Resumen
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.