherently a thermal process and thus, lends itself to being study by thermography. In this regard, there are various process parameters or process signatures such as built-bed temperature, temperature mapping of parts during deposition of layers, and the nozzle extrusion temperature that may monitor to optimize the quality of fabricated parts. In this work, we applied image based thermography layer by layer with the usage of an infrared camera to investigate the thermal behavior and thermal evolution of the FDM process for the standard samples printed by ABS filament. The combination of the layer based temperature profile plot and the temporal plot has been utilized to understand the temperature distribution and average temperature through the layers under fabrication. This information provides insights for potential modification of the scan strategy and optimization of process parameters in future research, based on the thermal evolution. Accordingly, this can reduce some frequent defects which have roots in thermal characteristics of the deposited layers and also, improve the surface quality and/or mechanical properties of the fabricated parts. In addition, this approach for monitoring the process will allow manufacturers to build, qualify, and certify parts with greater throughput and accelerate the proliferation of products into high-quality applications.
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    A Thermomechanical Analysis of Conformal Cooling Channels in 3D Printed Plastic Injection Molds
    (MDPI, 2018-12) Jahan, Suchana Akter; El-Mounayri, Hazim; Mechanical and Energy Engineering, School of Engineering and Technology
    Plastic injection molding is a versatile process, and a major part of the present plastic manufacturing industry. The traditional die design is limited to straight (drilled) cooling channels, which don't impart optimal thermal (or thermomechanical) performance. With the advent of additive manufacturing technology, injection molding tools with conformal cooling channels are now possible. However, optimum conformal channels based on thermomechanical performance are not found in the literature. This paper proposes a design methodology to generate optimized design configurations of such channels in plastic injection molds. The design of experiments (DOEs) technique is used to study the effect of the critical design parameters of conformal channels, as well as their cross-section geometries. In addition, designs for the "best" thermomechanical performance are identified. Finally, guidelines for selecting optimum design solutions given the plastic part thickness are provided.