Browsing by Author "Agarwal, Mangilal"

Now showing 1 - 10 of 95
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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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    Au nanoparticle assembly on cnts using flash induced solid-state dewetting
    (2015-04-28) Kulkarni, Ameya; Ryu, Jong Eun; Agarwal, Mangilal; Xie, Jian; Cheng, Ruihua
    Carbon Nanotubes (CNTs) are used extensively in various applications where substrate are required to be possessing higher surface area, porosity and electrical and thermal conductivity. Such properties can be enhanced to target a particular gas and biochemical for efficient detection when CNT matrix is functionalized with Nanoparticles (NPs). Conventional functionalization involves harsh oxidation repeated washing, filtration and sonication, which induce defects. The defects lead to hindered mobility of carriers, unwanted doping and also fragmentation of the CNTs in some cases. In this document we demonstrate functionalization of CNT with Au nanoparticles on a macro scale under dry and ambient condition using Xenon ash induced solid-state dewetting. A sputtered thin film was transformed into nanoparticles which were confirmed to be in a state of thermodynamic equilibrium. We worked on 3 nm, 6 nm, 9 nm, 15 nm, 30 nm initial thickness of thin films. Xenon ash parameters of energy, number of pulse, duration of pulse, duration of gap between consecutive pulses were optimized to achieve complete dewetting of Au thin films. 3 nm deposition was in the form of irregular nano-islands which were transformed into stable nanoparticles with a single shot of 10 J/cm2 of 2 ms duration. 6 nm and 9 nm deposition was in form of continues film which was also dewetted into stable nanoparticles with a single pulse but with an increased energy density of 20 J/cm2 and 35 J/cm2 respectively. In case of 15 nm and 30 nm deposition the thin film couldn't be dewetted with a maximum energy density of 50 J/cm2, it was observed that 3 and 4 pulses of 2 ms pulse duration and 2 ms gap duration with an energy density of 50 J/cm2 were required to completely dewet the thicker films. However irregularity was induced in the sizes of the NPs due to Ostwald ripening phenomenon which causes smaller particle within a critical difiusion length to combine and form a larger particle during or after dewetting process. For comparison, the Au thin films were also dewetted by a conventional process involving annealing of samples until the thin film was fully transformed into NPs and the size of NPs seized to grow. Scanning electron microscope (SEM) was used to characterize the samples. Thermodynamic stability of the particles was confirmed with statistical analyses of size distribution after every additional pulse.
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    Breast Cancer Detection via Microwave Imaging
    (Office of the Vice Chancellor for Research, 2011-04-08) Reid, Joshua R.N.; Ghane, Parvin; Shrestha, Sudhir; Agarwal, Mangilal; Varahramyan, Kody
    Breast cancer is one of the major common diseases among women and takes about 40,000 lives every year. Early detection of breast cancer greatly increases the chance of survival. The norm for today’s detection of breast cancer consists of mammograms, magnetic resonance imaging (MRI), and ultrasonic examination. Unfortunately, the process is a fraction of completeness despite its feeling of discomfort, high cost, and exposure to ionizing radiation which poses cumulative side effects respectively. The present research investigates the efficiency and implementation of microwave imaging to be used in the detection of breast cancer. Microwave imaging (MWI) is a process that illuminates the breast with microwave signals, and receives and analyses scattered signals for breast cancer detection and imaging. The electromagnetic waves that are scattered within the breast provide information that are transmitted and received via microstrip patch antennas, providing an image of detected lesions. In the presented poster, design of a patch antenna and simulation results are presented. In the event of designing, the overall goal was to obtain a voltage standing wave ratio (VSWR) less than 2 at 2.4 GHz signal frequency. To receive the intended results, the dimensions and design of the microstrip patch were important factors given the substrate parameters. Currently, the project is in the prototyping stage for the validation of simulation results and further optimization and development of the antenna for microwave breast cancer detection and imaging applications.
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    Canine-Inspired Chemometric Analysis of Volatile Organic Compounds in Urine Headspace to Distinguish Prostate Cancer in Mice and Men
    (MDPI, 2023-02-20) Woollam, Mark; Siegel, Amanda P.; Munshi, Adam; Liu, Shengzhi; Tholpady, Sunil; Gardner, Thomas; Li, Bai-Yan; Yokota, Hiroki; Agarwal, Mangilal; Chemistry and Chemical Biology, School of Science
    Canines can identify prostate cancer with high accuracy by smelling volatile organic compounds (VOCs) in urine. Previous studies have identified VOC biomarkers for prostate cancer utilizing solid phase microextraction (SPME) gas chromatography-mass spectrometry (GC-MS) but have not assessed the ability of VOCs to distinguish aggressive cancers. Additionally, previous investigations have utilized murine models to identify biomarkers but have not determined if the results are translatable to humans. To address these challenges, urine was collected from mice with prostate cancer and men undergoing prostate cancer biopsy and VOCs were analyzed by SPME GC-MS. Prior to analysis, SPME fibers/arrows were compared, and the fibers had enhanced sensitivity toward VOCs with a low molecular weight. The analysis of mouse urine demonstrated that VOCs could distinguish tumor-bearing mice with 100% accuracy. Linear discriminant analysis of six VOCs in human urine distinguished prostate cancer with sensitivity = 75% and specificity = 69%. Another panel of seven VOCs could classify aggressive cancer with sensitivity = 78% and specificity = 85%. These results show that VOCs have moderate accuracy in detecting prostate cancer and a superior ability to stratify aggressive tumors. Furthermore, the overlap in the structure of VOCs identified in humans and mice shows the merit of murine models for identifying biomarker candidates.
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    Carbon and cellulose based nanofillers reinforcement to strengthen carbon fiber-epoxy composites: Processing, characterizations, and applications
    (Frontiers, 2023-01-10) Biswas, Pias Kumar; Omole, Oluwaseun; Peterson, Garrett; Cumbo, Eric; Agarwal, Mangilal; Dalir, Hamid; Mechanical Engineering, School of Engineering and Technology
    Since the inception of carbon fiber reinforced polymer (CFRP) composites, different nanofillers have been investigated to strengthen their mechanical and physical properties. To date, the majority of research has focused on enhancing fiber/matrix interface characteristics and/or optimizing nanofiller dispersion within the matrix, both of which improve the performance of carbon fiber-epoxy composite structures. Nanofillers can be dispersed into the polymer matrix by different techniques or nanofillers are chemically bonded to fiber, polymer, or both via multiple reaction steps. However, a few studies were conducted showing the effects of different nanofillers on the performance of carbon fiber-epoxy composites. Here a critical study has been done to explore different carbon and cellulose-based nanofillers which are used to enhance the mechanical and physical properties of carbon fiber-epoxy composites. After giving a short history of carbon fiber production, the synthesis of carbon nanotubes (CNTs), graphene, cellulose-based nanofillers (cellulose nanocrystals and nanofibers), their dispersion in the polymer matrix, and chemical/physical bonding with the fiber or polymer have been extensively described here along with their processing techniques, characterizations, and applications in various fields.
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    Carbon Fiber Reinforced Lithium-Ion Battery Composites with Higher Mechanical Strength: Multifunctional Power Integration for Structural Applications
    (2021-08) Jadhav, Mayur Shrikant; Agarwal, Mangilal; Dalir, Hamid; Zhang, Jing
    This study proposes and evaluates a multi-functional carbon fiber reinforced composite with embedded Lithium-ion battery for its structural integrity concept. The comparison of versatile composite structures manufactured conventionally, air-sprayed and electrospun multi walled carbon nano tubes in order to discover a better packaging method for incorporating lithium-ion batteries at its core is determined. In the electrospinning process recognized globally as a flexible and cost-effective method for generating continuous Nano filaments. It was incorporated exactly on the prepreg surface to obtain effective inter-facial bonding and adhesion between the layers. The mechanical and physical properties of carbon fiber reinforced polymers (CFRP) with electrospun multi walled carbon nano tubes (CNTs) have evidenced to possess higher mechanical strength incorporated between the layers of the composite prepreg than the traditional CFRP prepreg composite, At the same time the air sprayed CFRP with CNTs offers mechanical strength more than the traditional CFRP prepreg but lesser than the electrospun. This can be a design consideration from the economic feasibility viewpoint. They also contribute to efficient load transfer and structural load bearing implementation without compromising the chemistry of battery. The design validation, manufacture methods, and experimental characterization (mechano-electrical) of Multi-functional energy storage composites (MESCs) are examined. Experimental results on the electrochemical characterization reveal that the MESCs show comparable performance to the standard lithium-ion pouch cells without any external packaging and not under any loading requirements. The mechanical performance of the MESC cells especially electrospun CFRP is evaluated from three-point bending tests with the results demonstrating significant mechanical strength and stiffness compared to traditional pouch cells and conventional, air-sprayed CFRP and at lowered packaging weight and thickness. This mechanical robustness of the MESCs enable them to be manufactured as energy-storage devices for electric vehicles.
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    Cellulose Nano Fibers Infused Polylactic Acid Using the Process of Twin Screw Melt Extrusion for 3d Printing Applications
    (2023-05) Bhaganagar, Siddharth; Dalir, Hamid; Agarwal, Mangilal; Zhang, Jing
    In this thesis, cellulose nanofiber (CNF) reinforced polylactic acid (PLA) filaments were produced for 3D printing applications using melt extrusion. The use of CNF reinforcement has the potential to improve the mechanical properties of PLA, making it a more suitable material for various 3D printing applications. To produce the nanocomposites, a master batch with a high concentration of CNFs was premixed with PLA, and then diluted to final concentrations of 1, 3, and 5 wt% during the extrusion process. The dilution was carried out to assess the effects of varying CNF concentrations on the morphology and mechanical properties of the composites. The results showed that the addition of 3 wt.% CNF significantly enhanced the mechanical properties of the PLA composites. Specifically, the tensile strength increased by 77.7%, the compressive strength increased by 62.7%, and the flexural strength increased by 60.2%. These findings demonstrate that the melt extrusion of CNF reinforced PLA filaments is a viable approach for producing nanocomposites with improved mechanical properties for 3D printing applications. In conclusion, the study highlights the potential of CNF reinforcement in improving the mechanical properties of PLA for 3D printing applications. The results can provide valuable information for researchers and industries in the field of 3D printing and materials science, as well as support the development of more advanced and sustainable 3D printing materials.
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    Chemometric Analysis of Urinary Volatile Organic Compounds to Monitor the Efficacy of Pitavastatin Treatments on Mammary Tumor Progression over Time
    (MDPI, 2022-07) Grocki, Paul; Woollam, Mark; Wang, Luqi; Liu, Shengzhi; Kalra, Maitri; Siegel, Amanda P.; Li, Bai-Yan; Yokota, Hiroki; Agarwal, Mangilal; Chemistry and Chemical Biology, School of Science
    Volatile organic compounds (VOCs) in urine are potential biomarkers of breast cancer. Previously, our group has investigated breast cancer through analysis of VOCs in mouse urine and identified a panel of VOCs with the ability to monitor tumor progression. However, an unanswered question is whether VOCs can be exploited similarly to monitor the efficacy of antitumor treatments over time. Herein, subsets of tumor-bearing mice were treated with pitavastatin at high (8 mg/kg) and low (4 mg/kg) concentrations, and urine was analyzed through solid-phase microextraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS). Previous investigations using X-ray and micro-CT analysis indicated pitavastatin administered at 8 mg/kg had a protective effect against mammary tumors, whereas 4 mg/kg treatments did not inhibit tumor-induced damage. VOCs from mice treated with pitavastatin were compared to the previously analyzed healthy controls and tumor-bearing mice using chemometric analyses, which revealed that mice treated with pitavastatin at high concentrations were significantly different than tumor-bearing untreated mice in the direction of healthy controls. Mice treated with low concentrations demonstrated significant differences relative to healthy controls and were reflective of tumor-bearing untreated mice. These results show that urinary VOCs can accurately and noninvasively predict the efficacy of pitavastatin treatments over time.
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    Chemometric Analysis of Volatile Organic Compound Biomarkers of Disease and Development of Solid Phase Microextraction Fibers to Evaluate Gas Sensing Layers
    (2022-08) Woollam, Mark David; Agarwal, Mangilal; Deiss, Frédérique; Goodpaster, John; Naumann, Christoph
    Canines can detect different diseases simply by smelling different biological sample types, including urine, breath and sweat. This has led researchers to try and discovery unique volatile organic compound (VOC) biomarkers. The power of VOC biomarkers lies in the fact that one day they may be able to be utilized for noninvasive, rapid and accurate diagnostics at a point of care using miniaturized biosensors. However, the identity of the specific VOC biomarkers must be demonstrated before designing and fabricating sensing systems. Through an extensive series of experiments, VOCs in urine are profiled by solid phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS) to identify biomarkers for breast cancer using murine models. The results from these experiments indicated that unique classes of urinary VOCs, primarily terpene/terpenoids and carbonyls, are potential biomarkers of breast cancer. Through implementing chemometric approaches, unique panels of VOCs were identified for breast cancer detection, identifying tumor location, determining the efficacy of dopaminergic antitumor treatments, and tracking cancer progression. Other diseases, including COVID-19 and hypoglycemia (low blood sugar) were also probed to identify volatile biomarkers present in breath samples. VOC biomarker identification is an important step toward developing portable gas sensors, but another hurdle that exists is that current sensors lack selectivity toward specific VOCs of interest. Furthermore, testing sensors for sensitivity and selectivity is an extensive process as VOCs must be tested individually because the sensors do not have modes of chromatographic separation or compound identification. Another set of experiments is presented to demonstrate that SPME fibers can be coated with materials, used to extract standard solutions of VOCs, and analyzed by GC-MS to determine the performance of various gas sensing layers. In the first of these experiments, polyetherimide (PEI) was coated onto a SPME fiber and compared to commercial polyacrylate (PAA) fibers. The second experiment tuned the extraction efficiency of polyvinylidene fluoride (PVDF) - carbon black (CB) composites and showed that they had higher sensitivity for urinary VOC extraction relative to a polydimethylsiloxane (PDMS) SPME fiber. These results demonstrate SPME GC-MS can rapidly characterize and tune the VOC adsorption capabilities of gas sensing layers.