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Abstract Fatigue scattering caused by inherent geometrical defects in laser powder bed fusion (LPBF) imposes a great challenge for fabricating reliable load-bearing components. However, the lack of sufficient fatigue data and the limitation of runout conditions rationalize the need to bridge the gap between limited data and fatigue reliability. This work has developed two models based on censored linear regression (CR) and censored Gaussian process regression (CGP), respectively, to predict fatigue life scattering bounds at a given confidence for both as-built and heat-treated SS 316L samples. Furthermore, fatigue life reliability is modeled under different stress amplitudes with a CGP-based reliability model. 

With advancements in sensor technology, real-time monitoring of machine health conditions allows us to perform condition-based maintenance (CBM) for multi-unit systems. The maintenance decision of a unit is usually dependent on other units in a multi-unit system, inducing an exponentially large state space, which makes CBM of large multi-unit systems a very challenging engineering problem. In this work, we first propose two heuristic decision policies for multi-unit systems, namely the binary action policy and the $(n,N_1,N_2)$-policy. Then we propose a multi-step lookahead rollout approach using the two heuristic policies to solve the challenging CBM problem. By applying the binary action policy, we can effectively reduce the action space and thus reduce the computational load in the rollout, while the $(n,N_1,N_2)$-policy can be an excellent base policy for the rollout to improve upon. The theoretical gap between the proposed rollout approach and the optimal policy is also derived. The study further shows extensive experimentation to demonstrate the effectiveness of the proposed lookahead rollout approach for solving the CBM problem for small (3 and 5 units), medium (10 and 15 units), and large (20, 30, 40, and 50 units) scale systems. 

Condition-based maintenance of multi-component systems is a prevalent engineering problem due to its effectiveness in reducing the operational and maintenance costs of the system. However, developing the exact optimal maintenance decisions for the large multi-component system is computationally challenging, even not feasible, due to the exponential growth in system state and action space size with the number of components in the system. To address the scalability issue in CBM of large multi-component systems, we propose a Component-Wise Markov Decision Process(CW-MDP) and an Adjusted Component-Wise Markov Decision Process (ACW-MDP) to obtain an approximation of the optimal system-level CBM decision policy for large systems with heterogeneous components. We propose using an extended single-component action space to model the impact of system-level setup cost on a component-level solution. The theoretical gap between the proposed approach and system-level optima is also derived. Additionally, theoretical convergence and the relationship between ACW-MDP and CW-MDP are derived. The study further shows extensive numerical studies to demonstrate the effectiveness of component-wise solutions for solving large multi-component systems.