A Compensation Controller Based on a Nonlinear Wavelet Neural Network for Continuous Material Processing Operations

Continuous material processing operations like printing and textiles manufacturing are conducted under highly variable conditions due to changes in the environment and/or in the materials being processed.As such,the processing parameters require robust real-time adjustment appropriate to the conditions of a nonlinear system.This paper addresses this issue by presenting a hybrid feedforward-feedback nonlinear model predictive controller for continuous material processing operations.The adaptive feedback control strategy of the controller augments the standard feedforward control to ensure improved robustness and compensation for environmental disturbances and/or parameter uncertainties.Thus,the controller can reduce the need for manual adjustments.The controller applies nonlinear generalized predictive control to generate an adaptive control signal for attaining robust performance.A wavelet-based neural network model is adopted as the prediction model with high prediction precision and time-frequency localization characteristics.Online training is utilized to predict uncertain system dynamics by tuning the wavelet neural network parameters and the controller parameters adaptively.The performance of the controller algorithm is verified by both simulation,and in a real-time practical application involving a single-input single-output double-zone sliver drafting system used in textiles manufacturing.Both the simulation and practical results demonstrate an excellent control performance in terms of the mean thickness and coefficient of variation of output slivers,which verifies the effectiveness of this approach in improving the long-term uniformity of slivers.

Sequential slip transfer of mixed-character dislocations across Σ3 coherent twin boundary in FCC metals: a concurrent atomistic-continuum study

Sequential slip transfer across grain boundaries(GB)has an important role in size-dependent propagation of plastic deformation in polycrystalline metals.For example,the Hall–Petch effect,which states that a smaller average grain size results in a higher yield stress,can be rationalised in terms of dislocation pile-ups against GBs.In spite of extensive studies in modelling individual phases and grains using atomistic simulations,well-accepted criteria of slip transfer across GBs are still lacking,as well as models of predicting irreversible GB structure evolution.Slip transfer is inherently multiscale since both the atomic structure of the boundary and the long-range fields of the dislocation pile-up come into play.In this work,concurrent atomistic-continuum simulations are performed to study sequential slip transfer of a series of curved dislocations from a given pile-up onΣ3 coherent twin boundary(CTB)in Cu and Al,with dominant leading screw character at the site of interaction.A Frank-Read source is employed to nucleate dislocations continuously.It is found that subject to a shear stress of 1.2 GPa,screw dislocations transfer into the twinned grain in Cu,but glide on the twin boundary plane in Al.Moreover,four dislocation/CTB interaction modes are identified in Al,which are affected by(1)applied shear stress,(2)dislocation line length,and(3)dislocation line curvature.Our results elucidate the discrepancies between atomistic simulations and experimental observations of dislocation-GB reactions and highlight the importance of directly modeling sequential dislocation slip transfer reactions using fully 3D models.