Browsing by Author "Mechanical and Energy Engineering, School of Engineering and Technology"

Now showing 1 - 10 of 225
  • Thumbnail Image
    Item
    2D MXenes: Tunable Mechanical and Tribological Properties
    (Wiley, 2021-04-28) Wyatt, Brian C.; Rosenkranz, Andreas; Anasori, Babak; Mechanical and Energy Engineering, School of Engineering and Technology
    2D transition metal carbides, nitrides, and carbonitrides, known as MXenes, were discovered in 2011 and have grown to prominence in energy storage, catalysis, electromagnetic interference shielding, wireless communications, electronic, sensors, and environmental and biomedical applications. In addition to their high electrical conductivity and electrochemically active behavior, MXenes' mechanical properties, flexibility, and strong adhesion properties play crucial roles in almost all of these growing applications. Although these properties prove to be critical in MXenes' impressive performance, the mechanical and tribological understanding of MXenes, as well as their relation to the synthesis process, is yet to be fully explored. Here, a fundamental overview of MXenes' mechanical and tribological properties is provided and the effects of MXenes' compositions, synthesis, and processing steps on these properties are discussed. Additionally, a critical perspective of the compositional control of MXenes for innovative structural, low-friction, and low-wear performance in current and upcoming applications of MXenes is provided. It is established here that the fundamental understanding of MXenes' mechanical and tribological behavior is essential for their quickly growing applications.
  • Thumbnail Image
    Item
    2D Titanium Carbide (MXene) Based Films: Expanding the Frontier of Functional Film Materials
    (Wiley, 2021-11) Li, Guohao; Wyatt, Brian C.; Song, Fei; Yu, Changqiang; Wu, Zhenjun; Xie, Xiuqiang; Anasori, Babak; Zhang, Nan; Mechanical and Energy Engineering, School of Engineering and Technology
    2D titanium carbide (Ti3C2Tx) MXene films, with their well-defined microstructures and chemical functionality, provide a macroscale use of nano-sized Ti3C2Tx flakes. Ti3C2Tx films have attractive physicochemical properties favorable for device design, such as high electrical conductivity (up to 20 000 S cm–1), impressive volumetric capacitance (1500 F cm–3), strong in-plane mechanical strength (up to 570 MPa), and a high degree of flexibility. Here, the appealing features of Ti3C2Tx-based films enabled by the layer-to-layer arrangement of nanosheets are reviewed. We devote attention to the key strategies for actualizing desirable characteristics in Ti3C2Tx-based functional films, such as high and tunable electrical conductivity, outstanding mechanical properties, enhanced oxidation-resistance and shelf life, hydrophilicity/hydrophobicity, adjustable porosity, and convenient processability. This review further discusses fundamental aspects and advances in the applications of Ti3C2Tx-based films with a focus on illuminating the relationship between the structural features and the resulting performances for target applications. Finally, the challenges and opportunities in terms of future research, development, and applications of Ti3C2Tx-based films are suggested. A comprehensive understanding of these competitive features and challenges shall provide guidelines and inspiration for the further development of Ti3C2Tx-based functional films, and contribute to the advances in MXene technology.
  • Thumbnail Image
    Item
    2D transition metal carbides (MXenes) in metal and ceramic matrix composites
    (Springer, 2021-06-02) Wyatt, Brian C.; Nemani, Srinivasa Kartik; Anasori, Babak; Mechanical and Energy Engineering, School of Engineering and Technology
    Two-dimensional transition metal carbides, nitrides, and carbonitrides (known as MXenes) have evolved as competitive materials and fillers for developing composites and hybrids for applications ranging from catalysis, energy storage, selective ion filtration, electromagnetic wave attenuation, and electronic/piezoelectric behavior. MXenes’ incorporation into metal matrix and ceramic matrix composites is a growing field with significant potential due to their impressive mechanical, electrical, and chemical behavior. With about 50 synthesized MXene compositions, the degree of control over their composition and structure paired with their high-temperature stability is unique in the field of 2D materials. As a result, MXenes offer a new avenue for application driven design of functional and structural composites with tailorable mechanical, electrical, and thermochemical properties. In this article, we review recent developments for use of MXenes in metal and ceramic composites and provide an outlook for future research in this field.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Active learning with generalized sliced inverse regression for high-dimensional reliability analysis
    (Elsevier, 2022-01) Yin, Jianhua; Du, Xiaoping; Mechanical and Energy Engineering, School of Engineering and Technology
    It is computationally expensive to predict reliability using physical models at the design stage if many random input variables exist. This work introduces a dimension reduction technique based on generalized sliced inverse regression (GSIR) to mitigate the curse of dimensionality. The proposed high dimensional reliability method enables active learning to integrate GSIR, Gaussian Process (GP) modeling, and Importance Sampling (IS), resulting in an accurate reliability prediction at a reduced computational cost. The new method consists of three core steps, 1) identification of the importance sampling region, 2) dimension reduction by GSIR to produce a sufficient predictor, and 3) construction of a GP model for the true response with respect to the sufficient predictor in the reduced-dimension space. High accuracy and efficiency are achieved with active learning that is iteratively executed with the above three steps by adding new training points one by one in the region with a high chance of failure.
  • Thumbnail Image
    Item
    Adaptive Kriging Method for Uncertainty Quantification of the Photoelectron Sheath and Dust Levitation on the Lunar Surface
    (ASME, 2021) Wei, Xinpeng; Zhao, Jianxun; He, Xiaoming; Hu, Zhen; Du, Xiaoping; Han, Daoru; Mechanical and Energy Engineering, School of Engineering and Technology
    This paper presents an adaptive Kriging based method to perform uncertainty quantification (UQ) of the photoelectron sheath and dust levitation on the lunar surface. The objective of this study is to identify the upper and lower bounds of the electric potential and that of dust levitation height, given the intervals of model parameters in the one-dimensional (1D) photoelectron sheath model. To improve the calculation efficiency, we employ the widely used adaptive Kriging method (AKM). A task-oriented learning function and a stopping criterion are developed to train the Kriging model and customize the AKM. Experiment analysis shows that the proposed AKM is both accurate and efficient.
  • Thumbnail Image
    Item
    Agent-based Three Layer Framework of Assembly-Oriented Planning and Scheduling for Discrete Manufacturing Enterprises
    (IEEE, 2018-06) Fan, Yinghui; Anwar, Sohel; Wang, Litao; Mechanical and Energy Engineering, School of Engineering and Technology
    To solve the cost burden caused by delivery tardiness for small and medium sized packaging machinery enterprises, the assembly-oriented planning and scheduling is studied based on the multi-agent technology. Taking into account the due date, the planning and scheduling are optimized iteratively with the rule-based algorithms complying with the machining and assembling process constraints. To make the realistic large-scale production planning scheme tailored to fit the optimization algorithms, a multi-agent system is utilized to conceptually construct a three-layer framework corresponding to three planning horizons of different task level. These planning horizons are quarter/month of product/subassembly level, week of part level, and day of operation level. With the strategy of combining top-down task decomposition and bottom-up plan update (where the quarterly orders are decomposed into the monthly, weekly, and daily tasks according to the product processing structure while the resulting plans of every layer are updated iteratively based on the bottom layer optimization), the proposed framework not only addresses the planning but also the periodic and event-driven scheduling of the processes. In this paper, a gravure printing machine is considered as a test case for evaluating the proposed framework. The simulation results with the historical data of the orders for this machine demonstrate the effectiveness of the proposed scheme on the control of the distribution of idle-time. It can also provide a resolution to tardiness penalty for small and medium sized enterprises.
  • Thumbnail Image
    Item
    Air Compressor Load Forecasting using Artificial Neural Network
    (Elsevier, 2021-04) Wu, Da-Chun; Bahrami Asl, Babak; Razban, Ali; Chen, Jie; Mechanical and Energy Engineering, School of Engineering and Technology
    Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network in air compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems' electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load management and look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Network and Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. The forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient of determination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and mean absolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield good results for compressors using variable speed drive (average R2 = 0.8 and no naïve forecasting), only the long short-term memory model gives acceptable results for compressors using on/off control (average R2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting naïve forecasting).
  • Thumbnail Image
    Item
    Analysis of a discrete-layout bimorph disk elements piezoelectric deformable mirror
    (SPIE, 2018-04) Wang, Hairen; Chen, Ziguang; Yang, Shengfeng; Hu, Lin; Hu, Ming; Mechanical and Energy Engineering, School of Engineering and Technology
    We introduce a discrete-layout bimorph disk elements piezoelectric deformable mirror (DBDEPDM), driven by the circular flexural-mode piezoelectric actuators. We formulated an electromechanical model for analyzing the performance of the new deformable mirror. As a numerical example, a 21-actuators DBDEPDM with an aperture of 165 mm was modeled. The presented results demonstrate that the DBDEPDM has a stroke larger than 10  μm and the resonance frequency is 4.456 kHz. Compared with the conventional piezoelectric deformable mirrors, the DBDEPDM has a larger stroke, higher resonance frequency, and provides higher spatial resolution due to the circular shape of its actuators. Moreover, numerical simulations of influence functions on the model are provided.
  • Thumbnail Image
    Item
    Analysis of Composite Structures in Curing Process for Shape Deformations and Shear Stress: Basis for Advanced Optimization
    (MDPI, 2021) Kumbhare, Niraj; Moheimani, Reza; Dalir, Hamid; Mechanical and Energy Engineering, School of Engineering and Technology
    Identifying residual stresses and the distortions in composite structures during the curing process plays a vital role in coming up with necessary compensations in the dimensions of mold or prototypes and having precise and optimized parts for the manufacturing and assembly of composite structures. This paper presents an investigation into process-induced shape deformations in composite parts and structures, as well as a comparison of the analysis results to finalize design parameters with a minimum of deformation. A Latin hypercube sampling (LHS) method was used to generate the required random points of the input variables. These variables were then executed with the Ansys Composite Cure Simulation (ACCS) tool, which is an advanced tool used to find stress and distortion values using a three-step analysis, including Ansys Composite PrepPost, transient thermal analysis, and static structural analysis. The deformation results were further utilized to find an optimum design to manufacture a complex composite structure with the compensated dimensions. The simulation results of the ACCS tool are expected to be used by common optimization techniques to finalize a prototype design so that it can reduce common manufacturing errors like warpage, spring-in, and distortion.