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In this work, a process-structure-property relationship for additively manufactured Al-Si-Mg alloy was constructed, with the aid of an integrated multi-physics model. Specifically, first, a series of thermal simulations were performed to understand molten pool geometry under different additive manufacturing (AM) operating conditions, including laser beam power, scanning speed, and hatch spacing. The porosity formation was predicted based on thermal simulation results, which yield molten pool dimension information for predicting the lack-of-fusion porosity. Dream.3D was utilized to reconstruct synthetic microstructures with different volume fraction of porosity.

The moisture/water absorption and microvoids/cracks progression are two well-understood mechanisms that have significant degradation effects on the mechanical properties/behaviors of the polymer-based composites. To theoretically investigate the effects of above two mechanisms, we develop a simple fiber reinforced polymer composites model by employing the internal state variable (ISV) theory. The water content and the anisotropically distributed damage of the composites are considered as two ISVs (the water content is described by a scalar variable and the damage is defined as a second order tensor) whose histories are governed by two specific physically-based evolution equations. The proposed model can be easily cast into a general theoretical framework to capture more polymer composites behaviors such as viscoelasticity, viscoplasticity and the thermal effect.

The autonomous collision avoidance problem for multi-trailer vehicle maneuvering is investigated in this paper. Different from conventional vehicle systems that contain one single moving part or multi-parts that can be considered as one rigid body, the interconnection between the tractor and each trailer, and interactions between trailers in the multi-trailer system introduce a high dimensional and highly complex dynamic system for the controller design. The external disturbance and parametric uncertainties further increase the difficulty in system identification and state space formulation. To implement a real time control system for various scenarios where the locations and states of the obstacles are not known beforehand, a supervisory algorithm is designed to convert the control problem to a discrete event system. The model predictive control (MPC) using limited lookahead policy is employed in the proposed algorithm.