Simulation model optimization for bonnet polishing considering consistent contact area response

Simulation model optimization plays a crucial role in the accurate prediction of material removal function in bonnet polishing processes,but model complexity often poses challenges to the practical implementation and efficiency of these processes.This paper presents an innovative method for optimizing simulation model parameters,focusing on achieving consistent contact area and the accurate prediction of the material removal function while preventing increase in model complexity.First,controllable and uncontrollable factors in bonnet simulations are analyzed,and then a simplified contact model is developed and applied under constant force conditions.To characterize the bonnet's contact performance,a contact area response curve is introduced,which can be obtained through a series of single spot contact experiments.Furthermore,a rubber hyperelastic parameter optimization model based on a neural network is proposed to achieve optimal matching of the contact area between simulation and experiment.The average deviation of the contact area under different conditions was reduced from 22.78%before optimization to 3.43%after optimization,preliminarily proving the effectiveness of the proposed simulation optimization model.Additionally,orthogonal experiments are further conducted to validate the proposed approach.The comparison between the experimental and predicted material removal functions reveals a high consistency,validating the accuracy and effectiveness of the proposed optimization method based on consistent contact response.This research provides valuable insights into enhancing the reliability and effectiveness of bonnet polishing simulations with a simple and practical approach while mitigating the complexity of the model.

Sulfonic acid-based metal organic framework functionalized magnetic nanocomposite combined with gas chromatography-electron capture detector for extraction and determination of organochlorine

The metal organic framework functionalized with sulfonic acid was combined with magnetic nanoparticles to fabricate a new nanocomposite(denoted as Fe3O4@PDA@Zr-SO3H).By combining with gas chromatography-electron capture detector,the resulting Fe3O4@PDA@Zr-SO3H nanocomposite was successfully used as a high-efficiency adsorbent for pre-concentrating eight organochlorine pesticides from water sample in environment.Apart from the ability of fast separation,the as-prepared Fe3O4@PDA@Zr-SO3H nanocomposite also exhibited high adsorption capacity for organochlorine pesticides.With the use of optimal experimental conditions,the linear relationship can be obtained in the range of 0.05~300μg/L,the correlation coefficient was over 0.9978,and the relative standard deviation was located in 2.5%-7.7%.Moreover,the limit of detection and quantification was between0.005-0.016μg/L and 0.017~0.050μg/L.Finally,the nanocomposite was used for the determination of organochlorine pesticides from environmental water samples,and displayed the recovery of 82%-118%.