|dc.date.accessioned||2022-10-25 16:12:21 (GMT)||
|dc.date.available||2022-10-25 16:12:21 (GMT)||
|dc.description.abstract||This thesis investigates the coupled relationship between the strip transfer and forming operations in
progressive die sheet metal forming, including the effects of the strip layout geometry, and its effect on
the process speed and accuracy. Servo-actuated strip lifters and feeder are considered to assist in
minimizing the dynamic response of the strip during the transfer process. A methodology is proposed
for identifying suitable trajectories to prescribe the motion of active strip lifters and feeder to obtain
consistent part quality without risk of process failures for a progressive die operation.
Multiple iterations of a finite element (FE) model were constructed in LS-DYNA to simulate a
progressive die operation. Various FE analysis techniques were used to reduce the computational cost
of the simulations to allow for enough data to be generated for machine learning applications. Both
explicit and implicit time-integration schemes were considered in iterations of the FE model.
Both single and dual carrier strip layouts were considered. The results of the FE simulations suggest
that the single carrier strip layouts produce larger predicted dynamic displacements and rotations of the
work-piece as compared to the dual carrier strip layouts during strip transfer. Furthermore, the single
carrier strip layout is shown to be susceptible to strip misalignment.
The final version of the FE model utilized geometry based on a demonstrator tool being deployed at
the Technische Universität München. A total of 1000 simulations were generated, 500 each for the ‘I’
and ‘O’ stretch-web types using a single carrier strip layout. Each simulation considered a unique
permutation of control inputs sampled from the set of possible strokes rates and trajectories for the
lifters and feeder. Cubic splines were used to generate the trajectories for the strip lifter and feeder by
varying the position of two knots used to define the shape of the spline.
The results from the 1000 simulations indicate that in general the ‘S’ stretch-web produces a larger
variance in the predicted dynamic response and ‘work-piece placement as compared to the ‘I’ stretchweb. Furthermore, the stroke rate and lifter trajectory were shown to have a large influence on the
overshooting of the work-pieces during strip transfer and the probability of whether tooling collisions
Multiple machine learning models were trained on the data generated by the final FE model. Two
types of classifiers were constructed using neural network and XGBoost architectures. The first
classifier predicts whether the clearance between the strip and binder are within a specified tolerance (to prevent collision with the tooling) during strip transfer. The second classifier predicts whether the
placement accuracy of the work-piece on the forming die after strip transfer is within a specified
tolerance. A range of tolerances were considered when labeling the data for both classifiers. Nestedcross fold validation was used to select the hyperparameter tuning and model selection.
The machine learning classifiers were used to test all possible control inputs using a ‘minimum feed
clearance’ of 10 mm and a maximum ‘work-piece placement error of 0.11 mm. The maximum stroke
rate at which a given pair of lifter and feeder trajectories can operate was identified for all permutations.
Five permutations that achieved the highest predicted stroke rate were simulated for an additional five
strokes. The classifiers showed a reasonable ability to predict the ‘minimum feed clearance’ and ‘workpiece placement in the extended FE simulations for the selected trajectories, but, was unable to account
for the strip misalignment which occurred after several strokes in all simulations.
This research successfully demonstrates a methodology for using machine learning models trained
on FE simulations to predict process outcomes of a progressive die operation with variable feeder and
lifter trajectories. The FE simulations used to train the machine learning models were generated by
adopting computationally-effective FE modelling techniques in a single press stroke model. The
machine learning models were shown to reasonably predict the process outcomes of novel input
permutations in a multi-stroke FE simulation. One of the largest constraints in this research is the FE
simulation time which limited the model complexity that could be considered in the training set
generation. Furthermore, the demonstration of the machine learning predictions for a multi-stroke
process was limited due to the susceptibility of the single carrier strip layout to misalign after strip
progression. Future work should consider the use of dual carrier strip layouts for the generation of the
training data. Alternative approaches may also be considered, such as a machine learning framework
for directly predicting the forward dynamics of the progressive die operation or a co-simulation
approach in which a robust controller interacts directly with the FE simulation.||en
|dc.publisher||University of Waterloo||en
|dc.subject||Sheet Metal Forming||en
|dc.subject||Finite Element Analysis||en
|dc.title||A Methodology for Data-Informed Process Control in Progressive Die Sheet Metal Forming||en
|uws-etd.degree.department||Mechanical and Mechatronics Engineering||en
|uws-etd.degree.grantor||University of Waterloo||en
|uws-etd.degree||Master of Applied Science||en
|uws.contributor.affiliation1||Faculty of Engineering||en