Browsing by Author "McWilliams, Brandon"

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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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    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.