Browsing by Author "Meng, Lingbin"

Now showing 1 - 8 of 8
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    Atomistic modeling of resistivity evolution of copper nanoparticle in intense pulsed light sintering process
    (Elsevier, 2019-02) Meng, Lingbin; Zhang, Yi; Yang, Xuehui; Zhang, Jing; Mechanical and Energy Engineering, School of Engineering and Technology
    In this work, the intense pulsed light (IPL) sintering process of copper nanoparticle ink is simulated using molecular dynamics (MD) method. First, the neck size growth between the two copper nanoparticles during the IPL sintering process is computed. The resultant electrical resistivity is then calculated by substituting the neck size into the Reimann-Weber formula. Overall, a rapid decrease of electric resistivity is observed in the beginning of the sintering, which is caused by quick neck size growth, followed by a gradually decrease of resistivity. In addition, the correlation of the simulated temperature dependent resistivity is similar to that of the experimentally measured resistivity. The MD model is an effective tool for designers to optimize the IPL sintering process.
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    A Combined Modeling and Experimental Study of Tensile Properties of Additively Manufactured Polymeric Composite Materials
    (Springer, 2020) Meng, Lingbin; Yang, Xuehui; Salcedo, Eduardo; Baek, Dong-Cheon; Ryu, Jong Eun; Lu, Zhe; Zhang, Jing; Mechanical and Energy Engineering, School of Engineering and Technology
    In this study, the mechanical properties, in terms of stress–strain curves, of additively manufactured polymeric composite materials, Tango black plus (TB+), vero white plus (VW ), and their intermediate materials with different mixing ratios, are reported. The ultimate tensile strength and elongation at break are experimentally measured using ASTM standard tensile test. As the content of VM+ increases, the strength of the polymeric materials increases and elongation decreases. Additionally, the Shore A hardness of the materials increases with reduced TB+ concentration. In parallel to the experiment, hyperelastic models are employed to fit the experimental stress–strain curves. The shear modulus of the materials is obtained from the Arruda–Boyce model, and it increases with reduced concentration of TB+. Due to the good quality of the fitted data, it is suggested that the Arruda–Boyce model is the best model for modeling the additively manufactured polymeric materials. With the well characterized and modeled mechanical properties of these hyperelastic materials, designers can conduct computational study for application in flexible electronics field.
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    Machine Learning in Additive Manufacturing: A Review
    (Springer, 2020) Meng, Lingbin; McWilliams, Brandon; Jarosinski, William; Park, Hye-Yeong; Jung, Yeon-Gil; Lee, Jehyun; Zhang, Jing; Engineering Technology, School of Engineering and Technology
    In this review article, the latest applications of machine learning (ML) in the additive manufacturing (AM) field are reviewed. These applications, such as parameter optimization and anomaly detection, are classified into different types of ML tasks, including regression, classification, and clustering. The performance of various ML algorithms in these types of AM tasks are compared and evaluated. Finally, several future research directions are suggested.
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    A Multi-Scale Multi-Physics Modeling Framework of Laser Powder Bed Fusion Additive Manufacturing Process
    (Elsevier, 2018-05) Zhang, Jing; Zhang, Yi; Lee, Weng Hoh; Wu, Linmin; Sagar, Sugrim; Meng, Lingbin; Choi, Hyun-Hee; Jung, Yeon-Gil; Mechanical Engineering, School of Engineering and Technology
    A longstanding challenge is to optimize additive manufacturing (AM) process in order to reduce AM component failure due to excessive distortion and cracking. To address this challenge, a multi-scale physics-based modeling framework is presented to understand the interrelationship between AM processing parameters and resulting properties. In particular, a multi-scale approach, spanning from atomic, particle, to component levels, is employed. The simulations of sintered material show that sintered particles have lower mechanical strengths than the bulk metal because of their porous structures. Higher heating rate leads to a higher mechanical strength due to accelerated sintering rates. The average temperature in the powder bed increases with higher laser power. The predicted distortion due to residual stress in the AM fabricated component is in good agreement with experimental measurements. In summary, the model framework provides a design tool to optimize the metal powder based additive manufacturing process.
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    Phase field simulation of dendritic microstructure in additively manufactured titanium alloy
    (Elsevier, 2019-01) Zhang, Jing; Wu, Linmin; Zhang, Yi; Meng, Lingbin; Mechanical and Energy Engineering, School of Engineering and Technology
    Additive manufacturing (AM) processes for metals, such as selective laser sintering and electron beam melting, involve rapid solidification process. The microstructure of the fabricated material and its properties strongly depend on the solidification. Therefore, in order to control and optimize the AM process, it is important to understand the microstructure evolution. In this work, using Ti-6Al-4V as a model system, the phase field method is applied to simulate the microstructure evolution in additively manufactured metals. First, the fundamental governing equations are presented. Then the effects of various processing related parameters, including local temperature gradient, scan speed and cooling rate, on dendrites’ morphology and growth velocity are studied. The simulated results show that the dendritic arms grow along the direction of the heat flow. Higher temperature gradient, scan speed and cooling rate will result in small dendritic arm spacing and higher growth velocity. The simulated dendritic morphology and arm spacings are in good agreement with experimental data and theoretical predictions.
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    Probabilistic Feasibility Design of a Laser Powder Bed Fusion Process Using Integrated First-Order Reliability and Monte Carlo Methods
    (ASME, 2021-09) Meng, Lingbin; Du, Xiaoping; McWilliams, Brandon; Zhang, Jing; Mechanical and Energy Engineering, School of Engineering and Technology
    Quality inconsistency due to uncertainty hinders the extensive applications of a laser powder bed fusion (L-PBF) additive manufacturing process. To address this issue, this study proposes a new and efficient probabilistic method for the reliability analysis and design of the L-PBF process. The method determines a feasible region of the design space for given design requirements at specified reliability levels. If a design point falls into the feasible region, the design requirement will be satisfied with a probability higher or equal to the specified reliability. Since the problem involves the inverse reliability analysis that requires calling the direct reliability analysis repeatedly, directly using Monte Carlo simulation (MCS) is computationally intractable, especially for a high reliability requirement. In this work, a new algorithm is developed to combine MCS and the first-order reliability method (FORM). The algorithm finds the initial feasible region quickly by FORM and then updates it with higher accuracy by MCS. The method is applied to several case studies, where the normalized enthalpy criterion is used as a design requirement. The feasible regions of the normalized enthalpy criterion are obtained as contours with respect to the laser power and laser scan speed at different reliability levels, accounting for uncertainty in seven processing and material parameters. The results show that the proposed method dramatically alleviates the computational cost while maintaining high accuracy. This work provides a guidance for the process design with required reliability.
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    Process Design of Laser Powder Bed Fusion of Stainless Steel Using a Gaussian Process-Based Machine Learning Model
    (Springer, 2020) Meng, Lingbin; Zhang, Jing; Mechanical and Energy Engineering, School of Engineering and Technology
    In this work, a Gaussian process (GP)-based machine learning model is developed to predict the remelted depth of single tracks, as a function of combined laser power and laser scan speed in a laser powder bed fusion process. The GP model is trained by both simulation and experimental data from the literature. The mean absolute prediction error magnified by the GP model is only 0.6 μm for a powder bed with layer thickness of 30 μm, suggesting the adequacy of the GP model. Then, the process design maps of two metals, 316L and 17-4 PH stainless steels, are developed using the trained model. The normalized enthalpy criterion of identifying keyhole mode is evaluated for both stainless steels. For 316L, the result suggests that the ΔHhs≥30 criterion should be related to the powder layer thickness. For 17-4 PH, the criterion should be revised to ΔHhs≥25.
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    Simulation of Spatters Sticking Phenomenon in Laser Powder Bed Fusion Process Using the Smoothed Particle Hydrodynamics Method
    (American Society of Mechanical Engineers, 2021-11) Meng, Lingbin; Sun, Tao; Dube, Tejesh; Sagar, Sugrim; Yang, Xuehui; Zhang, Jian; Zhang, Jing; Mechanical and Energy Engineering, School of Engineering and Technology
    In this work, a smoothed particle hydrodynamics (SPH) method is developed to simulate the spattering phenomenon in the laser powder bed fusion (L-PBF) process. First, an experiment using the high-speed synchrotron X-ray full-field imaging is conducted to acquire in-situ images during the L-PBF process. Then, a scenario is selected from the X-ray image as a case study of the SPH model. In the case study, a particle is ejected and melted by the metal vapor, impacts with another particle, solidifies, and sticks to the other particle to form a rigid body. As a result, the trajectories of the two particles match well with the experimental observation. The evolution of velocity and temperature of the particle is extracted from the simulation for analysis. The SPH model can be a useful alternative to computational models of simulating the spattering phenomenon of L-PBF.