ABSTRACT
In metal additive manufacturing (AM), precise temperature field prediction is crucial for process monitoring, automation, control, and optimization. Traditional methods, primarily offline and data-driven, struggle with adapting to real-time changes and new process scenarios, which limits their applicability for effective AM process control. To address these challenges, this paper introduces the first physics-informed (PI) online learning framework specifically designed for temperature prediction in metal AM. Utilizing a physics-informed neural network (PINN), this framework integrates a neural network architecture with physics-informed inputs and loss functions. Pretrained on a known process to establish a baseline, the PINN transitions to an online learning phase, dynamically updating its weights in response to new, unseen data. This adaptation allows the model to continuously refine its predictions in real-time. By integrating physics-informed components, the PINN leverages prior knowledge about the manufacturing processes, enabling rapid adjustments to process parameters, geometries, deposition patterns, and materials. Empirical results confirm the robust performance of this PI online learning framework in accurately predicting temperature fields for unseen processes across various conditions. It notably surpasses traditional data-driven models, especially in critical areas like the Heat Affected Zone (HAZ) and melt pool. The PINN's use of physical laws and prior knowledge not only provides a significant advantage over conventional models but also ensures more accurate predictions under diverse conditions. Furthermore, our analysis of key hyperparameters-the learning rate and batch size of the online learning phase-highlights their roles in optimizing the learning process and enhancing the framework's overall effectiveness. This approach demonstrates significant potential to improve the online control and optimization of metal AM processes.
ABSTRACT
This paper proposes a Takagi-Sugeno (TS) fuzzy sliding mode observer (SMO) for simultaneous actuator and sensor fault reconstruction in a class of nonlinear systems subjected to unknown disturbances. First, the nonlinear system is represented by a TS fuzzy model with immeasurable premise variables. By filtering the output of the TS fuzzy model, an augmented system whose actuator fault is a combination of the original actuator and sensor faults is constructed. An H∞ performance criteria is considered to minimize the effect of the disturbance on the state estimations. Then, by using two further transformation matrices, a non-quadratic Lyapunov function (NQLF), and fmincon in MATLAB as a nonlinear optimization tool, the gains of the SMO are designed through the stability analysis of the observer. The main advantages of the proposed approach in comparison to the existing methods are using nonlinear optimization tools instead of linear matrix inequalities (LMIs), utilizing NQLF instead of simple quadratic Lyapunov functions (QLF), choosing SMO as the observer, which is robust to the uncertainties, and assuming that the premise variables are immeasurable. Finally, a practical continuous stirred tank reactor (CSTR) is considered as a nonlinear dynamic, and the numerical simulation results illustrate the superiority of the proposed approach compared to the existing methods.
ABSTRACT
To mitigate the injurious effect of the rotational acceleration of the brain, a modular Impact Diverting Mechanism (IDM) has been developed. The IDM can replace stickers (decals) that normally attach to the exterior of a football helmet. The IDM decals reduce friction and catch points between the covered area with the IDM on the outer shell of the helmet and the impacting surface, thereby decreasing rotational acceleration acting on the player's head. A Riddell Speed helmet's exterior was prepared with the IDM and outfitted to a headform equipped with linear accelerometers and gyroscopes. The helmets were tested at an impact velocity of 5.5 m/s at 15°, 30°, and 45° to the vertical: on the front, side, and back of the helmet. Results of 135 impact tests in the lab show that the IDM decal, when compared to helmets without it, reduced the rotational acceleration, rotational velocity, SI, HIC, and RIC ranging from 22% to 77%, 20% to 74%, 13% to 68%, 7% to 68%, 31% to 94%, respectively. Protection against rotational acceleration from oblique impacts is not prioritized in modern football helmets, as evident by current standard helmet testing protocols. This study demonstrates that the inclusion of the IDM decals in football helmets can help reduce the effects of rotational acceleration of the head during oblique impacts.
Subject(s)
Football , Head Protective Devices , Mechanical Phenomena , Acceleration , Head , Humans , Materials Testing , RotationABSTRACT
PURPOSE: Oblique impact tests can provide important information regarding the level of protection of a helmet. Two factors that influence the results of oblique impact tests on motorcycle helmets are discussed in this work. The first factor is the angle of the anvil on which the helmet impacts. The second one is the friction between the headform and the helmet's interior. METHODS: To study the first factor, 2 anvil angles are provided, one 30° and the other one 15° to the vertical. To analyze the second factor, we consider 2 types of headform surfaces: the original metal surface of the standard headform and the same headform covered uniformly with a layer of silicone rubber that is 1 mm thick. RESULTS: The results show that varying the anvil's angle and surface friction can directly affect the linear and rotational acceleration of the headform. CONCLUSION: Testing helmets for different oblique impact angles can help assess their protection capability. The coefficient of friction between the helmet's interior and the headform plays an important role in the headform's rotational acceleration during an impact. Using a standard surface friction for headform similar or close to that of the human scalp can ensure that the results of the oblique impact tests are more consistent and realistic.
