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.
Highlights
Large-size thin shell parts are among the most widely used components in industrial applications such as aircraft skins, vehicle cover panels, rocket outer shells, and ship curved panels
Due to the complex interaction dynamics that involves the multipoint location, the low rigidity of thin shell parts, and the ongoing separation of the desired shape, the cliff effect where the stiffness of the fixturing system obviously steps down is very likely to emerge unexpectedly [7]. This emergent behavior would bring out potentially harmful effects or waste expensive large-size thin shell parts owing to out-tolerance, so it needs to be eliminated through reconfigurable fixturing planning
We introduced the Markov chain Monte Carlo (MCMC) sampling method to generate the new potential samples that depend on a transition probability [47]
Summary
Large-size thin shell parts are among the most widely used components in industrial applications such as aircraft skins, vehicle cover panels, rocket outer shells, and ship curved panels. Locating schema remain almost the same from the beginning to the end, while during the trimming operation, the effective locators may change with the cutting separation of the desired shape from the original part [6] In this case, “N” is no longer a constant but a dynamic time-varying variable, so the author suggested replacing “N” with “X” and called this particular location fashion the “X-2-1” location principle [7]. These studies are not directly applicable to the case in which the effective locators dynamically change throughout the trimming operation.
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