Hazim El-Mounayri

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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.


Recent Submissions

Now showing 1 - 10 of 34
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    Structural and Electrochemical Properties of the High Ni Content Spinel LiNiMnO4
    (MDPI, 2021) Li, Tianyi; Chang, Kai; Hashem, Ahmed M.; Abdel-Ghany, Ashraf E.; El-Tawil, Rasha S.; Wang, Hua; El-Mounayri, Hazim; Tovar, Andres; Zhu, Likun; Julien, Christian M.; Mechanical and Energy Engineering, School of Engineering and Technology
    This work presents a contribution to the study of a new Ni-rich spinel cathode material, LiNiMnO4, for Li-ion batteries operating in the 5-V region. The LiNiMnO4 compound was synthesized by a sol-gel method assisted by ethylene diamine tetra-acetic acid (EDTA) as a chelator. Structural analyses carried out by Rietveld refinements and Raman spectroscopy, selected area electron diffraction (SAED) and X-ray photoelectron (XPS) spectroscopy reveal that the product is a composite (LNM@NMO), including non-stoichiometric LiNiMnO4-δ spinel and a secondary Ni6MnO8 cubic phase. Cyclic voltammetry and galvanostatic charge-discharge profiles show similar features to those of LiNi0.5Mn1.5O4 bare. A comparison of the electrochemical performances of 4-V spinel LiMn2O4 and 5-V spinel LiNi0.5Mn1.5O4 with those of LNM@NMO composite demonstrates the long-term cycling stability of this new Ni-rich spinel cathode. Due to the presence of the secondary phase, the LNM@NMO electrode exhibits an initial specific capacity as low as 57 mAh g−1 but shows an excellent electrochemical stability at 1C rate for 1000 cycles with a capacity decay of 2.7 × 10−3 mAh g−1 per cycle.
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    Nanostructured Molybdenum-Oxide Anodes for Lithium-Ion Batteries: An Outstanding Increase in Capacity
    (MDPI, 2021) Wang, Hua; Li, Tianyi; Hashem, Ahmed M.; Abdel-Ghany, Ashraf E.; El-Tawil, Rasha S.; Abuzeid, Hanaa M.; Coughlin, Amanda; Chang, Kai; Zhang, Shixiong; El-Mounayri, Hazim; Tovar, Andres; Zhu, Likun; Julien, Christian M.; Mechanical and Energy Engineering, School of Engineering and Technology
    This work aimed at synthesizing MoO3 and MoO2 by a facile and cost-effective method using extract of orange peel as a biological chelating and reducing agent for ammonium molybdate. Calcination of the precursor in air at 450 °C yielded the stochiometric MoO3 phase, while calcination in vacuum produced the reduced form MoO2 as evidenced by X-ray powder diffraction, Raman scattering spectroscopy, and X-ray photoelectron spectroscopy results. Scanning and transmission electron microscopy images showed different morphologies and sizes of MoOx particles. MoO3 formed platelet particles that were larger than those observed for MoO2. MoO3 showed stable thermal behavior until approximately 800 °C, whereas MoO2 showed weight gain at approximately 400 °C due to the fact of re-oxidation and oxygen uptake and, hence, conversion to stoichiometric MoO3. Electrochemically, traditional performance was observed for MoO3, which exhibited a high initial capacity with steady and continuous capacity fading upon cycling. On the contrary, MoO2 showed completely different electrochemical behavior with less initial capacity but an outstanding increase in capacity upon cycling, which reached 1600 mAh g−1 after 800 cycles. This outstanding electrochemical performance of MoO2 may be attributed to its higher surface area and better electrical conductivity as observed in surface area and impedance investigations.
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    Optimization of Chessboard Scanning Strategy Using Genetic Algorithm in Multi-Laser Additive Manufacturing Process
    (ASME, 2021-02) Malekipour, Ehsan; Valladares, Homero; Shin, Yung; El-Mounayri, Hazim; Mechanical and Energy Engineering, School of Engineering and Technology
    Residual stress and manufacturing time are two serious challenges that hinder the widespread industry adoption and implementation of the powder-bed fusion (PBF) process. Commercial Multi-Laser PBF (ML-PBF) systems have been developed by several vendors in recent years, which dramatically increase the production rate by employing more heat sources (up to 4 laser beams). Although numerous research works conducted toward mitigation of the effects of residual stress on printed parts in the Single Laser PBF (SL-PBF) process, no research work on this topic has been reported for the ML-PBF process to date. One of the most efficient real-time approaches to mitigate the influence of residual stress and as such the process lead time effectively is to improve the scanning strategy. This approach can be also implemented effectively in the ML-PBF process. In this work, we extend the previously developed GAMP (Genetic Algorithm Maximum Path) strategy for optimizing the scanning path in ML-PBF. The E-GAMP (the Extended GAMP) strategy manipulates the printing topology of the islands and generates more thermally efficient scanning patterns for the chessboard scanning strategy in ML-PBF. This strategy extends the single thermal heat source to multiple ones (2 as well as 3 lasers). To validate the effectiveness of the proposed strategy, we simulate the thermal distribution throughout a simple rectangular layer by ABAQUS for both the traditional successive scanning strategy and the E-GAMP strategy. The results demonstrate that the E-GAMP strategy considerably decreases the manufacturing time while it reduces the maximum temperature gradient, or in other words, generates a more uniform temperature distribution throughout the exposure layer.
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    Integrated System Model of District Cooling for Energy Consumption Optimization
    (ECOS, 2020-11) Dalvi, Akshay S.; Razban, Ali; El-Mounayri, Hazim; El-Mekkawy, Tarek; Promyoo, Rapeepan; Mechanical and Energy Engineering, School of Engineering and Technology
    The successful modeling of a multi-plant district cooling (DC) system presents several challenges in integrating system level requirements with engineering analysis for verification and optimization. Currently, the ability to predict the behavior and performance parameters such as chilled water temperature difference, annual energy consumption, and central chiller plant coefficient of performance (COP) of the dynamic system is limited. Effective modeling and efficient simulation are required when it comes to complex physical systems. This paper presents an integrated model that combines system architecture with physical modeling to represent and simulate a multi-plant district cooling system (DCS). We refer to this model as model-based systems engineering (MBSE) model of the DC system. A systems modeling language (SysML) model is created to develop a multi-domain architecture of the DC system that will satisfy stakeholder needs and requirements. This model is capable of executing behavior and parametric aspects (or “views”) of the system. A closed-loop of information flow was developed to map SysML constructs with their respective Modelica models to support the integration of simulated experiments with SysML construct. The integrated MBSE model is successfully implemented and the results show that the IPLV.SI value of the chiller model was 6.4157, which is in the acceptable range. Based on the initial conditions provided by the actual plant, the simulation results show that the chilled water temperature predictions by Modelica as 4.8℃ verify the corresponding stakeholders’ requirements captured in the SysML model.
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    Scanning Strategies in the PBF Process: A Critical Review
    (ASME, 2021-02) Malekipour, Ehsan; El-Mounayri, Hazim; Mechanical and Energy Engineering, School of Engineering and Technology
    The powder-bed fusion (PBF) process is capable of producing near-fully dense metallic parts; however, various defects — particularly thermal abnormalities — can still be observed during the process. Some of these thermal defects — cracks, distortion, delamination of layers, and microporosity — cannot be removed by post-processing operations. The majority of these abnormalities are the result of residual stress, heat accumulation, lack of inter-track /inter-layer bonding, lack of powder fusion, or a combination of these factors. Modifying the scanning strategy (the topology of scanning tracks) can efficiently mitigate these abnormalities by adjusting the process parameters and adopting proper scanning patterns. The implementation of different scanning strategies significantly changes the ultimate quality of printed parts and manufacturing process lead time. Choosing a proper scanning strategy minimizes the residual stress and internal porosity, generates homogeneous microstructure, and avoids heat accumulation throughout the part during the printing process. In this work, we conducted a critical review of different scanning strategies, their pros and cons, limitations, and influence on the resulting properties of fabricated parts. Furthermore, we report the latest efforts for improvement of the current scanning strategies and introduce the-state-of-the-art strategies in the multi-laser PBF (ML-PBF) process. The insights provided here can assist scholars in evaluating existing scanning strategies and scanning patterns, and in identifying ways both to overcome scanning limitations and to modify them. On the other hand, it can assist manufacturers in selecting the best scanning strategies for their products based on their designs, demands, and resources.
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    Thermo-fluid Topology Optimization and Experimental Study of Conformal Cooling Channels for 3D Printed Plastic Injection Molds
    (Elsevier, 2019) Jahan, Suchana; Wu, Tong; Shin, Yung; Tovar, Andres; El-Mounayri, Hazim; Mechanical and Energy Engineering, School of Engineering and Technology
    With the advent of additive manufacturing, innovative design methods, such as network-based techniques, and structural topology optimization have been used to generate complex and highly efficient cooling systems in recent years. However, methods that incorporate coupled thermal and fluid analysis remain scarce. In this paper, a coupled thermal-fluid topology optimization algorithm is introduced for the design of conformal cooling channels. The problem is formulated based on a coupling of Navier- Stokes equations and convection-diffusion equation. The problem is solved by gradient-based optimization after analytical sensitivity derived using adjoint method. With this method, the channel position problem is replaced to a material distribution problem. The material distribution directly depends on the effect of flow resistance, heat conduction, natural and forced convection. The algorithm leads to a two-dimensional conceptual design having optimal heat transfer and balanced flow, which is further transformed into three-dimensional cooling channel design. Here, a comprehensive study is presented, starting from design, simulation, 3D printing process and experimental testing of an injection mold with conformal cooling channels in industrial production environment. A traditional mold model is provided by an industrial collaborator. To enhance the overall thermo-fluid performance of the mold and improve final product quality, a redesign of this mold core is done with conformal cooling channels inside. The final design is 3D printed in pre-alloyed tool-steel powder Maraging Steel using Truprint 3000 metal 3D printing machine. The printed core required some heat treatment and finishing processes and added features to be incorporated to make it production ready. Once all the preparation was complete, the core was tested experimentally in a multicavity injection molding machine in real industrial environment at our industrial partner’s production facility. This paper describes all the steps starting from design, analysis, die 3D printing and finally ending at final experimental testing, as well as recommendations for tool designer and injection molding industry to implement additive manufacturing for their benefit. This paper is not just focused on a specific aspect such as design, simulation or manufacturing, but rather a comprehensive paper presenting a case study on implementation of topology optimization and additive manufacturing in real life industrial production scenario.
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    Innovative Digital Manufacturing Curriculum for Industry 4.0
    (Elsevier, 2019) Promyoo, Rapeepan; Alai, Shashank; El-Mounayri, Hazim; Mechanical and Energy Engineering, School of Engineering and Technology
    Manufacturing companies across all major industries are facing serious challenges trying to competitively design and manage modern products, which are becoming increasingly complex multi-domain systems or “systems of systems”. Model-based systems driven product development (or SDPD, for Systems Driven Product Development) has been proposed as a solution based on driving the product lifecycle from the systems requirements and tracing back performance to stakeholders’ needs through a RFLP (Requirement, Functional, Logical, Physical) traceability process. The SDPD framework integrates system behavioral modeling with downstream product design and manufacturing process practices to support the verification/validation of the systems behavior as products progress through all phases of the lifecycle, as well as the optimization of trade-offs decisions by maintaining the cross-product digital twin and thread for global decision optimization in an efficient and effective way. We have developed an innovative digital manufacturing curriculum (designed around the SDPD paradigm) that is based on the digitalization of the SE (Systems Engineering) process through the integration of modelling and simulation continuum, in the form of Model-based Systems Engineering (MBSE), with Product lifecycle management (PLM). At the core of this curriculum is a shift of focus from theory to implementation and practice, through an applied synthesis of engineering fundamentals and systems engineering, that is driven by a state-of-the-art digital innovation platform for product (or system) development consisting of integrated software (digital) tools spanning the complete lifecycle. The curriculum consists of three key components, namely, modelling and simulation continuum, traceability, and digital thread. The curriculum provides a foundation for implementing the digital twin and supports the training of the next generation of engineers for Industry 4.0. The digital manufacturing (or SDPD) framework is applied in the design and optimization of an electric skateboard. The implementation demonstrates: 1) The benefits of digitalization/model-based engineering when developing complex multi-domain products or systems; 2) The ability of students to effectively complete a real-life modern product development within the time line of one semester; 3) The provision of MBSE curriculum for Engineering Education 4.0, characterized by key, integrated skills for the digital enterprise and Industry 4.0.
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    A Framework for Estimating Mold Performance Using Experimental and Numerical Analysis of Injection Mold Tooling Prototypes
    (Springer, 2019) Jahan, Suchana; El-Mounayri, Hazim; Tovar, Andres; Shin, Yung C.; Mechanical Engineering and Energy, School of Engineering and Technology
    Additive Manufacturing (AM), 3D printing, rapid prototyping, or rapid tooling refer to a range of technologies that are capable of translating virtual CAD model data into physical model. It is executed in growing number of applications nowadays. A wide range of materials are currently being used to produce consumer products and production tools. AM has brought in revolutionary changes in traditional manufacturing practices. Yet, there are certain drawbacks that hinder its advancement at mass manufacturing. High cost associated with AM is one of them. Using 3D printed tooling can provide long-time cost effectiveness and better product quality. Additively manufactured injection molds can increase the cooling performance, reduce production cycle time, and improve surface finish and part quality of the final plastic product. Yet, manufacturers are still not using the printed molds for industrial mass production. Numerical analysis can provide approximation of such improved performance, but, factual experimental results are necessary to satisfy performance criteria of molds to justify the large investment into tooling for existing industries. In this research work, a desktop injection molding machine is used to evaluate performance of 3D printed molds to develop a cost and performance analysis tool. It serves as a baseline to predict the performance of molds in real-time mass manufacturing of consumer products. The analysis describes how appropriate the estimation can be from any simulation study of molds, how much the scaling down of tool and molding system can affect the prediction of actual performance, what correction factors can be used for better approximation of performance matrices. Several “scaled down” prototypes of injection molds have been used. They have design variations as: with or without cooling system, conformal or straight cooling channels, solid or lattice matrix, and metal or tough resin as the mold material. The molds are printed in in-house printing machines and can also be printed online with limited charges. This also provides an excellent demonstration of using inexpensive material and manufacturing process, such as resin to estimate the performance of highly expensive 3D printed stainless steel molds. The work encompasses a framework to reduce overall cost of implementing AM, by lowering time and monetary expenses during the research and development, and prototyping phases.
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    Implementation of Conformal Cooling & Topology Optimization in 3D Printed Stainless Steel Porous Structure Injection Molds
    (2016) Jahan, Suchana A.; Wu, Tong; Zhang, Yi; El-Mounayri, Hazim; Tovar, Andres; Zhang, Jing; Acheson, Douglas; Nalim, M. Razi; Guo, Xingye; Lee, Weng Hoh
    This work presents implementation of numerical analysis and topology optimization techniques for redesigning traditional injection molding tools. Traditional injection molding tools have straight cooling channels, drilled into a solid body of the core and cavity. The cooling time constitutes a large portion of the total production cycle that needs to be reduced as much as possible in order to bring in a significant improvement in the overall business of injection molding industry. Incorporating conformal cooling channels in the traditional dies is a highly competent solution to lower the cooling time as well as improve the plastic part quality. In this paper, the thermal and mechanical behavior of cavity and core with conformal cooling channels are analyzed to find an optimum design for molding tools. The proposed design with conformal cooling channels provides a better alternative than traditional die designs with straight channels. This design is further optimized using thermo-mechanical topology optimization based on a multiscale approach for generating sound porous structures. The implemented topology optimization results in a light-weight yet highly effective die cavity and core. The reduction in weight achieved through the design of dies with porous structures is meant to facilitate the adoption of additive manufacturing for die making by the tooling industry.
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    A Framework for Optimizing the Design of Injection Molds with Conformal Cooling for Additive Manufacturing
    (2015-01-01) Wu, Tong; Jahan, Suchana A.; Kumaar, Praveen; Tovar, Andres; El-Mounayri, Hazim; Zhang, Yi; Zhang, Jing; Acheson, Doug; Brand, Kim; Nalim, M. Razi
    This work presents 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 multi-scale 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. Finally, material modeling using simulation as well as design of experiments is underway for obtaining the material properties and their variations.