Continuous Numerical Analysis of Slug Rivet Installation Process Using Parameterized Modeling Method

The slug rivet installation process is complex. A lot of parameters are included during the riveting deformation process. The workload and time cost of a traditional simulation study is very high since a traditional numerical model should be modified manually time by time when riveting parameters change. The data processing after simulation is another complex work. To improve the situation, this paper presents a parameterized modeling method. The modeling process and data processing algorithm can be developed using Python script. The parameterized model can automatically and continuously re-build without any manual intervention according to the riveting parameter auto-update condition. The post-processing analysis can be automatically conducted and saved as well. Then this paper conducts continuous analysis to illustrate the impact of riveting parameters on riveting quality. The parameterized model keeps running 41 times until the riveting parameter is out of range. The parameterized modeling method is a useful method for a simulation study. The study will pave the way for further investigations.

Data-driven parameterized modeling of LTI systems with guaranteed stability

We propose a theoretical framework and an automated algorithm for the construction of a parameterized reduced-order model of a general LTI system from its sampled input-output responses. The model is cast as a parameterized rational function in Generalized Sanathanan-Koerner form, which allows for implicit parameterization of the model poles. Our main result is the guaranteed enforcement of uniform stability of the model in the parameter space through a sufficient condition, which requires the (strict) positive realness of the denominator subsystem. This condition is enforced through adaptive constraints during the model construction loop. Several applications to the field of Electronic Design Automation are presented.