Digital Twin-Driven Reconfigurable Fixturing Optimization for Trimming Operation of Aircraft Skins

Abstract

The incomplete constraint induced by multipoint reconfigurable fixturing and the inherently weak rigidity of thin shell parts significantly hinder the stability of flexible fixturing systems. In particular, during the trimming operation, the number of effective locators may change with the progressive separation of the desired shape from that of the blank part, which easily produces the cliff effect (instantaneous dramatic reduction) of the system stiffness. As a result, the location layout becomes a main crux in reality. Regarding this issue, the author herein presents a digital twin-based decision-making methodology to generate reconfigurable fixturing schemes through integrating virtual and physical information. Considering the intrinsic features of the trimming process, such as the time-varying propagation of the system stiffness and the coupling effects of multiattribute process parameters, the hidden Markov model was introduced to cope with reconfigurable fixturing optimization. To achieve fast convergence and seek a feasible solution, local information (where low system rigidity occurs) was extracted and shared to guide the optimization process in a front-running simulation. To demonstrate the presented method, trimming experiments were performed on a large-size compliant workpiece held by a reconfigurable fixturing system that was developed independently by our research group. The experimental results indicate that the proposed method could adaptively iterate out the optimal locating schema and process control reference from the virtual fixturing and trimming simulation to guarantee the time-varying stability of the trimming process in the real world. Clearly, the digital twin-based reconfigurable fixturing planning approach generated a high possibility of building a context-specific, closed-loop decision-making paradigm and allowing the reconfigurable fixturing system to behave in a more adaptable and flexible manner.

Digital Twin Model of Electric Drives Empowered by EKF

“…Many researchers have been interested in various platforms, but the Metaverse, with its ability to allow users to work in a virtual world through avatars, has shown the importance of conducting research [ 5 , 6 ]. In a study [ 7 ], the instability in flexible fixturing systems caused by incomplete constraints and weak rigidity has been addressed. This way, a digital twin-based approach to generate reconfigurable fixturing schemes, using a hidden Markov model to account for time-varying system stiffness and coupling effects, was proposed.…”

Optimization of fixture locating layout design using comprehensive optimized machine learning

“…Alshameri et al [26] used SAA; same as Du et al [23] for calculation of the set of points for designing the robust fixture. Hu [27] used hidden Markov model for optimization of digital twin-driven reconfigurable fixturing in aviation industry. Yu and Wang [28] used machine-learning method to extract the model that can imitate the same process using RSM and mathematical optimization.…”

“…Based on reviewed articles, the previous works can be divided into two groups. The first group of the work is established based on the combination of optimization method (evolutionary, global, mathematic) with FEM [10,11,13,16,[19][20][21][22][23][24][25][26][27]29]. These studies are accurate in calculation of the workpiece deformation during the machining operation.…”

Fast electrothermal coupling calculation method for supporting digital twin construction of electrical equipment

“…The second is to build a DT model through the multiphysics model and use fast numerical methods to update the internal state of DT [7,13,14]. The multi-physics calculation method employed in DT mostly adopts the conventional finite element method (CFEM) [13,15,16]. For a complex electrical equipment, a large number of grids are often required to ensure the accuracy of the calculation.…”